# Vstorm URL: https://vstorm.co ## VSA 1-pager URL: https://vstorm.co/1-pager-vsa A one-page overview of how Vstorm delivers agentic AI — the approach, the value, and what working with us looks like. Free download. [Home](/)/[Ebooks](/ebook/)/VSA 1-pager Free ebook # VSA 1-pager Familiarize yourself with Vstorm's approach that secures business results from custom AI agent deployment — from purchase decisions through implementation. ## What's inside * Clarity across the full AI deployment journey * How to reduce risk before you commit resources * A cleaner path from procurement to go-live * Transparency and predictability you can plan against ## Get free access Two quick steps, then the download starts. A copy also goes to your inbox. One page. Free. No sales call attached. Step 1 of 2Your details 1. 1 You 2. 2 Access Full nameWork email Use your work email — we will also send a copy of the PDF there. Company _(optional)_Role _(optional)_ How did you hear about us? GooglePerplexityChatGPTGeminiClaudeGrokAdsClutchUpworkLinkedInFrom a friendOpen sourceOther Select at least one. I agree that Vstorm may store and process my data to deliver this ebook, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Send me occasional agentic AI updates. Unsubscribe anytime. Back Continue Get the ebook Your PDF is ready [Download PDF](/ebooks/VSA-1-pager.pdf) We have also emailed you a copy. If it does not arrive in a few minutes, check spam or write to info@vstorm.co. What you get ## What value do you get from the VSA? Four outcomes the one-pager is built to leave you with — before you scope a build. Clarity in AI implementation You will understand the full AI deployment journey, from purchase decisions to implementation, with a structured and informed approach. Risk reduction and smarter decisions You will learn how to identify and mitigate uncertainties — technical feasibility and real business value — before you commit resources. Seamless procurement and deployment You will be able to streamline the path from purchase to go-live, cut inefficiencies, and allocate resources where they matter. Greater transparency and predictability You will gain a way to plan, track, and measure AI project success so execution stays aligned with business goals. Work with us ## Ready to put agentic AI to work? Book a free 30-minute consultation. We will map one real process worth automating. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Venture Tech Builder URL: https://vstorm.co/a-tech-driven-venture-builder Co-build digital companies with Vstorm — generative AI, MVP delivery, network access, and fundraising support from Proof of Value through scale. [Home](/)/[Services](/services/)/Venture Tech Builder # Venture Tech Builder A tech-driven venture builder for startup founders. We invest expertise, generative AI engineering, and infrastructure so founders can co-create digital companies — from Proof of Value to organisation and scale. [Book a discovery call](/schedule-a-meeting/) [See how we work](/tristorm/) Founder idea Proof of Value MVP Proof of Organisation Scaling-up Advisory Engineering Network Capital access 01 Prove 02 Organise 03 Scale How we can help you ## Technical expertise to launch or scale a start-up Generative AI engineering included — from first idea through delivery. This is the Vstorm path to make the idea real. Strategic and operations advisory Turn the vision into actionable strategic planning and a product roadmap, and plug operating modules into the startup so building moves faster. Leverage technology Build a Proof of Value, MVP, or tech solution on our infrastructure — tight cost and time-to-market without cutting quality. Network Test the idea against real buyers and reach clients, partners, and senior collaborators through the Vstorm network — instead of starting cold. Capital access We are not a VC fund, but we open doors to investors, angels, and partners, and support fundraising from prep and legal work through day-to-day investor engagement. Proven workflow ## Proven workflow of collaboration From Proof of Value through Proof of Business to Proof of Organisation and Scaling-up — so the next milestone rests on evidence. * 01 How we help * 02 Proof of Value * 03 Collaboration 01 / 03 ### How we can help you Technical expertise to launch or scale a start-up — generative AI engineering included — from first idea through delivery. This is the Vstorm path to make the idea real. * Strategic and operations advisory — vision into a clear plan, product roadmap, and plug-and-play operating modules. * Leverage technology — Proof of Value, MVP, or a full tech build on our infrastructure with tight cost and time-to-market. * Network — clients, partners, and senior collaborators to test the idea and accelerate growth. * Capital access — we are not a VC fund, but we open doors to investors, angels, and partners, and support fundraising from prep through investor work. 02 / 03 ### Proof of Value Prove technical feasibility without ignoring legal, business, conceptual, and team constraints — evidence you can use to decide whether to scale. * Testing the market — release early, read customer feedback, and take criticism seriously. * Adaptation — iterate product and business model until market fit holds. * Product-market fit — optimize until further investment has a clear path to ROI. * Scaling — once the model is market-proven, invest to grow. 03 / 03 ### Proven collaboration workflow Work through Proof of Value, Proof of Business, and Proof of Organisation and Scaling-up — so the next milestone (including a financing round) rests on evidence, not slides. * Proof of Value — technical, legal, business, conceptual, and team feasibility. * Proof of Business — market testing, adaptation, and product-market fit. * Proof of Organisation and Scaling-up — a market-proven model ready to grow. Our customers are featured on * ![Forbes](/logos/featured-in/forbes.svg) * ![The New York Times](/logos/featured-in/nyt.svg) * ![Business Insider](/logos/featured-in/business-insider.svg) * ![TNW](/logos/featured-in/tnw.svg) * ![Wired](/logos/featured-in/wired.svg) * ![TechCrunch](/logos/featured-in/techcrunch.svg) How we work ## Principles behind every engagement Founder-first delivery — clear goals, short cycles, and a bias to ship. Entrepreneur-first Help founders grow in the shortest useful time — we have walked these paths before. Mission driven Build extraordinary startups with a worldwide name — the work has to matter. Goal oriented Meaningful goals, measured and orchestrated — results over activity. Move fast Minimal process that shortens cycles and speeds decisions so results land sooner. Ventures ## Start-up success stories ![Collaborative conversational AI assistant for a web annotation startup](/app/uploads/2026/09/case-card-collaborative-assistant-4x3.png) ### Collaborative Conversational AI assistant Open-source AI platform enabling real-time multi-user collaboration with LLMs, built with LangChain for a California-based web annotation startup. [Learn more ↗](/case-study/collaborative-conversational-ai-assistant-with-automation/) ![Mapline.AI case study — real estate due diligence AI agent](/app/uploads/2026/09/case-card-mapline-due-diligence-4x3.png) ### Mapline.AI Vstorm built a custom AI Agent for Mapline.AI that reduces real estate due diligence from weeks to minutes using advanced RAG, specialized OCR, and Milvus vector search. [Learn more ↗](/case-study/advanced-rag-engineering-for-real-estate-due-diligence-ai-agent/) We work with * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![Yeeld](/logos/clients/yeeld.svg) Yeeld , Fintech, Financial coaching 6 agents orchestrated by one supervisor Multi-agent financial coach: pattern extraction, gamified habit-building and two coaching personas with opposite attitudes. * ![Mapline.AI](/logos/clients/mapline.png) Mapline.AI , Proptech, Real Estate Weeks → minutes due-diligence time before and after Single agent with specialised OCR, metadata filtering and re-ranking for remote site evaluation. * ![HiredHelpr](/logos/clients/hiredhelpr-dark.svg) HiredHelpr , Home Services Agentic AI for operational workflow automation. * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Engagement ## Other models of engagement ![Vstorm engineers working inside a client team](/app/uploads/2023/01/Untitled-1920-x-1280px-e1686736493308.webp) ### Forward Deployed Engineering Remote extended teams with pre-vetted profiles — embed AI and software engineers in your squad when co-build is not the fit. [About Forward Deployed Engineering ↗](/team-extension/) ![Vstorm delivery team — project-based engagement](/app/uploads/2023/01/Untitled-1920-x-1280px-e1686736493308.webp) ### Project-based development Full-cycle projects with QA and project management — scoped delivery when you need an outcome, not a seat. [About project-based development ↗](/ai-agent-development-company/) Talk to a real person ## Free consultation for your start-up ![Antoni Kozelski — CEO and Co-founder at Vstorm](/app/uploads/2023/05/Templqatka-insta-tweeter-Antoni-Instagram-Post-Portrait-21.webp) CEO and Co-founder ### Antoni Kozelski Startup-minded serial tech entrepreneur, business angel, and strategic advisor. Co-founder and CEO at Vstorm. Working globally, with a focus on western Europe, CEE, and the USA. Key areas: strategy, team development, finance, project management, business development, and marketing. [LinkedIn ↗](https://www.linkedin.com/in/antoni-kozelski/) Why choose us ## Why co-build with Vstorm? 01 ### Experience as a venture tech builder 30+ production deployments since 2017 Agentic AI delivery since 2017: 25+ AI engineers inside a company of 40+ people, investing expertise, engineering, and infrastructure into ventures we co-create. 02 ### Specialized tech stack purpose-built tooling, production-grade Co-investment of expertise, generative AI engineering, and infrastructure — from Proof of Value and MVP to Proof of Organisation and Scaling-up. 03 ### End-to-end support consult → proof of value → deploy → maintain Full support from first consultation and Proof of Value through deployment and maintenance, so the venture keeps running after the build phase ends. ## Ready to co-build your next venture? Book a free 30-minute consultation. We will map Proof of Value, MVP scope, and whether co-building with Vstorm is the right fit. [Let's work together](/contact-us/)[Schedule a meeting](/schedule-a-meeting/) Insights ## Learn more on startups and tech Adoption, automation, and delivery notes from the team. [View all articles](/ai-blog-news/) [![](/app/uploads/2026/04/2024-01-20__Y4A0426-scaled.webp) Agentic AI ### What makes a decision-maker ready for AI adoption? Years past the ChatGPT moment, hindsight is available. What separated the decision-makers who extracted value from LLMs from those who did not. ](/agentic-ai/what-makes-a-decision-maker-ready-for-ai-adoption/)[![](/app/uploads/2026/02/pexels-gdtography-277628-911738-1-6-768x513.png) Agentic AI ### What do we mean by AI automation, actually? One of misconceptions we observe at Vstorm is that client expectas the 'AI Agent' to be "just to be put in," ](/agentic-ai/what-do-we-mean-by-ai-automation-actually/)[![](/app/uploads/2026/02/Discussion-at-Davos-scaled.webp) Strategy ### A Commentary on Deloitte's "State of AI in the Enterprise" On January 21, 2026, Deloitte publicly unveiled their State of AI in the Enterprise report during the World Economic Forum. ](/ai/a-commentary-on-deloittes-state-of-ai-in-the-enterprise/) --- ## About Vstorm URL: https://vstorm.co/about-us Agentic AI engineering company from Wrocław, building AI since 2017. 40+ people, 25+ AI engineers, 30+ agents running in production. [Home](/)/About Us # About Vstorm Agentic AI for companies that grow by getting stronger. We call them Small Giants: companies that compete on focus rather than headcount. Vstorm builds the agentic AI layer that lets a focused team operate like one many times its size — production-grade, delivered through the TriStorm methodology, and owned by you. [Reach out to our experts](/schedule-a-meeting/) [See how we work](/tristorm/) ![The Vstorm team in the Wrocław office, with remote teammates joining on the screen behind them](/_astro/team-2026.VCI7buUE.jpg) One team — in the room and on the call * 30+ agents in production * 40+ people on board * 2017 building AI since Mission ## Busy, but not agile. Most companies we work with are busy but not agile. They hold knowledge they cannot access, and good people are locked into repetitive work instead of the hard thinking. Agentic AI changes that. Most companies get it wrong, bolting on isolated tools that never compound into anything. What Small Giants need is AgenticOS: a single operating layer where every agent shares context and works as part of how the whole business runs. Not a collection of tools. The way the company runs. TriStorm is how we build it. Strategize first, build the highest-impact piece in production-grade code, then embed with your team until it changes how people work. Three phases, no shortcuts, owned by you. The destination 01 ### Small Giants A company that chose to be great instead of just big. It grows by making its people capable of more, not by adding more people every time a problem appears. With the right technology, a focused team outperforms organizations five or ten times its size — without losing the speed or the identity that made it worth building. The vehicle 02 ### AgenticOS One layer where every agent shares context and builds on what the others know. Buying agents one at a time gives you a chatbot here and an automation there, each solving a small problem, none of them compounding. AgenticOS is the operating layer instead: decisions get faster and knowledge stops living in people's heads. The road 03 ### TriStorm The methodology that turns the vision into production code: strategic alignment and planning, then Proof of Value, then process augmentation. Understand how the company actually works, build the highest-impact piece first, embedded with the team — then make sure it changes how people work. Software nobody adopts is expensive shelfware. Our journey ## Hope is not a strategy. Since 2017 we have built toward one goal: production AI that companies own. The route ran through natural language processing, data engineering, and machine learning — long before LLMs had a name. 1. Founded ### The pre-LLM years Vstorm starts with natural language processing, data engineering, and machine learning — building working systems for companies, not research demos. 2017. ![Early Vstorm engineering work](/_astro/llm-beginning.DwHdCxRP.jpg) where it started 2. LLM engineering ### Large language models become the core practice Production LLM systems for clients — and contributions to the LangChain ecosystem from its beta versions. 2022. 3. Agentic AI ### Same craft, clearer language What we had been shipping as production LLM systems with tools and orchestration, the market started calling agentic. We become an official Pydantic implementation partner. 2024. ![The Vstorm team working together around the table in the office](/_astro/team-table-2026.gzkNa-a0.jpg) the agentic practice 4. Ecosystem ### Building where the tools are built Official Pydantic implementation partner and the first agentic AI engineering company in the Agentic AI Foundation. We contribute to Pydantic AI, LangChain, and LlamaIndex — 31 public repositories, 3,900+ GitHub stars as of September 2026. [See the open source work](/agentic-ai-open-source-initiatives/) 2026. 5. Production at scale ### 30+ agents in production 25+ AI engineers, 12 industries — and the first agentic AI engineering company in the Agentic AI Foundation. [See the work](/case-studies/) Now. Positioning ## Leaders in our field. ![Vstorm engineers at an industry conference](/app/uploads/2025/02/2024-01-20__Y4A0635_small-e1738740573627.jpg) ### We have been with LLMs from the very beginning We created Vstorm in 2017, at the very start of Large Language Models research. Show more The year 2017 marked the beginning of intensive research on scaling language models (LLMs). The introduction of larger models with billions of parameters opened new perspectives on text generation and comprehension, laying the foundation for future developments in artificial intelligence and Agentic AI. ![A Vstorm workshop — the team gathered around U-shaped tables during an internal presentation](/_astro/team-workshop-2026.HHC05q7f.jpg) ### Why did we decide to become specialists in LLMs and AI? We had the technical expertise and industry insight to redesign processes and integrate agents across high-value workflows. Show more We decided to become specialists in Agentic AI and LLMs because we recognized their immense transformative potential for businesses. The ability of AI agents to autonomously execute complex workflows — with evaluation and guardrails keeping hallucination risk under control — allowed implementation to enhance how businesses truly operate and deliver measurable value. By focusing on these technologies, we positioned ourselves to help businesses harness their power for advanced automation, personalization, and decision-making as an end-to-end partner. Core values ## What we hold ourselves to. 01 ### Value-driven We automate business-critical pain points — measured in market performance, not demo counts. 02 ### Human-centric Agents free people for higher-value work. Adoption is designed with operators, not against them. 03 ### Commitment to excellence PhD-grade engineering, evaluated before it ships — in code, in delivery, in writing. 04 ### Joy We love what we do — it shows in the work and in the room. Vstorm Explained ## A series on who we are and how we work. Straight answers to the questions we hear most — how we deliver, why we bet on agentic AI over automation shortcuts, and how we keep agents honest in production. [ Part 1 Trust & Positioning ### Why TriStorm beats staff augmentation Staff-augmentation shops put strategy in one room and the build in another. TriStorm keeps both with the same team — usually the only setup where agentic AI actually reaches production. Read Part 1 01 Strategize 02 Build 03 Transform ](/vstorm-explained/vstorm-is-not-a-staff-augmentation-shop/) [ Part 2 Agentic AI ### Why we bet on agentic AI over RPA RPA executes a rule. Agentic AI reasons about the exception. The mechanism-level difference, with two production deployments. RPA AGENT Read Part 2](/vstorm-explained/why-agentic-ai-not-rpa/)[ Part 3 Agentic AI Engineering ### Evaluation before production Schema-valid output is still wrong until you measure it. How Vstorm evaluates agents before they ship — with the production numbers behind it. EVAL GATE PROD Read Part 3](/vstorm-explained/evaluation-before-production/) ![Antoni Kozelski, CEO and Co-founder of Vstorm](/_astro/antoni.B7CkQyzF.jpg) Our mission > “We are not here to make companies dependent on us. We are here to make them sovereign — able to run, change and own the systems that decide how their business works.” > > **Antoni Kozelski**CEO & Co-founder Open source ## We build in the open. With over 30+ real-world implementations. The first Agentic AI Consultancy joined the ![Agentic AI Foundation](/app/uploads/2026/02/Logo.png) to contribute and co-shape the industry trends. Our libraries on ![](/app/uploads/2026/02/Frame1.svg) used by **50k+** developers [Visit our GitHub](https://github.com/vstorm-co) ![NVIDIA](/app/uploads/2026/02/NVIDIA_logo-1.svg) ![Reddit](/app/uploads/2026/02/Frame2.svg) ![Amazon Web Services](/app/uploads/2026/02/Amazon_Web_Services_Logo-2.svg) ![Oracle](/app/uploads/2026/02/Frame3.svg) ![OpenAI](/app/uploads/2026/02/OpenAI_Logo-2.svg) ![Google](/app/uploads/2026/02/logos_google.svg) ![Nokia](/app/uploads/2026/02/Nokia_2023-2.svg) ![Pfizer](/app/uploads/2026/02/Pfizer_2021-2.svg) ![TikTok](/app/uploads/2026/02/logos_tiktok.svg) We've been partnering and contributing to the main Agentic AI technologies ![Pydantic](/app/uploads/2026/02/Pydantic.svg) ![LangChain](/app/uploads/2026/02/Lang.svg) ![Vstorm engineers speaking at industry conferences](/app/uploads/2026/02/Photos.png) Our PhD-grade AI engineers are speaking in industry-leading conferences all over the world. Our team ## Meet our team. ![Antoni Kozelski](/_astro/antoni.B7CkQyzF.jpg) Serial tech entrepreneur, business angel, and strategic advisor. Has led Vstorm strategically and operationally since 2017 — 200%+ year-on-year growth, with delivery across Europe, the US, Canada, and Saudi Arabia. [Articles](/author/antoni-kozelski/) ### Antoni Kozelski CEO & Co-founder ![PhD. Bartosz Gonczarek](/_astro/bartosz.BStnM453.png) Co-founded Explain Everything (acquired by Promethean) and supported the growth of Alphamoon (acquired by Box.com). PhD in economics, EY Entrepreneur of the Year 2018 finalist — joining vision with technology. [Articles](/author/bartosz-adam-gonczarek/) ### PhD. Bartosz Gonczarek Vice President & Co-founder ![PhD(c) Wojciech Achtelik](/_astro/wojciech.D1BDf4Qr.jpg) Leads production agentic AI engineering — from architecture through deployment in regulated, high-stakes environments. [Articles](/author/wojciech-achtelik/) ### PhD(c) Wojciech Achtelik AI Engineer Lead ![Anna Vinnyk](/_astro/anna.DmslxTlJ.jpg) Builds the community of engineers and operators behind Vstorm's delivery — talent-first, remote-first. ### Anna Vinnyk HR & Recruitment [ ![Vstorm team at the Deloitte Technology Fast 50 awards](/_astro/deloitte-team._bJCAnfo.jpg) Behind the badge ### Deloitte Technology Fast 50 — the story How a team shipping production agents across Europe earned a place among the region’s fastest-growing tech companies. Read the story ](/community/vstorm-leader-in-llms-solutions-recognized-by-deloitte-technology-fast-50/) Where we stand ## Our approach to conduct. Integrity and trust guide how we do business — ethically, with mutual respect, and within the laws and regulations that apply to us. talent mesh A B C D ONE TEAM MERIT-FIRST 01 ### Diversity and inclusion It is all about bringing people together around a similar mindset, the topics they care about, without losing sight of their individuality. To ensure that our community is fair and 100 percent talent-based, we focus on diversity and inclusion. The fourth industrial revolution has made us aware that happy employees add value to the business, making it more ethical and competitive. outward contribution VSTORM gives local volunteer pro bono CONTRIBUTING 02 ### Community social responsibility It means making a positive contribution to our communities. The members of our community commit themselves voluntarily, as long as it is in their hearts. We operate as an ethical, sustainable, and human-oriented brand in a digitalized world. waste → down paperless audit recycle waste index −42% low waste 03 ### Environmentally-sound As our planet is shaking, we know it is time for us to act and go green. We are a paperless community, with no waste policies: * We minimize waste by evaluating operations and ensuring they are as efficient as possible. * We promote rechargeable batteries wherever possible. * Actively promote recycling both internally and with our clients. * Utilize any opportunity to raise awareness of opportunities to minimize our environmental footprint. **We are hiring.** Engineers and operators building production agents. [See open roles](/career/) ## Get to know what we write about [View all articles](/ai-blog-news/) [![](/app/uploads/2023/12/dominik-jirovsky-re2LZOB2XvY-unsplash-scaled.webp) Strategy ### Instant customer service. AI chatbots in e-commerce AI chatbots in e-commerce, in particular, have become integral in enhancing customer service, providing instant, personalized responses to customer inquiries, and significantly improving user experience. ](/ai/instant-customer-service-ai-chatbots-in-e-commerce/)[![](/app/uploads/2026/07/Group-1777-5.png) Engineering ### Pydantic AI v2 and the road to production-grade agentic AI Pydantic AI v2 bundles instructions, tools, hooks and model settings into composable capabilities, with Vstorm packages linked from the Harness. ](/pydanticai/pydantic-ai-v2-and-the-road-to-production-grade-agentic-ai/)[![](/app/uploads/2026/06/Group-1777-2-2.png) Agentic AI ### From RPA to agentic AI: rebuilding the revenue cycle Denials hit 11.81% in 2024 despite widespread RPA. Why an automated revenue cycle still leaks revenue, and what agentic AI changes. ](/agentic-ai/from-rpa-to-agentic-ai-rebuilding-the-revenue-cycle/) --- ## Vstorm Academy URL: https://vstorm.co/academy 251 articles on agentic AI, a 558-entry glossary, delivery guides, client videos and 31 open-source repositories — most of it needs no email. [Home](/)/Vstorm Academy # Vstorm Academy Everything we learned shipping agents to production, written down. The blog, the glossary, the guides we hand clients during delivery, and the agent libraries we maintain in public. Most of it needs no email address. [Read the blog](/ai-blog-news/) [Browse the glossary](/glossary/) BlogGlossaryGuidesOpen source Built in-house 1. 01Read 2. 02Apply 3. 03Build The library ## Six collections Articles, definitions, guides, two client films, one assessment, and the repositories we maintain in public. [Not sure where to start? Talk it through with an engineer](/schedule-a-meeting/) All Read 3 Watch 2 Try 1 Build 1 Sort Featured A–Z Largest first Showing 7 of 7 * Read 251 articles ### [Blog](/ai-blog-news/) Practical takes on agentic AI and delivery * [Human in the loop for AI agents: when the agent needs permiss…](/agentic-ai/human-in-the-loop-ai-agents-permission-to-change-the-plan/) * [Process mapping for agentic AI: a step-by-step guide](/agentic-ai/process-mapping-for-agentic-ai/) * [Agentic AI governance in regulated industries: what actually …](/agentic-ai/agentic-ai-governance-in-regulated-industries/) * Build 31 repositories ### [Open source](https://oss.vstorm.co/) Agent tooling our team builds and maintains in the open * [full-stack-ai-agent-template](https://github.com/vstorm-co/full-stack-ai-agent-template) * [pydantic-deepagents](https://github.com/vstorm-co/pydantic-deepagents) * [pydantic-ai-backend](https://github.com/vstorm-co/pydantic-ai-backend) * Read 3 downloads ### [Ebooks & guides](/ebook/) Long-form guides you can take away and read * [RAG & Information Retrieval Guide for 2026](/rag-and-information-retrieval-guide/) * [The LLM Book](/the-llm-book/) * [VSA 1-pager](/1-pager-vsa/) * Watch 2 films ### [Videos](/academy/#videos) Client stories told by the people who ran the project * [Building text-to-workflow Agentic AI with Synera](/academy/#videos) * [Multi-agent AI-support for Mixam](/academy/#videos) * Watch 5 events ### [Events](/events/) Talks, panels and webinars, with recordings * [Vstorm webinar: after AGNTCon + MCPCon 2026](/events/agntcon-mcpcon-follow-up-webinar-2026/) * [AGNTCon + MCPCon North America 2026](/events/agntcon-mcpcon-north-america-2026/) * [Agent horror stories: Halloween drinks with Vstorm](/events/agntcon-mcpcon-drinks-san-jose-2026/) * Try 1 tool ### [Tools](/academy/#tools) Score your own readiness before you scope a build * [AI readiness assessment](/ai-readiness-assessment/) * [Open-source agent tooling](https://github.com/vstorm-co) * Read 558 entries ### [AI Glossary](/glossary/) Clear definitions for the agentic AI vocabulary * [About OpenAI](/glossary/about-openai/) * [ACE AI Agent](/glossary/ace-ai-agent/) * [Ada AI Agent](/glossary/ada-ai-agent/) No resources in this filter. Choose All or another format. Watch ## Two clients, on camera Both were recorded with the client after the system was running in production. Neither is a demo reel or a scripted testimonial. * ![](https://img.youtube.com/vi/sCzXMSIDwcM/maxresdefault.jpg) ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) Synera ### Building text-to-workflow Agentic AI with Synera Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. 2 hrs → 3 min to generate a validated workflow [Read the case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![](https://img.youtube.com/vi/f4VI44e7s-I/maxresdefault.jpg) ![Mixam](/_astro/mixam-logo.B59_GcxD.png) Mixam ### Multi-agent AI-support facilitating highly customized order completion for Mixam A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. 95.4% Success rate in workflow results [Read the case study](/case-study/ai-agent-for-order-recommendation-and-completion/) [More on YouTube](https://www.youtube.com/@vstorm-ai-consultancy) Tools ## Two things you can run yourself Start with a guided readiness profile, or jump straight into the agent libraries we ship in public. AI Readiness assessment.vstorm.co vstorm AI readiness assessment Is your organization ready for AI? Start the Assessment * **~10–15 minutes** No technical knowledge required * **Seven dimensions** Strategy, process, data, systems, team, AI experience, governance * **Report in your inbox** Personalised, with next steps Interactive ### AI readiness assessment Profile business, data, technology and governance readiness in one guided pass — then leave with a structured score. * 10–15 min * Free * No scoping call [Open assessment](/ai-readiness-assessment/) vstorm-co github.com/vstorm-co Pinned * full-stack-ai-agent-template Public Full-stack AI app generator — FastAPI + Next.js with AI agents, RAG, streaming, and a production-minded starter layout. Python 1,919 * pydantic-deepagents Public Open-source, self-hosted Claude Code — a terminal AI assistant and the Python framework behind it, built on Pydantic AI. Python 1,068 * pydantic-ai-backend Public File storage and sandbox backends: console file tools, Docker-isolated execution, and permission presets for access control. Python 129 Open source ### Agent tooling on GitHub 31 public repositories and 3,900+ GitHub stars, built on Pydantic AI — including Pydantic DeepAgents. Counted on github.com/vstorm-co, 28 September 2026. * 31 repos * 3,900+ stars [Browse on GitHub](https://github.com/vstorm-co) From the blog ## Most recent articles The blog is the most active surface here — these three are the newest. [All articles](/ai-blog-news/) [![](/app/uploads/2026/10/cover-human-in-the-loop-ai-agents-permission-to-change-the-plan-v3.png) Agentic AI ### Human in the loop for AI agents: when the agent needs permission to change the plan Human in the loop for AI agents: who approves a change of plan, what evidence they need, how to stop approval fatigue and keep bookings matched to approvals. ](/agentic-ai/human-in-the-loop-ai-agents-permission-to-change-the-plan/)[![](/app/uploads/2026/10/cover-process-mapping-for-agentic-ai-v3.png) Agentic AI ### Process mapping for agentic AI: a step-by-step guide Nine steps to map one workflow for agentic AI: choose it, map it from real records, measure it, check readiness, redesign it and set a go/no-go KPI. ](/agentic-ai/process-mapping-for-agentic-ai/)[![](/app/uploads/2026/10/cover-agentic-ai-governance-in-regulated-industries-v3.png) Agentic AI ### Agentic AI governance in regulated industries: what actually binds you The EU AI Act delay moved high-risk deadlines to 2027, but agents still answer to Article 50, DORA and their audit trail. Seven controls to build now. ](/agentic-ai/agentic-ai-governance-in-regulated-industries/) Newsletter ## Two agentic-AI dispatches a month Every two weeks on Wednesday: plain-English breakdowns of what we ship, from CEO and founder Antoni Kozelski and the delivery team. No spam, unsubscribe anytime. [About the newsletter](/ai-newsletter/) FAQ ## Questions about the Academy All Using the AcademyContentWorking with Vstorm What is in Vstorm Academy? + Four things: 251 blog articles on agentic AI delivery, an AI glossary with 558 entries, long-form guides and ebooks, and 31 open-source repositories our engineers maintain. It also collects the client story videos and the AI readiness assessment. Do I have to give you my email? + Not for most of it. The blog, the glossary, the open-source repositories and the videos are open. The ebooks and the RAG guide are gated behind an email form, because they are the lead magnets that fund the rest. Is this a course with lessons and a certificate? + No. Academy is a library, not a curriculum. There is no enrollment, no sequence, and no certificate. It is the written record of what we learned building agentic AI systems for clients, published so you can read it without booking a call. Who writes this material? + It depends on the piece, and the byline says which. Our founders and research analysts write most of the strategy and market analysis, the content team writes the explainers and the glossary, and the engineers who build the systems write the deep technical posts — those also run on oss.vstorm.co. Case study numbers come from the projects themselves, and client quotes are published with a name and a title. How often does it change? + The blog is the most active surface. The glossary grows as terms come up in delivery. Open-source repository counts and star totals are point-in-time facts, refreshed when we recount them — the current figures were counted in July 2026. Can we get a workshop instead of reading? + Yes. The Pragmatics of AI workshop covers the same ground in a session run with your team, and the AI readiness assessment gives you a structured profile in 10 to 15 minutes. Both are linked from this page. Work with us ## Read enough? Bring us the workflow that is costing you the most A 30-minute call with an engineer who assesses whether an agent fits your workflow. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI consulting in Saudi Arabia URL: https://vstorm.co/agentic-ai-consulting-in-saudi-arabia Agentic AI consulting for Saudi organisations: use cases ranked by value, a Proof of Value on your data, Arabic and English agents running where your data lives. [Home](/)/[Services](/services/)/Agentic AI Consulting in Saudi Arabia # Agentic AI consulting in Saudi Arabia Agentic AI Consulting in Saudi Arabia Agentic AI consulting for Saudi organisations: use cases ranked by value, a Proof of Value on your data, Arabic and English agents running where your data lives. [Schedule free consultation](/schedule-a-meeting/) [AI agent development in KSA](/ai-agent-development-in-saudi-arabia/) use cases by value Field reports Contract review Customer service Invoice matching EN / AR Proof of Value your data on-premise when required Results in production ## What our agents do in production Three published results from agents that run in daily operations, each with a different architecture. The same team scopes, builds and hands over every system, so what is agreed in consulting is what reaches production. 44% → 98% ### Triage accuracy for STCC Clinical triage that executes the guideline step by step scored 98% on an open 50-scenario benchmark, where a raw language model scored 44% (STCC case study) 44% → 98% ### Clinical-triage accuracy at Schmitt-Thompson Executing the clinical guideline instead of relying on the raw model lifts accuracy from 44% to 98% on the open 50-scenario benchmark (Schmitt-Thompson case study) 2 hrs → 3 min ### Workflow generation at Synera An agent that validates its own steps turns a plain-language request into a working engineering workflow in minutes instead of hours (Synera case study) Bring one workflow and the documents behind it In the first session we go through the process, the systems it touches and how we would measure an agent on it before anything is built. [Schedule free consultation](/schedule-a-meeting/) FAQ ## Questions about agentic AI in Saudi Arabia Have you delivered agentic AI in Saudi Arabia before? + Yes. For a Saudi government programme aligned with Vision 2030, delivered with a local IT partner, we built the agentic layer behind an Arabic conversational agent that lets staff query incident data, statistics and geographic KPIs across 13 Amanas. Three agents work behind it, and the case study describes how. What does agentic AI consulting from Vstorm include? + We rank your workflows by value, map the one worth automating first and redesign it for agents. A Proof of Value on your data, your systems and your edge cases ends with a go or no-go decision backed by measured accuracy and running costs. If it is a go, we build the agents and hand over the code, the data and the decision logic, with a model you can swap. Can the agents work in Arabic and English? + Yes, and our Arabic-language deployments do. The ARIJ agent answers journalists in English and Arabic from ARIJ's own knowledge base, and the agent for the Saudi public-sector programme takes questions in Arabic. Arabic retrieval still has to be tested on your documents: spelling variants, mixed-language files and scanned pages all change what search finds, so we measure it before launch rather than assume it. Where does the system run, and who controls the data? + We design it to run where your data already lives, in your cloud account or the environment your security team approves, and open-weight models are an option when documents may not leave it. Access rules are checked before an agent reads a document, so a user only gets answers from files they may open. Tell us your hosting and data-protection constraints in the first session; they shape the architecture from day one. How do you know the agent's answers are correct? + By testing against your own cases before launch, not against a generic benchmark. We check whether the right information was retrieved, whether the answer stays inside it and whether actions end in the right state, and we re-run the same tests after every change to documents, prompts or models. The results go into the evidence you keep after handover. Can the agents connect to our existing systems? + That is most of the work. Agents call your CRM, ERP, document stores and internal APIs through tools with the least access they need, and every action is logged. We agree the access model with your IT and security teams before the build starts. How is Vstorm different from a global consulting firm? + We are engineers who build and run agents, with 30+ production deployments behind us and a team of 25+ AI engineers. Vstorm was the first agentic AI engineering company in the Agentic AI Foundation, and our engineers contribute to the frameworks the agents run on. You get a working system and the means to run it without us, not a strategy document. Start with one workflow ## Turn a Vision 2030 objective into a scoped agent. Map one high-value process and get a clear path from feasibility to deployed agents with measurable ROI. [Schedule free consultation](/schedule-a-meeting/)[Read the Saudi case study](/case-study/agentic-ai-in-bringing-visual-order-to-public-spaces/) ## Get to know what we write about [More articles on the blog](/ai-blog-news/) [![](/app/uploads/2026/10/cover-agentic-rag-architecture-knowledge-access-control-v3.png) Engineering ### Agentic RAG: Architecture, Knowledge, Access Control How agentic RAG changes architecture, knowledge and access control: corrective vs self-reflective designs, what they cost, and where permission checks belong. ](/rag/agentic-rag-architecture-knowledge-access-control/)[![](/app/uploads/2026/10/cover-lesson-3-system-access-is-a-hard-constraint-v3.png) Agentic AI ### Lesson 3: System access is a hard constraint Why system access (VPNs, API keys, ERP exports) belongs on the critical path of an agentic AI project, with a named owner and deadline. ](/agentic-ai/lesson-3-system-access-is-a-hard-constraint/)[![](/app/uploads/2026/10/cover-lesson-2-why-data-without-context-is-worthless-v3.png) Agentic AI ### Lesson 2: Why data without context is worthless Database records capture outcomes, not the reasoning behind them. Why AI projects need context capture and data experts from the architecture stage. ](/agentic-ai/lesson-2-why-data-without-context-is-worthless/) --- ## Agentic AI consulting services URL: https://vstorm.co/agentic-ai-consulting Agentic AI and AI agent consulting from the engineers who build the agents: workflows scored, a Proof of Value on your data, a roadmap your team runs. [Home](/)/[Services](/services/)/Agentic AI consulting services # Agentic AI consulting services Know which workflow to give an agent before you fund it Less manual work in the one workflow worth automating. Its success threshold is agreed with us first, and you stop before the full build if the Proof of Value misses it. [Book a free consultation](/schedule-a-meeting/) [See client results](#aac-proof-heading) Scope Use case Architecture Business case Agent Your code Monitored Same team 01 Scope 02 Build 03 Run and monitor Mixam: conversion to a final sale from about 20% to about 40% * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![ARIJ Network](/logos/clients/arij.png) ARIJ Network , Investigative Journalism, MENA Every inquiry answered in English and Arabic, from ARIJ's own knowledge base Bilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base. Why AI strategies stall ## An AI strategy is only as good as the first workflow it changes. Plans stall when nobody checks the data, systems and cost per task first. 1. 01 Where it stalls: Use cases picked from a list of ideas. What we do: We score your workflows, then start with one. 2. 02 Where it stalls: The roadmap comes before anyone checks the data. What we do: We check your data before any plan. 3. 03 Where it stalls: One team writes the strategy, another builds it. What we do: The engineers who scope the work build it. How we advise ## AI agent consulting, tested on your own data Ranked workflows, one Proof of Value and a roadmap your team runs. 01Scored 02Proof 03Roadmap 0103 ### Your workflows, scored We score your workflows on value, feasibility and cost of error, checked against your data. * Top workflow redesigned * Architecture choice, written down * Business case with one metric ![Mixam](/logos/clients/mixam.png)Mixam started with print-order setup; conversion to a final sale rose from about 20% to about 40%. 0203 ### A go or no-go on your data The top workflow runs on your data and moves to the build only if it meets the metric. * Decision before the full spend * Stack and security reviewed * Built from proven components ![Synera](/logos/clients/synera.svg)Synera's agent checks each workflow in several steps before it runs. 0303 ### A roadmap your team runs The next workflows follow what the first one taught, and your team owns the code and evaluations. * Points where people approve * No preferred model or vendor * Team trained to run it your workflows, scored one goes to a Proof of Value sequenced into a roadmap bars: value / feasibility Invoice intake 3 Quote requests 1 Claims triage Contracts Support inbox Order changes 2 Onboarding HR questions Month-end Price updates Returns Audit prep Quote requests on your data Proof of Value agreed metric cost per task go: build it no-go: stop early roadmap, run by your team 1 Quote requests 2 Order changes 3 Invoice intake Engineering in the open ## We help build what agentic AI runs on Open standards, open-source tooling and published research, all public, so you can [check our engineering](/agentic-ai-open-source-initiatives/) before you hire us. ![Agentic AI Foundation](/app/uploads/2026/02/Logo.png) Shaping agentic AI ### The first agentic AI engineering company to join the Agentic AI Foundation The Linux Foundation formed it around Anthropic's MCP, OpenAI's AGENTS.md and Block's goose. AWS, Google and Microsoft are platinum members. In its own words: “Together, we're shaping the future of agentic AI.” Open source ### We build open-source agent tooling that other engineering teams install 9M+ PyPI downloads 3,900+ GitHub stars 31 public repositories fix merged Framework contributors ### When the framework breaks, we fix it at the source We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase. Research ### Our engineers and PhDs publish peer-reviewed research Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results. framework 1.0 agents live, Jul 2025 Track record ### Frontier AI teams now build on what we shipped first Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day. Case studies ## Scoped and built by the same team [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [All case studies](/case-studies/) Engagement models ## Ways to start Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold. ### Start with one workflow We map one workflow with your experts and agree a success threshold before any build. Best whenYou want the success test agreed before you commit to a build. [Book a free consultation](/schedule-a-meeting/) 2. [Pragmatics of AI workshop Best whenYou need to pick the first workflow.](/pragmatics-of-ai-workshop/) 3. [Process redesign and build Best whenYou want one team accountable for the outcome.](/tristorm/) 4. [Stalled pilot recovery Best whenA pilot stuck before production, or a live agent that needs a second opinion.](/services/#group-recovery) FAQ ## Questions about agentic AI and AI agent consulting What is AI agent consulting? + AI agent consulting is advice on which workflows an AI agent should take over, how it should be built and what it should cost, checked against your data and systems. At Vstorm it is the same engagement as agentic AI consulting, run by the engineers who then build the agent: your workflows are scored on value, feasibility and the cost of an error, and the top one runs a Proof of Value on your data before the full spend. What does AI agent consulting include? + It includes your workflows ranked by value and feasibility, the redesigned process for the top workflow, an architecture choice and a business case with one success metric. The top workflow then runs a Proof of Value on your data and ends in a go or no-go decision before the full build. When should we bring in an agentic AI consultant? + Four situations come up most often. You have a long list of AI ideas and need to know which workflow an agent can take over first, and what it will return. A prototype works in a demo but not with real data, users or volume. You are choosing between a custom agent, an off-the-shelf tool and a vendor platform. Or the first agent is live and the next ones need shared foundations, governance and monitoring before they multiply the risk. Why do most AI agent projects fail before reaching production? + Most agentic AI projects fail because they are run like ordinary software projects. An agent calls models, tools and company systems, so it has failure modes a web app does not: wrong tool calls, hallucinated values, silent drift after a model update. Across 30+ production deployments we have seen the same pattern. The projects that ship treat the agent as an engineering system with tests, observability and guardrails from the first sprint, not as a prompt to be tuned later. How do you bridge the gap between our AI vision and what is technically achievable? + We check each use case against your data, your systems and your budget before anyone writes a roadmap. Then we build it on real data and stop at a Proof of Value gate: the agent either meets the agreed success metric or the project does not move on. The same engineers who scoped the work build it, so nothing is lost in a handoff between a strategy team and a delivery team. Are AI agents only for companies with large budgets? + No. We start with one workflow where an agent can pay for itself, reuse components we have already built, and scale only after the first system proves its value. You own the code, so there is no platform subscription that grows with usage. What is the difference between agentic AI and the automation tools we already use? + Rule-based automation follows a fixed path and stops when an input does not fit it. An agentic AI system decides which step to take next: it reads context, calls tools and handles exceptions that would break a scripted workflow. That flexibility is why agents need more engineering around them, including evaluation sets, permission limits and logging of every decision. How do you make sure an agentic AI project delivers measurable ROI? + We agree the success metric before the build starts and instrument the system to report it from day one. Observability shows cost per task, accuracy and time saved, so the business case is measured rather than estimated. Which business processes benefit most from agentic AI? + Knowledge-heavy processes where people spend their time gathering data, checking documents or moving information between systems. Typical examples are document processing, customer service, due diligence and multi-step workflows that span several tools. We rank candidate processes by volume, data quality and how costly an error would be, and start where the ratio is best. How long does it take to deploy a working AI agent in our existing workflow? + The first milestone is a Proof of Value on your own data, so you see whether the agent works before committing to a full build. The timeline for the full build depends on the integrations, the state of your data and your compliance requirements, and we estimate it once the Proof of Value is done. How do you scale AI agents across multiple departments without disrupting operations? + We build a shared foundation first: model access, tool integrations, evaluation and monitoring. Each new agent reuses it, so the second and third use cases ship faster than the first. New agents run alongside existing processes until their results match the agreed quality bar, and only then take over. Do we own the AI agents you build, or are we locked into your platform? + You own everything. We build on open-source frameworks and hand over the code, the prompts and the evaluation sets. There are no platform fees and no proprietary runtime, so you can change model providers, extend the system or move it to another team without asking us. Our team has no AI expertise. Can you still help us adopt agentic AI? + Yes, and that is a common starting point. We run the engineering end to end and train your team as we go, with documentation and pairing on the parts they will maintain. By the end of the engagement your engineers can operate and extend the system, and you decide how much ongoing support you want from us. Free consultation ## Bring one process to a free consultation We tell you whether an agent should change it, and how. [Book a free consultation](/schedule-a-meeting/)[Read case studies](/case-studies/) From the blog ## Reading on agentic AI consulting [More articles on the blog](/ai-blog-news/) [![](/app/uploads/2026/03/95012D22-051B-4AA2-B47D-51FC15D1C707_1_105_c-768x512.jpeg) Agentic AI ### Why most agentic AI projects fail before a single line of code is written 42% of enterprises abandoned most AI initiatives before production in 2025, up from 17% (S&P Global). The failure happens during planning. ](/agentic-ai/why-most-agentic-ai-projects-fail-before-a-single-line-of-code-is-written/)[![](/app/uploads/2026/04/288C8762-FB8D-43CE-B595-44C96A29B27F_1_105_c-768x512.jpeg) Agentic AI ### Agentic AI ROI: two returns business cases should measure Agentic AI business cases should count two returns: economic savings and the harder-to-measure gains in decision speed, knowledge and agility. ](/agentic-ai/agentic-ai-roi-two-returns-business-cases-should-measure/)[![](/app/uploads/2026/03/Group-1777-1-1.png) Agentic AI ### Boutique AI consulting firms vs Large Consultancies: Pricing and Service Comparison 2026 Boutique agentic AI consultancies against large firms: capabilities, limits, pricing bands and which engagement each model is built to carry. ](/agentic-ai/boutique-ai-consulting-firms-vs-large-consultancies-pricing-and-service-comparison/) --- ## Agentic AI in manufacturing URL: https://vstorm.co/agentic-ai-for-manufacturing Agents that generate engineering workflows, answer production questions across MES and ERP data, and flag maintenance issues before they cost a shift. [Home](/)/[Industries](/industries/)/Manufacturing # Agentic AI in manufacturing Agents that work the production floor. We build agents that turn engineering intent into complete parametric workflows and read production data directly from MES and ERP systems. Every generated workflow or query carries the validation steps a plant engineer will actually sign off on. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Bracket, 4 bolt holes, 6 mm plate design intent sketch parameters extrude hole pattern export re-checked check failed engineer draft workflow sign-off 01 Intent 02 Build & check 03 Review Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most manufacturing AI stalls at a single bolted-on chatbot Production data lives across MES, ERP, PLM and SCADA systems that were never built to talk to each other, and the judgment that resolves conflicts between them sits with a senior engineer rather than a database. A single LLM call against that sprawl, one prompt turned into one SQL query or one design brief turned into one workflow, breaks quietly the moment the schema spans many views or the design intent has more than one valid engineering path. Decomposing the task into checked steps, each validated before the next runs, is what catches a wrong join or a bad parameter before it reaches the floor. We start every manufacturing engagement by mapping which decisions an agent can make outright, which need a validation step, and which stay with an engineer — before any integration work begins. Sources Agent Outcomes Workflow agent Design intent Production data Equipment sensors Workflow output Production query Human reviewer every step validated before the next runs Use cases ## Where agents earn trust in manufacturing operations Workflows with a clear engineering or production boundary, and a validation step before anything reaches the floor. 01 ### Text-to-workflow generation An agent reads design intent and assembles a complete parametric engineering workflow automatically — replacing hours of manual node-wiring with a reviewed draft. 02 ### Natural-language production queries A multi-step agent-graph translates plain questions into verified SQL across MES and ERP schemas, instead of a single-shot query that silently returns the wrong join. 03 ### Predictive maintenance triage An agent reads equipment sensor streams, flags anomalies against maintenance history, and drafts a work order for a technician to confirm — not to approve. 04 ### Quality deviation flagging Cross-references inspection data against spec documents to flag deviations for engineering sign-off, compressing a manual paperwork trail into one reviewable record. The cost of manual engineering work ## Where engineering and production workflows lose hours today These numbers come from real shipped agentic AI engagements. Synera's is manufacturing-adjacent engineering software; the Schmitt-Thompson and Mixam figures are not manufacturing deployments — they show the same staged-validation and multi-agent orchestration mechanism in healthcare and retail. Every figure below links to the case study behind it. Agents do not replace engineering judgment — they compress the manual wiring, querying and cross-checking that sits before it, with a validation step before anything reaches the floor. Manual engineering With agents in production ### Manual workflow setup per engineer 2 hrs Each complex parametric workflow on Synera's platform meant an engineer manually wiring nodes by hand. [Synera case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) ### With text-to-workflow agents 3 min The agent reads design intent and assembles the complete workflow through multi-step validation rather than a single generation pass — a fluent but illegal graph never reaches the interpreter. [Synera case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 44% → 98% ### Accuracy lift on the open 50-scenario triage benchmark Not a manufacturing deployment, but the same staged retrieval-and-validation mechanism a production database query needs to avoid a silently wrong join. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) 95.4% ### Success rate for a production multi-agent advisor A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (retail)](/case-study/ai-agent-for-order-recommendation-and-completion/) Client results ## Proof from production manufacturing deployments Our directly-published manufacturing proof is Synera's engineering-workflow platform. The other two cases show the same staged-validation and multi-agent orchestration mechanism in healthcare and retail — not manufacturing deployments. [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [All case studies](/case-studies/) Delivery path ## From workflow audit to production agents on the shop floor TriStorm keeps engineering validation and delivery aligned — integration risk surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Scope the workflow and system boundaries We audit the target engineering or production workflow, the MES/ERP/PLM schema it touches, and existing data quality — ranking use cases by impact and integration risk. * Workflow & systems audit * Data source map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build, test and validate We implement against real production data shapes and design-intent samples, with an evaluation suite scored against known-good outputs before anything reaches the floor. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring and support Production rollout with monitoring, audit logging on every generated workflow or query, and a structured runbook handoff to engineering and operations. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own engineering and production workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in manufacturing, answered All FitUse casesComplianceDeliveryOperations Where does agentic AI actually fit on a shop floor that already runs MES, ERP, and SCADA? + In the workflows that sit between those systems today — engineering intent that becomes a parametric workflow, a production question that becomes a database query, a sensor reading that becomes a work order. We do not replace the MES or ERP. We build the agent layer that reasons across them and hands a validated result to an engineer or operator. How is this different from the automation rules we already run on the line? + Rules-based automation repeats a fixed sequence and breaks the moment a part, sensor reading, or schema does not match its assumptions. An agent reasons through the exception — checks a second source, retries a different path, or escalates, instead of stopping the line or silently returning a wrong answer. What does agentic AI actually generate for an engineering team? + For Synera, a text-to-workflow agent reads design intent and assembles a complete parametric engineering workflow automatically — work that used to mean an engineer manually wiring nodes by hand. Generating one validated workflow went from about two hours to about three minutes. Their published library of 1,000+ existing workflows was what we transformed into the training dataset; that figure describes the input we had to work with, not the number of workflows the agent has since produced. Can an agent answer questions directly against our production database? + Yes, through a hybrid agent-graph rather than a single LLM call — a graph of checked steps catches a bad join instead of shipping it. We have not yet shipped that specific pattern inside a manufacturing client; the closest reference is the guideline-executing triage pipeline we built for Schmitt-Thompson Clinical Content, which lifted a raw LLM from 44% to 98% on an open 50-scenario benchmark. The same staged-validation mechanism is what a production database query needs. How do you handle traceability for an ISO-audited or regulated process? + Every step an agent takes is logged — what data it read, what it generated, and who reviewed it before it reached production. We design that audit trail alongside the agent, not after a quality audit asks for it. What happens when the agent is not confident in a generated workflow or query? + It escalates rather than guesses. Confidence thresholds and validation gaps route the output to a human engineer with the reasoning attached — the same escalation pattern behind Synera's production system and the staged-validation pipelines we have shipped elsewhere. Can this integrate with our existing PLM, MES, or ERP without a rip-and-replace? + Yes, through your existing APIs and data infrastructure. Synera's platform integrates with systems that were already in place — the agent is a new layer, not a system replacement, and we bring the same integration discipline to a manufacturing engagement. What is the typical timeline to a working system? + A scoped Proof of Value (one workflow, real data, a working agent) typically lands in around three weeks. Full production rollout with monitoring and an operator handoff follows the same TriStorm phases as any other engagement. Start with one workflow ## Map one engineering or production workflow worth automating A 30-minute call identifies the systems involved, the validation steps required, and a realistic path to a working agent your engineering team will trust. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in construction & engineering URL: https://vstorm.co/agentic-ai-in-construction-engineering Agentic AI for construction and engineering firms — auditable agents for RFI drafting, submittal triage, field report synthesis, and change-order impact analysis. [Home](/)/[Industries](/industries/)/Agentic Ai In Construction Engineering # Agentic AI in construction & engineering Agents that work inside field and design operations. We build agents that read RFIs, drawings, specs, and daily field reports, then draft the response, report, or change-order impact a project engineer would otherwise assemble by hand. Every draft carries its sources and routes to a reviewer before it touches a contract or a schedule. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) RFI 042 agent rev B A B 1 2 A-301 rev C spec 08 71 00 draft, cites A-301 rev C project engineer 01 RFI 02 Current revision 03 Engineer review Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most construction firms have automated data entry, not decisions The volume is not anecdotal. Navigant Construction Forum examined 1,362 projects carrying 1,083,807 requests for information and found an average of 796 RFIs per project, with a median of 9.7 days to a response, closer to ten days on longer projects (2013, the most rigorous public dataset on RFI cycle time we could find). A single commercial project generates thousands of RFIs, submittals and daily field reports, most of it unstructured text, drawings and photos that no rule-based system was built to read. Put a chatbot on top of that repository and it will still miss a spec conflict or a schedule slip. An agent reads across drawings, specs and field data, takes a first-pass action such as drafting the RFI response, flagging the clash or sizing the schedule impact, then escalates to a project engineer or PM before anything touches a contract. We start every engagement by mapping which decisions an agent can draft versus must escalate, and building the audit trail a GC, owner, or compliance reviewer will actually accept. Sources Agent Outcomes Coordination agent RFIs & submittals Drawings & specs Daily fieldreports Schedule / P6 Project engineer Audit log every recommendation traces to a source document Use cases ## Where agents earn trust in construction and engineering operations Workflows with a defined source document, a clear decision boundary, and a reviewer already in the loop. 01 ### RFI and submittal triage An agent reads an incoming RFI against the drawing set and spec sections, drafts a sourced response, and routes anything ambiguous to the project engineer. 02 ### Design clash and constructibility review Cross-references structural, MEP, and architectural drawings to flag conflicts before they reach the field, for an engineer to confirm. 03 ### Daily field report synthesis Turns superintendent notes, photos, and crew logs into a structured daily report and flags schedule-risk language for review. 04 ### Change-order impact drafting Estimates the cost and schedule impact of a proposed change against the current schedule and cost code, then drafts the change order for PM sign-off. Delivery path ## From workflow audit to a production coordination agent TriStorm keeps document complexity and engineering aligned — integration risk surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Map workflow and document flow We audit the target workflow (RFI routing, submittal review, or field reporting) along with the drawing sets, specs, and systems it touches, then rank use cases by decision impact and document complexity. * Workflow audit * Document & systems map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We build against your real drawing sets, specs, and historical RFIs, with an evaluation suite scored against past decisions before a draft ever reaches a project engineer. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout integrated with your existing scheduling and document systems, with full audit logging and a runbook so your PMs and engineers run the system independently. * Production deployment * Audit trail & monitoring * Operator runbook The cost of manual document review ## Where engineering and field workflows lose days today We have not yet shipped a production agent inside a construction firm. The figures below come from adjacent engagements (Synera in engineering software and Schmitt-Thompson in clinical content), and they show the two mechanisms a submittal or RFI workflow depends on: generating a validated output instead of a first draft, and cross-checking every claim against a named source document. Every figure links to the case study behind it. Agents do not approve change orders or sign off on submittals. They compress the retrieval and cross-checking that sits before every engineering decision, and leave the decision (and the audit trail) with the project engineer. Before agents With agents in production ### To assemble one validated engineering workflow by hand 2 hrs Adjacent, not a construction firm — engineers spent hours on setup that carried no engineering judgement. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) ### With a text-to-workflow agent in production 3 min Multi-step validation rather than a single generation pass — the same difference between a drafted RFI response and a checked one. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 44% → 98% ### Guideline-executing accuracy on the open 50-scenario benchmark Adjacent, not a construction firm — Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Client results ## Proof from adjacent engineering and document-validation deployments No published construction engagement yet. These are the closest available proof for the mechanism — validated generation instead of a first draft, and every claim cross-checked against a named source document. [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [View all case studies](/case-studies/) Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own RFI, submittal and field reporting workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in construction and engineering, answered All FitUse casesComplianceDeliveryOperations How much of an engineer's time does RFI and submittal handling actually take? + The best public dataset is still Navigant Construction Forum's 2013 study of 1,362 projects covering 1,083,807 RFIs: an average of 796 RFIs per project, a median of 9.7 days to a response, and (on the estimates Navigant collected from practitioners) roughly eight hours of administrative and technical work per RFI. Those figures are over a decade old and we quote them as an order of magnitude, not a current benchmark. We would rather show you the count and cycle time from your own last three projects, which takes about a day to pull. Where does agentic AI actually fit into a construction or engineering firm? + In workflows that already produce a paper trail and have a clear decision boundary — RFI response drafting, submittal triage, daily field report synthesis, change-order impact analysis. We do not deploy agents to issue instructions to the field or sign off on a contract change. We deploy them to read, cross-check, and draft, with a project engineer or PM as the final gate. How is this different from a chatbot bolted onto our document management system? + A chatbot answers one question at a time from whatever it can find. An agent reasons across a drawing set, spec sections, and prior RFIs, completes a multi-step task, and escalates when it hits a conflict it cannot resolve. We have not yet shipped a production agent inside a construction firm specifically — the closest reference point is Synera, an engineering platform where a text-to-workflow agent reads design intent and assembles a complete parametric workflow — about two hours of manual node-wiring compressed to about three minutes. The underlying mechanism, reasoning across technical documents to produce a structured output, is the same one construction estimating and design-review workflows need. What can an agent actually take a first pass at on a construction project? + Drafting an RFI response against the drawing set and spec sections. Synthesizing a superintendent's field notes and photos into a structured daily report. Estimating the cost and schedule impact of a proposed change before a PM finalizes the change order. In each case the agent produces a draft with its sources attached, not a final answer. Can an agent catch a design clash or constructibility issue before it reaches the field? + It can flag one. Cross-referencing structural, MEP, and architectural drawings for conflicts is a pattern-matching and reasoning task well suited to an agent — it surfaces the conflict and the source pages, and an engineer confirms whether it is real. It replaces the manual page-by-page review, not the engineer's judgment. Who is liable if the agent misreads a drawing or a spec section? + Nobody signs off on the agent's read alone. Every output carries a confidence signal and a citation back to the source document, and anything below threshold, or anything touching a contract, cost, or schedule commitment, routes to a human reviewer before it moves. The agent drafts; your team decides. Does this respect our existing change-order and contract approval chain? + Yes. The agent drafts inside your existing approval workflow — it does not get signing authority and it does not bypass your PM, owner's rep, or contract administrator. Its job is to shorten the time between a triggering event and a reviewable draft, not to change who approves what. What is the timeline to a working system? + A scoped Proof of Value, one workflow, your real drawings and historical RFIs, a working agent, typically lands in about three weeks. Full production rollout with monitoring and a handoff to your team follows the same TriStorm phases as any other engagement. Does this integrate with our existing scheduling and document systems? + It integrates through your existing systems and APIs — P6, Procore, BIM 360, or whatever your document and schedule stack already is. We build against what you run, not a rip-and-replace platform, and you keep ownership of the code and the evaluation suite at handoff. Start with one workflow ## Map one RFI, submittal, or scheduling workflow worth automating A 30-minute call identifies your document flow, integration points, and a realistic path to a working agent your project engineers will actually use. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in customer service URL: https://vstorm.co/agentic-ai-in-customer-service Agentic AI for customer service — agents that answer grounded in real account and product data, and hand over to a person with the context already attached. [Home](/)/[Industries](/industries/)/Agentic Ai In Customer Service # Agentic AI in customer service Support answers grounded in real account data. We build agents that read a customer's actual account and order data, answer or route the request, and escalate only what genuinely needs a person — with the context already attached. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Queue Answered To a person support agent reads account data Where's my order? #4471 shipped Charged twice 2 charges found Change address 01 Queue 02 Answer 03 Hand off Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most support automation answers from a script, not from your data A scripted bot handles the questions someone anticipated. Everything else becomes a dead end the customer has to escape — and that escape route is not a nice-to-have. In a Gartner survey of 3,566 B2B and B2C customers run in February and March 2026, 87% said a company using generative AI for service must still provide access to a human agent. The same research found customers are markedly more willing to reuse a tool that did not make them fight their way past it. We start by mapping which requests an agent can close on evidence from your own documentation, and which should reach a person with the context already gathered. Sources Agent Outcomes Support agent Support requests Account &order data Productdocumentation Answer withcitation Human handoff Audit log every answer grounded in a named source Use cases ## Where agents earn trust in customer support Requests that need real context, not a canned response. 01 ### Email response automation Reads incoming support emails and drafts a response grounded in your actual product and account data — the same context-grounded mechanism behind our multi-agent product advisor for Mixam. 02 ### Multilingual support chat Answers questions in the languages your customers actually use, grounded in your own documentation rather than a generic translation layer. 03 ### Order and account status Answers questions grounded in the customer's real order and account data, not a generic FAQ disconnected from their actual situation. 04 ### Intelligent request routing Reads the request and routes it to the right specialist or system with reasoning attached, instead of a generic ticket queue. The cost of answering from a script ## Where support conversations lose customers today ARIJ Network is a direct multilingual support deployment. Mixam and Schmitt-Thompson are adjacent (a guided product advisor and a clinical triage system), and they demonstrate the two mechanisms a support agent lives or dies on: answering only from a named source, and declining to answer when the source does not cover the case. Every figure below links to the case study behind it. Agents do not decide refunds or rewrite policy on their own. They close the requests your own documentation already answers, and hand the rest over with the context gathered — which is the part that decides whether customers come back to the channel. 95.4% ### Success rate for a production multi-agent advisor Adjacent, not a support desk — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (guided configuration)](/case-study/ai-agent-for-order-recommendation-and-completion/) 44% → 98% ### Guideline-executing accuracy on the open 50-scenario benchmark Adjacent, not a support desk — Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Client results ## Proof from production support deployments ARIJ Network is a direct support deployment. Mixam and Schmitt-Thompson are the closest available proof for the mechanism underneath — grounded answers tied to a named source, and a refusal path that was designed rather than discovered in production. [ ### Multilingual AI agent-powered chatbot supporting journalist training A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic. EN + ARone agent answering in English and Arabic from ARIJ's own materials 2languages, English and Arabic, from one knowledge base Read case study ![ARIJ Network multilingual AI agent supporting journalist training](/_astro/pexels-cottonbro-3206120-scaled.BwNOCwHD.jpg) ](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production support agent TriStorm keeps response quality and engineering aligned — the requests an agent should never close alone are named before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Map support workflows and data We audit target request types, CRM and help-desk boundaries, and data quality — ranking automation candidates by volume and by the cost of getting the answer wrong. * Workflow & data audit * System integration map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We implement against real support data, with an evaluation suite scored for accuracy and for correct refusal before any response reaches a customer. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout with monitoring, audit logging and reporting on handoff and repeat-contact rates together, plus a structured handoff so your support team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own support workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in customer service, answered All FitUse casesDeliveryOperations What is the actual difference between a support chatbot and an agentic support system? + A chatbot answers one message at a time from a script. An agent reads the customer's account and order history, reasons across that context, and completes the request (a refund, a status update, a routing decision), escalating only when it should not decide alone. Where does agentic AI actually pay off in customer service? + Wherever a support answer needs more than a canned response — grounded in a customer's real account data, a product's real documentation, or a multilingual audience that a single script cannot serve. Do customers just ask for a human anyway? + Many will, and the design should assume it rather than fight it. In a Gartner survey of 3,566 B2B and B2C customers run in February and March 2026, 87% said a company using generative AI for service must still provide access to a human agent. Klarna is the cautionary case: on its own figures it announced in February 2024 that its assistant was handling two-thirds of service chats, and by May 2025 its CEO told Bloomberg the company had pushed cost too hard at the expense of quality and began hiring support staff again. We build the handoff as a first-class path with context attached, not as a fallback that only fires after the customer has given up. Can an agent answer support questions in more than one language? + Yes — we built a RAG-based agentic chatbot in English and Arabic for ARIJ Network that answers journalists inside Moodle from ARIJ's own materials. What other support workflows suit an agent? + Email response automation grounded in your product catalog, order-status and account questions with real context, and routing complex requests to the right specialist instead of a generic queue. Can this integrate with our existing CRM or help desk? + Yes, through your existing APIs. We map your support stack first, then build the agent to read and write through your current auth model. How do you measure whether it is actually working? + Not with a deflection rate on its own. Deflection is easy to inflate, because a customer who gets a useless answer and closes the tab without asking for a person can be counted as a success. We report the share of requests resolved with a citation the reviewer can check, the repeat-contact rate on those same requests, and the handoff rate, because a handoff rate falling while repeat contacts rise is a system getting worse, not better. Do we own the system after it is built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in. Start with one workflow ## Map one support workflow worth automating A 30-minute call identifies the request types, data sources, and a realistic path to a working agent. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in defence & public safety URL: https://vstorm.co/agentic-ai-in-defence Auditable agents for ISR fusion, predictive maintenance and incident triage — built for defence and public-safety work with no tolerance for error. [Home](/)/[Industries](/industries/)/Agentic Ai In Defence # Agentic AI in defence & public safety Agents built for zero-tolerance environments. We build agents that fuse sensor and intelligence feeds, triage maintenance and security alerts, and draft mission-ready reports — with the audit trail and human sign-off a security review board will actually approve. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) classification boundary ISR feed sensor log field report fused track draft assessment 1 2 3 commander decides 01 Fuse 02 Assess 03 Human decision Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Defence and public-safety AI dies in accreditation, not the demo Sensor fusion, maintenance triage and incident reporting are document-heavy decisions carrying a high cost of error. Accreditation is where these systems stall, not the demo: an agent that cannot show its work, cannot retain human command authority, or cannot survive a security review will never clear review. A rules engine cannot handle unstructured intelligence, and a chatbot cannot maintain the audit trail an accreditation process requires. We start every defence or public-safety engagement by mapping classification boundaries, escalation authority, and the audit log — before a line of integration code is written. Sources Agent Outcomes Triage & fusionagent Sensor & ISR feeds Maintenance logs Incident reports Commander /dispatcher Ops dashboard Audit log every recommendation logged, human authority retained Use cases ## Where agents earn trust in defence and public-safety operations Workflows with a clear human-authority boundary and a mandatory paper trail. 01 ### Intelligence & sensor fusion An agent correlates ISR feeds, sensor telemetry, and field reports into a single sourced summary — flagging anomalies for an analyst, never acting on them unsupervised. 02 ### Predictive fleet maintenance Agents read telemetry and maintenance logs across vehicle, aircraft, or equipment fleets and draft work orders ahead of failure — reviewed by a maintenance officer before dispatch. 03 ### Cybersecurity alert triage Correlates SIEM and SOC alerts across systems, drafts an incident report with evidence attached, and escalates high-confidence threats to an analyst — compressing triage time without removing the human decision. 04 ### Emergency dispatch coordination Aggregates incoming incident data and unit availability to recommend resource allocation — the dispatcher retains the final call, and every recommendation is logged. Delivery path ## From workflow audit to accredited production agents TriStorm keeps security review and engineering aligned — classification and command-authority questions surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Map workflow and classification boundaries We audit the target process, data classification, and network boundaries — ranking use cases by mission impact and accreditation risk. * Workflow & classification audit * Data boundary map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build, test and validate We implement against real sensor, log, or incident data shapes, with an evaluation suite scored against your own protocols before any recommendation reaches a human. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring and audit trail Production rollout inside your security boundary, with every action logged and a structured handoff so your operations and security teams run the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them inside your own classification and review boundaries. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. What the mechanism delivers ## Zero-tolerance validation, measured in production These numbers come from shipped agentic AI engagements outside defence — Schmitt-Thompson (healthcare), Synera (engineering software) and Mixam (print on demand). They are here because they measure the rigor bar this work needs: staged validation instead of a single pass, traceable multi-step reasoning, and orchestration that holds up under load. Every figure links to the case study behind it. Agents do not issue an order or clear a target. They gather, cross-check and draft the assessment that sits before a human decision. Every step is logged for review. 44% → 98% ### raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark Not a defence deployment — Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) 2 hrs → 3 min ### to generate a validated workflow Not a defence deployment — Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 95.4% ### Success rate in workflow results Not a defence deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) Client results ## Proof from zero-tolerance-for-error deployments We have not yet shipped a production agent inside a defence or public-safety organization. These are the closest available references — the same staged-validation and traceable-reasoning mechanism, shipped in healthcare, engineering software and print on demand. [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [View all case studies](/case-studies/) FAQ ## Agentic AI in defence & public safety, answered All FitUse casesComplianceDeliveryOperations Where does agentic AI actually fit in defence and public-safety operations? + In workflows with a clear human-authority boundary and a paper trail already required — intelligence and sensor fusion, predictive maintenance triage, cybersecurity alert correlation, incident and dispatch coordination. We deploy agents to gather, cross-check, and draft. A commander, dispatcher, or analyst stays the final decision-maker. Do you build autonomous weapons or lethal-decision systems? + No. We build decision-support and back-office agents — analysis, triage, drafting, coordination. Command authority and any use-of-force decision stay with a human, by design, not as an afterthought bolted on for compliance. What does a typical deployment look like? + An agent reads a high-volume, unstructured input (ISR feeds, maintenance logs, SIEM alerts, incident reports) cross-references it against your existing systems, and produces a sourced draft: a summary, a work order, a triage recommendation. It escalates when confidence drops instead of guessing. This is the same rigor bar our clinical triage build for Schmitt-Thompson Clinical Content met: 44% to 98% accuracy on the open 50-scenario expert benchmark in a zero-tolerance-for-error setting. How do you handle classified or controlled data? + Agents deploy inside your existing network boundary (air-gapped, on-premise, or private cloud) and operate within your current access controls and clearance model. We map the data boundary before we design the agent, not after. What happens when the agent is not confident in its output? + It escalates. Confidence thresholds and coverage gaps route to a human reviewer with the full reasoning trail attached, so nothing acts unsupervised on incomplete information. Can this integrate with existing command, control, or dispatch systems? + Yes, through your existing APIs and data infrastructure, not a rip-and-replace. Agents are built to sit alongside current C2, CAD, or maintenance-management systems, not to replace the system of record. What is the typical timeline to a working system? + A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three weeks. Full production rollout with monitoring and operator handoff follows the same TriStorm phases as any other Vstorm engagement. Do we own the agent code after deployment? + Yes. Full source ownership of agent logic, integrations, and the evaluation harness — no proprietary runtime lock-in on what we deliver. Start with one workflow ## Map one mission or safety workflow worth automating A 30-minute call identifies classification constraints, integration points, and a realistic path to a working agent your security review board will sign off on. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in corporate finance & insurance URL: https://vstorm.co/agentic-ai-in-finances Auditable agents for financial close, reconciliation, FP&A and claims verification — for banks, insurers and fintechs. SOX and GDPR-ready. [Home](/)/[Industries](/industries/)/Financial Services & Insurance # Agentic AI in corporate finance & insurance Agents that work inside close, claims, and underwriting. We build agents that reconcile ledgers, verify claims, and draft underwriting memos — every figure traced to source, with a reviewer as the final gate. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) general ledger bank statement #2041 4,120.00 #2042 980.50 #2043 1,240.00 #2041 4,120.00 #2042 980.50 #2043 1,204.00 verification agent tied out #2041 4,120.00 #2042 980.50 finance reviewer draft entry 1,240.00 vs 1,204.00 awaits sign-off 01 Pull 02 Match 03 To reviewer Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Corporate finance and insurance run on numbers no one can afford to get wrong Month-end close, claims adjudication and underwriting all depend on unstructured documents: bank statements, claim forms, submission packets. A rules engine cannot parse them, and a generic chatbot cannot be trusted to summarize them without inventing a figure. The expensive failure here is a quiet one, a plausible-sounding hallucination that lands in a ledger or a claim file before anyone catches it. Pairing retrieval with validation and a hard escalation rule closes that gap: uncertain output stops and waits for a reviewer. We design the audit trail before we design the agent — every figure traces back to its source document and every escalation is logged. Sources Agent Outcomes Verification agent Claims & policies Bank / GLstatements Budget vs actuals ERP / claims Finance reviewer Audit log every figure traced to source, nothing posted unreviewed Use cases ## Where agents earn trust in finance and insurance Recurring workflows across multiple systems — with a reviewer who signs off before anything is final. 01 ### Month-end close & reconciliation Pull GL, sub-ledger, and bank data, match transactions, and flag variances — draft entries for a controller to approve, never post unsupervised. 02 ### Claims intake & verification Cross-check claim details, policy terms, and supporting documents across systems — hours of manual review compressed to minutes before sign-off. 03 ### Underwriting submission triage Extract risk factors from packets, loss runs, and statements, then draft a structured memo — unclear cases route straight to an underwriter. 04 ### Variance analysis & commentary Compare budget to actuals, isolate material variance drivers, and draft FP&A commentary — same day, not at month-end. The cost of manual review ## Where finance and insurance workflows lose hours today These numbers come from real shipped agentic AI engagements outside finance and insurance — ARIJ Network (media), Mixam (retail) and Schmitt-Thompson (healthcare). They show the same before/after validation and multi-agent orchestration mechanism finance and insurance workflows need. Every figure below links to the case study behind it. Agents do not post unsupervised journal entries or approve claims. They compress the gathering, cross-checking and drafting that sits before every decision. Each step is logged for review. 95.4% ### Success rate for a production multi-agent advisor Not a finance deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (retail)](/case-study/ai-agent-for-order-recommendation-and-completion/) 44% → 98% ### Accuracy lift on the open 50-scenario triage benchmark Not a finance deployment — Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Client results ## Proof from finance and insurance deployments We do not yet have a published finance or insurance engagement. These cases are the closest available proof — the same multi-agent orchestration and staged-validation mechanism, shipped in retail, healthcare and media. [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [ ### Multilingual AI agent-powered chatbot supporting journalist training A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic. EN + ARone agent answering in English and Arabic from ARIJ's own materials 2languages, English and Arabic, from one knowledge base Read case study ![ARIJ Network multilingual AI agent supporting journalist training](/_astro/pexels-cottonbro-3206120-scaled.BwNOCwHD.jpg) ](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production finance agent TriStorm keeps controller and compliance sign-off aligned with engineering — control risks surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Scope the close and claims workflow We audit the target process (close calendar, claims queue, or underwriting pipeline) along with SOX, GDPR, and existing system-of-record boundaries, ranking use cases by impact and control risk. * Workflow & controls audit * Data access map * Prioritized use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build, test and validate We implement against real financial data shapes, with an evaluation suite scored against your own reconciliation rules or underwriting guidelines before any output reaches a reviewer. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring and support Production rollout with monitoring, full audit logging, and a structured handoff so your finance or claims team operates the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own finance workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in corporate finance & insurance, answered All BasicsDeliveryFitUse casesComplianceOperations What is agentic AI in finance? + Agentic AI in finance is software that plans and carries out a multi-step financial task across systems (pulling data, cross-checking it against source documents, and drafting an output) with a human reviewer as the final gate. Unlike a chatbot that answers one message at a time, an agent completes work such as reconciling a ledger or verifying a claim, and escalates when its confidence drops. It differs from analytical AI, which scores and predicts, and from generative AI, which drafts and summarizes. How quickly do finance and insurance agents pay off? + The return comes from compressing review-heavy work, not from replacing your team. Our guideline-executing validation pipeline for Schmitt-Thompson Clinical Content (a healthcare deployment, not finance) reached 98% on a curated 50-scenario open benchmark and 94% weighted F1 across 591 expert-validated scenarios — the same staged-validation approach we bring to a reconciliation or claims workflow. We scope the first workflow to reach a measurable result within weeks, then expand from proven value under the TriStorm methodology. Do you work with banks and fintechs, or only corporate finance and insurance teams? + Both. The same auditable, human-in-the-loop pattern applies across financial services — banks, fintechs, asset managers, and insurers. We do not yet have a published banking engagement, but the closest reference is Mixam, where a three-agent product advisor orchestrates against live catalog and order APIs inside one auditable system. Whatever the institution, agents gather and cross-check data and draft an output, with your reviewer as the final gate. Where does agentic AI actually fit inside corporate finance and insurance operations? + In workflows with a clear decision boundary and a document trail already required for audit — reconciliation, claims verification, underwriting intake, variance analysis. We do not deploy agents to post a journal entry or approve a claim unsupervised. We deploy them to gather, cross-check, and draft, with a controller, underwriter, or claims reviewer as the final gate. How is this different from a chatbot bolted onto our finance or claims portal? + A chatbot answers one message at a time and holds no state across a task. An agent reasons across multiple sources (a general ledger, a claim file, a submission packet) completes a multi-step task, and escalates when its confidence drops. Our guideline-executing triage pipeline for Schmitt-Thompson Clinical Content (a healthcare deployment) runs staged retrieval and deterministic disposition alongside the model, not instead of it; the same mechanism applies to a ledger or a claim file. Which corporate finance processes benefit most from agents? + Month-end close and reconciliation, and FP&A variance analysis and forecast commentary — processes with recurring structure, multiple source systems, and a reviewer who signs off before anything is finalized. Which insurance workflows are actually shipping into production right now? + Claims intake and verification, and underwriting submission triage. Both involve reading unstructured documents (claim forms, loss runs, financial statements) and producing a structured, sourced draft a human can approve quickly instead of assembling from scratch. How do you handle SOX and GDPR requirements? + Agents operate inside your existing access controls and system-of-record boundaries — we do not stand up a shadow database of financial or policyholder data. Every action is logged, and reasoning is attached to every output, so a controller or compliance reviewer can trace how a figure or a decision was reached. What happens when the agent is not confident in its output? + It stops and escalates. Confidence thresholds and document coverage gaps route to a human reviewer with the reasoning attached — the same pattern behind the staged validation layer we built for Schmitt-Thompson's clinical triage pipeline (healthcare, not finance); the escalation logic transfers directly. Can this integrate with our existing ERP, claims, or policy administration systems? + Yes, through your existing APIs and data infrastructure, not a rip-and-replace. Mixam's three-agent product advisor, for example, connects into an existing catalog and order pipeline inside one auditable system, without owning the underlying data — the same integration discipline applies to an ERP, claims, or policy administration system. Do we own the agent code after deployment? + Yes. Full source ownership of agent logic, integrations, and evaluation harnesses transfers to your team at handoff — no proprietary runtime lock-in on what we build. Start with one workflow ## Automate one close, claims, or underwriting workflow first A 30-minute call maps the controls, integration points, and a realistic path to an agent your finance or claims team will actually trust. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in healthcare URL: https://vstorm.co/agentic-ai-in-healthcare Agentic AI for healthcare — auditable agents for clinical triage support, claims verification, and patient scheduling, on HIPAA-aware infrastructure. [Home](/)/[Industries](/industries/)/Healthcare # Agentic AI in healthcare Agents that work inside clinical operations. We build agents that read clinical guidelines, verify claims, and coordinate patient touchpoints — with the audit trail and human review a clinical or compliance team will actually sign off on. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Child, fever 3 days Fever protocol no no yes triage agent acuity ER now same day routine home care nurse signs off 01 Intake 02 Protocol 03 Nurse sign-off Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most healthcare AI stops at the pilot Triage guidelines, claims documentation and patient intake all turn on unstructured input and a high cost of being wrong. Models read that input well enough. What keeps a healthcare pilot from reaching production is the machinery around it: an evaluation harness, reasoning traceable to a source, and a human-in-the-loop gate that a clinical or compliance review will accept. We start every healthcare engagement by mapping where an agent can act, where it must escalate, and how every decision gets logged — before a line of integration code is written. Sources Agent Outcomes Triage & reviewagent Patient intake Clinicalguidelines Claims documents EHR / scheduling Human clinician Audit log every recommendation traces to a sourced guideline Use cases ## Where agents earn trust in healthcare operations Workflows with a clear decision boundary and a paper trail already required. 01 ### Clinical triage guideline retrieval An agent reads proprietary triage protocols and returns a sourced, auditable recommendation — not a generic AI guess. 02 ### Claims verification Cross-checks accident and claim details against multiple internal systems before a human signs off — hours of manual review compressed to minutes. 03 ### Multi-channel patient scheduling An SMS and voice agent gathers pre-visit information and coordinates availability — reducing administrative load per physician. 04 ### Care coordination follow-up Post-discharge check-ins flag risk signals and route to staff — continuity of care without a manual call list. The cost of manual review ## Where clinical and claims workflows lose hours today These numbers come from real shipped agentic AI engagements. Schmitt-Thompson's is a direct healthcare deployment; the ARIJ Network and Mixam figures are not healthcare — they show the same before/after validation and orchestration mechanism in media and retail. Every figure below links to the case study behind it. Agents do not make unsupervised clinical judgments. They compress the gathering, cross-checking and drafting that sits before every decision. Each step is logged for review. 0 ### Accuracy lift on the open 50-scenario triage benchmark STCC's triage guideline agent was validated scenario by scenario before clinical use — every recommendation traces to a sourced guideline. [STCC case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) 95.4% ### Success rate for a production multi-agent advisor Not a healthcare deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (retail)](/case-study/ai-agent-for-order-recommendation-and-completion/) Client results ## Proof from production healthcare deployments Schmitt-Thompson is our direct healthcare proof. The other cases are outside healthcare — they show the same sourced, escalation-gated agent mechanism in media, commerce and engineering. [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [ ### Multilingual AI agent-powered chatbot supporting journalist training A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic. EN + ARone agent answering in English and Arabic from ARIJ's own materials 2languages, English and Arabic, from one knowledge base Read case study ![](/_astro/The-LLM-Book-Vstrom-2.CDz2_UY9.png) ](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [All case studies](/case-studies/) Delivery path ## From workflow audit to a production clinical agent TriStorm keeps clinical validation and engineering aligned — safety risks surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Map the process and data constraints We audit the target process, HIPAA and data-access constraints, and existing system boundaries — ranking use cases by clinical impact and implementation risk. * Workflow & compliance audit * Data access map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate against guidelines We implement against real clinical data shapes, with an evaluation suite scored against your own guidelines before any output reaches a clinician. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with clinical oversight and support Production rollout with monitoring, audit logging, and a structured handoff so your clinical and compliance teams operate the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own clinical and claims workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in healthcare, answered All BasicsFitComplianceDeliveryOutcomes What is agentic AI in healthcare? + Agentic AI in healthcare is software that plans and carries out a multi-step clinical or administrative task across systems (retrieving guidelines, cross-checking records, and drafting an output) with a clinician or reviewer as the final gate. Unlike a chatbot that answers one message at a time, an agent completes work such as a triage guideline lookup or a claims verification, and escalates when its confidence drops. It does not make unsupervised clinical judgments; it gathers, cross-checks, and drafts for human review. Where does agentic AI actually fit into clinical operations? + In workflows with a clear decision boundary and a paper trail already required — triage guideline lookup, claims verification, pre-visit intake, care coordination follow-ups. We do not deploy agents to make unsupervised clinical judgments; we deploy them to gather, cross-check, and draft, with a clinician or reviewer as the final gate. How is this different from a generic AI chatbot bolted onto our patient portal? + A chatbot answers one message at a time. An agent reasons across multiple sources (guidelines, records, claims data) completes a multi-step task, and escalates when confidence drops. Our clinical triage build for Schmitt-Thompson Clinical Content evaluated on 591 expert-validated scenarios and a curated 50-scenario open benchmark, lifting a raw LLM from 44% to 98% on the open 50-scenario benchmark. How do you handle HIPAA and patient data? + Agents integrate through secure, HL7-compliant interfaces and operate within your existing access controls — role-based permissions, encryption, and audit logging on every action. We design the data path before we design the agent. What happens when the agent is not confident in its answer? + It escalates. Confidence thresholds and guideline coverage gaps route to a human reviewer with the reasoning attached — the same pattern we built for STCC's triage system, where every recommendation traces back to a sourced guideline. Can this integrate with our existing EHR and scheduling systems? + Yes, through your existing APIs and data infrastructure, not a rip-and-replace. That is the same integration discipline behind every production agent we have shipped — for ARIJ Network, for example, the agent reads and answers only from their existing Moodle environment without touching the underlying platform. What is the typical timeline to a working system? + A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks. Full production rollout with monitoring and clinician handoff follows the same TriStorm phases as any other Vstorm engagement. What have agents actually delivered in healthcare deployments you have shipped? + For Schmitt-Thompson Clinical Content, a guideline-executing triage agent reached 98% on the open 50-scenario benchmark before going into clinical use — a real, named healthcare deployment, not a projection. Our other production agents shown below are outside healthcare (ARIJ Network, Mixam), but demonstrate the same sourced, escalation-gated mechanism applied to multilingual support and commerce workflows. What does 98% on the open triage benchmark mean for a clinical deployment? + It means the system reached expert-level disposition accuracy on a curated, nurse-validated open benchmark — 98%, on par with a 94.4% five-nurse correct-disposition rate — after lifting a raw LLM from 44% by making the model execute STCC guidelines rather than invent dispositions. Every recommendation traces to a sourced guideline question; cases outside coverage escalate to a human reviewer. Start with one workflow ## Map one clinical or claims workflow worth automating A 30-minute call identifies compliance constraints, integration points, and a realistic path to a working agent your clinical team will trust. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in mining URL: https://vstorm.co/agentic-ai-in-mining Auditable agents for predictive maintenance, safety and compliance reporting, and supply chain coordination across remote mining sites. [Home](/)/[Industries](/industries/)/Agentic Ai In Mining # Agentic AI in mining Agents that work inside remote-site operations. We build agents that read sensor and inspection data, draft maintenance and compliance recommendations, and coordinate supply chains across remote sites — with the audit trail a safety or operations team will actually sign off on. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) conveyor C-3 historian inspection maint. log 1 2 3 draft work order Replace bearing 1 vibration up 2 bearing wear 3 service overdue site engineer 01 Read 02 Draft 03 Sign-off Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Mining operations run on judgment calls that never get systematized Maintenance decisions, safety inspections and dispatch calls at remote sites depend on data scattered across historians, CMMS logs, technician notes and paper checklists. The sensors are usually fine, and so is the model. What no site safety or operations lead will trust with equipment and people on the line is a chatbot bolted onto that data with no evaluation harness, no sourced reasoning and no escalation path. We start every mining engagement by mapping which decisions an agent can draft, which must stay with a qualified reviewer, and how every recommendation gets logged — before any integration work begins. Sources Agent Outcomes Maintenance &safety agent Sensor &historian data Inspection reports Maintenance logs Work order system Site engineer Audit log every recommendation traces to its source data Use cases ## Where agents earn trust in mining operations Workflows that already produce a paper trail and already route through a human reviewer. 01 ### Predictive maintenance triage An agent reads vibration, temperature, and runtime data alongside technician notes and drafts a prioritized maintenance recommendation — with a reviewer confirming before a work order is issued. 02 ### Safety and compliance reporting Inspection checklists, near-miss reports, and environmental readings get consolidated into a structured, auditable report an agent drafts and a compliance officer reviews before filing. 03 ### Equipment failure diagnostics When a fault is flagged, an agent correlates historian data across similar equipment and past incidents to draft a likely root cause — cutting the diagnostic search before a technician is dispatched. 04 ### Supply chain and spare-parts coordination An agent tracks lead times, site inventory, and dispatch schedules across remote locations and drafts reorder or reallocation recommendations before a shortage stalls a maintenance window. Delivery path ## From workflow audit to a production mining agent TriStorm keeps operational risk and engineering aligned — integration and safety questions surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Map systems and workflow We audit the target process (maintenance backlog, shift reporting, or compliance pack) along with historian, EAM and sensor boundaries, ranking candidates by impact and integration risk. * Systems & workflow audit * Data quality review * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build, test and validate We implement against real operational data shapes, with an evaluation suite scored against your own thresholds before any output reaches an engineer. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring and support Production rollout with monitoring, full audit logging, and a structured handoff so your operations team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own maintenance and compliance workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. What the mechanism delivers ## Multi-system coordination, measured in production These numbers come from shipped agentic AI engagements outside mining — Synera (engineering software), Mixam (print on demand) and Schmitt-Thompson (healthcare). They are here because they measure what a mining workflow depends on: multi-step validation instead of a single pass, orchestration across many tools, and an extraction layer that stops rather than guesses. Every figure links to the case study behind it. Agents do not adjust a plant setpoint or sign off a maintenance order. They gather, cross-check and draft what sits before an engineer's decision. Every step is logged for review. 2 hrs → 3 min ### to generate a validated workflow Not a mining deployment — Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 95.4% ### Success rate in workflow results Not a mining deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) 44% → 98% ### raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark Not a mining deployment — Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Client results ## Proof from production agentic deployments We have not yet shipped a production agent inside a mining or heavy-industry operator. These are the closest available references — the same multi-step validation and multi-tool orchestration, shipped in engineering software, print on demand and healthcare. [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [View all case studies](/case-studies/) FAQ ## Agentic AI in mining and resources, answered All FitUse casesComplianceDeliveryOperations Where does agentic AI actually fit into a mining operation? + In workflows that already generate structured records and already require a human sign-off — maintenance work orders, inspection logs, permit and environmental reporting, procurement and dispatch coordination. We do not deploy agents to make unsupervised control decisions on physical equipment. We deploy them to read sensor and inspection data, draft the recommendation, and route it to the engineer or supervisor who signs off. How is this different from the SCADA and fleet-management automation we already run? + SCADA and fleet-management systems execute fixed control logic against fixed thresholds. An agent reasons across sources a control system was never built to read (maintenance history, technician notes, procurement lead times, weather and permit constraints) and drafts a decision with its reasoning attached, then escalates when it is not confident. It sits alongside your control systems, not inside them. Which mining workflows see agents first? + Ones with a clear paper trail already required and a human already reviewing the output: predictive maintenance triage, equipment failure diagnostics, safety and compliance inspection reporting, and supply chain or spare-parts coordination across remote sites. These are judgment-and-documentation workflows, not control-loop automation. Can an agent actually help with equipment maintenance planning? + Yes — that is the closest-fit workflow. An agent reads sensor streams, work-order history, and technician notes, then drafts a prioritized maintenance recommendation with its reasoning shown. It is the same orchestration pattern behind Synera's engineering agent platform, where generating one validated workflow went from about two hours to about three minutes — reading structured and unstructured inputs and producing a reviewable output, not a black-box verdict. How do you handle safety-critical decisions and regulatory audit requirements? + The agent never has final authority over a safety-relevant action. Every recommendation carries its source data and reasoning, is logged, and is routed to a qualified reviewer before anything happens on site. That audit trail is the same infrastructure a mine safety or environmental regulator would want to see in an inspection. What happens when the agent is not confident in a recommendation? + It escalates rather than guesses. Confidence thresholds and data-coverage gaps route the case to a human reviewer with the full reasoning chain attached, the same escalation pattern we build into every regulated deployment. Can this integrate with the equipment sensors and ERP systems we already run, across remote sites? + Yes, through existing APIs and data feeds — historian databases, CMMS, ERP, procurement systems. We do not ask you to replace SCADA or fleet-management infrastructure to add an agent layer on top of it, and we design for intermittent connectivity at remote or offshore sites from day one. What is the typical path from a first conversation to a working system? + A scoped Proof of Value (one workflow, real operational data, a working agent) lands in about three weeks under our TriStorm methodology. Production rollout with monitoring and an operator handoff follows the same phased approach we use across every industry we serve. Start with one workflow ## Map one maintenance or compliance workflow worth automating A 30-minute call identifies your site data sources, safety constraints, and a realistic path to a working agent your operations team will trust. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in print & publishing on demand URL: https://vstorm.co/agentic-ai-in-print-on-demand Agentic AI in print on demand — agents that guide product configuration across combinatorial catalogs, check supplier capacity, and route print jobs in real time. [Home](/)/[Industries](/industries/)/Agentic Ai In Print On Demand # Agentic AI in print & publishing on demand Agents that navigate a billion product combinations. We build agents that recommend the right product configuration, check it against real supplier capacity, and dispatch the job, so a customer's request becomes a printable order without a human resolving every edge case. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) A5 booklet, 40 pages combinations 1 Stock 90 gsm 130 gsm 170 gsm Binding Stapled Perfect Wire-o Finish Matte Gloss Soft-touch advisor agent 130 gsm, Stapled, Matte Supplier capacity Site A Site B Site C full dispatched 01 Request 02 Narrow 03 Dispatch Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most print-on-demand catalogs are too large for rules, and too unforgiving for guesswork A print-on-demand catalog can span a billion valid combinations of stock, format, binding and finishing, far past what a rules engine or a static configurator can branch through cleanly. Customers abandon the funnel at the first dead end; operators absorb the cost of a job routed to a supplier that cannot actually deliver it. Closing both gaps takes an agent that reasons across product, capacity and customer intent together, and escalates only the cases it genuinely cannot resolve. We start by mapping where the agent can decide outright, where it must check supplier capacity before committing, and where a human operator stays the final gate. Sources Agent Outcomes Product advisoragent Customer request Product catalog Supplier capacity Order system Print dispatch Human operator every routing decision checked against live capacity Use cases ## Where agents earn trust in print-on-demand operations Workflows with a bounded decision space, a real fallback, and a cost to getting it wrong. 01 ### Product configuration guidance An agent narrows stock, binding, and finishing choices against what the customer actually needs, across catalogs too large for a static configurator to cover. 02 ### Supplier capacity and job routing Before a job is confirmed, the agent checks it against real facility uptime and capacity, and dispatches to whichever partner in the network can actually deliver on time. 03 ### Automated pre-press file checks Files are inspected for bleed, resolution, and format issues before they reach the print floor, flagging or fixing what it can and routing the rest to an operator. 04 ### Order status and exception handling Customers get a real answer on delay, reprint, or delivery questions because the agent reads the same order and capacity data the operations team does. The cost of a dead-end configurator ## Where print-on-demand funnels lose orders today These numbers come from our shipped print-on-demand engagement — not industry averages. Every figure below links to the case study behind it. Agents do not commit a job the network cannot deliver. They narrow, check live capacity, and dispatch — and escalate to a human operator whenever confidence drops. Static configurator With agents in production ### Product combinations customers navigated alone 1B+ Mixam's customers faced paper stock, format, binding, and finishing decisions across a billion-plus combination catalog — with static filters and support tickets as the only guide. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) ### Workflow success rate with a multi-agent advisor 95.4% A PydanticAI and RAG product advisor that checks live supplier capacity at every step before a job is confirmed. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) +11.76% ### Increase in orders on day 1 of the Australian launch The published figure is scoped to one market on the first day the assistant was live. We state the scope because a single-market day-one number is not a sitewide annual result. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) 70% ### Of new users need help choosing options Mixam's own finding about its customers, not an outcome we delivered: gutter, bleed, paperweight and coating are not things a book author is expected to know. That share is the reason a guided path exists at all. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) Client results ## Proof from a production print-on-demand deployment [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [View all case studies](/case-studies/) In the industry ## Vstorm at PRINTING United Expo ![Vstorm and PRINTING AI stand at PRINTING United Expo in Las Vegas](/_astro/vstorm-printing-ai-stand.CCnsiBev.webp) PRINTING United Expo, Las Vegas ### Development partner of PRINTING AI At PRINTING United Expo in Las Vegas, Vstorm appeared as the development partner of PRINTING AI at its stand in the North Hall. Bartosz Gonczarek, PhD, brought the engineer's view: what agentic AI already does for printing companies, based on production work such as the Mixam product advisor. [Read the Mixam case study ↗](/case-study/ai-agent-for-order-recommendation-and-completion/) ![Audience at a PRINTING AI panel at PRINTING United Expo](/_astro/printing-ai-panel-audience.B8w4vbwa.webp) Panel discussions ### What printing companies asked about agentic AI Three themes came up in the audience questions. Cut through the buzz and show what AI already does in print operations. Keep every conversation grounded in real client use cases. Help companies change how they approach adopting agentic AI. [Read Bartosz's notes on LinkedIn ↗](https://www.linkedin.com/feed/update/urn:li:activity:7508969090783498240/) Delivery path ## From catalog audit to a production advisor agent TriStorm keeps the highest-friction step in your funnel or fulfillment chain the first thing we prove out, not the last. real inputs adversarial latency VALIDATING go / no-go 1 ### Scope the catalog and routing workflow We audit where customers stall in configuration and where jobs get misrouted, then map the product, supplier, and capacity data the agent will need to reason over. * Funnel & routing audit * Data access map * Prioritized use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build, test and validate We implement against real product and order data, with an evaluation suite scored before any recommendation or routing decision reaches a customer or the print floor. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring and support Production rollout with monitoring and audit logging on every recommendation and dispatch decision, handed off with a runbook your operations team can run independently. * Production deployment * Routing monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own product configuration and order workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in print & publishing on demand, answered All BasicsFitUse casesDeliveryOperationsProof What is agentic AI in print on demand? + Agentic AI in print on demand is software that completes a multi-step order task — recommending a product configuration across a large combinatorial catalog, checking it against real supplier capacity, and routing the print job, instead of answering one message at a time like a chatbot. It acts within a bounded decision space and escalates to a human operator whenever its confidence drops. Where does agentic AI actually fit into a print-on-demand operation? + In the decision points that currently force a customer to abandon a configurator or a human to manually route a job — product recommendation across a large combinatorial catalog, supplier and capacity selection, and pre-press file triage. These are workflows with a bounded decision space and a clear fallback, which is exactly where an agent can act without unsupervised risk. How is this different from the rule-based logic our MIS or storefront already runs? + Rule-based logic branches on fixed conditions and stops at the first case nobody coded for. An agent reasons across product options, supplier constraints, and customer intent at once, then completes the multi-step task — recommend, configure, and route, instead of handing the customer a dead end. Our multi-agent build for Mixam guides customers through more than a billion product combinations with a 95.4 percent agent routing success rate. What does an agent actually do in a product configurator? + It holds the conversation: narrowing paper stock, binding, and finishing options against what the customer is describing, checking those combinations against real supplier capacity, and handing off a validated spec rather than a wish list. The goal is a completed, printable order, not just a chat window. Can an agent handle supplier and capacity decisions, not just customer-facing chat? + Yes. The same reasoning layer that talks to the customer evaluates which print facility or partner in the network can actually fulfill the job on time, and dispatches accordingly. That is the harder engineering problem and where most POD automation stops short of production reliability. Does this replace our existing storefront, MIS, or ERP? + No. Agents sit on top of what you run today and connect through existing APIs — they read product data and supplier status, and write orders and routing decisions back into your systems. We do not propose a rip-and-replace of infrastructure that already works. What is the typical path to a working agent? + A scoped Proof of Value on one workflow (the highest-friction step in your funnel or fulfillment chain) typically lands in around three weeks. From there, TriStorm carries the same build into monitored production with a clear escalation path for anything the agent should not decide alone. What happens when the agent cannot confidently complete a job? + It escalates instead of guessing. Low-confidence product combinations, capacity conflicts, or files that fail pre-flight checks route to a human operator with the agent's reasoning attached, so the exception gets resolved once, not rediscovered. How do you validate an agent before it touches live orders? + Against real product and order data, scored on an evaluation suite before it reaches a customer or a print floor. That is how we got a multi-agent product advisor to a 95.4 percent routing success rate for Mixam before it became the default path through the catalog. What kind of result has agentic AI delivered in a live print-on-demand deployment? + Our multi-agent product advisor for Mixam guides customers through more than a billion product combinations, evaluates supplier capacity, and dispatches print jobs in real time — reaching a 95.4 percent agent routing success rate before it became the default path through the catalog. Start with one workflow ## Map the one configuration or routing step costing you the most orders A 30-minute call identifies where an agent can act on its own, where it must escalate, and a realistic path to a working system in production. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in retail URL: https://vstorm.co/agentic-ai-in-retail Auditable agents for guided product configuration, order exception handling and inventory coordination, built on the systems you already run. [Home](/)/[Industries](/industries/)/Retail & eCommerce # Agentic AI in retail Agents that work inside retail and DTC operations. We build agents that guide complex product decisions, resolve order exceptions, and coordinate inventory across your existing systems. Every action checks live state and logs back to the order record, so operations teams can see what the agent did and why. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Order 5120 Lamp, oak Shade ×2 damaged log exception opened stock checked replacement sent live stock Store A Warehouse Store B 01 Exception 02 Live stock 03 Replaced Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most retail AI answers the question and leaves the order unfinished Bundles, sizing, substitutions during a stockout, an order that arrived damaged: retail and DTC decisions run several steps deep and turn on configuration. A single-turn chatbot can describe a policy; it cannot check live inventory, cross-reference order history, and complete or escalate the task. An operations team will trust order-state access, catalog logic and a defined escalation path, and that is the layer most retail deployments leave out. We start by mapping which decisions the agent can complete outright, which need a human, and how every action logs back to the order system. Sources Agent Outcomes Order agent Product catalog Order &inventory data Customer history Order system Human rep Audit log every completed step logged against the order Use cases ## Where agents earn trust in retail operations Workflows where the right action depends on checking live state, not reciting a policy. 01 ### Guided product configuration An agent walks customers through complex product or bundle decisions in real time, checking compatibility and stock instead of pointing to a static filter. 02 ### Order exception & returns triage Flags damaged, delayed, or mismatched orders, pulls the relevant order and shipment data, and drafts a resolution for review or completes routine cases within set rules. 03 ### Inventory & reorder coordination Monitors stock across SKUs and locations, cross-checks lead times with suppliers, and drafts reorder recommendations before a stockout hits the storefront. 04 ### Post-purchase support, multi-channel Handles order-status, shipping, and account questions over chat, email, or SMS, reasoning across the order system instead of a scripted decision tree. The cost of static self-service ## Where commerce workflows lose orders today These numbers come from real shipped agentic AI engagements. Mixam's are direct print-on-demand commerce; the third figure is not retail — it shows the same before/after coverage mechanism in media. Every figure below links to the case study behind it. Our directly-published retail proof is from print-on-demand commerce. The multi-step orchestration mechanism (live state checks, multi-step completion, human escalation) is the same one general retail and DTC operations need; the third figure below is not retail at all, but shows the same before/after coverage mechanism. Static self-service With agents in production ### Product combinations navigated alone 1B+ Mixam's customers faced paper stock, print specification and delivery decisions across a billion-plus combination catalog — with static filters and support tickets. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) ### Workflow success rate with a multi-agent advisor 95.4% A PydanticAI and RAG product advisor built for the 70% of new users Mixam identified as needing help choosing options — checking live state at every step. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) +11.76% ### Increase in orders on day 1 of the Australian launch The published figure is scoped to one market on the first day the assistant was live. We state the scope because a single-market day-one number is not a sitewide annual result. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) EN + AR ### one agent answering in English and Arabic from ARIJ's own materials Not a retail deployment — A bilingual English/Arabic autonomous agent embedded in ARIJ's Moodle environment, answering only from ARIJ's own knowledge base. [ARIJ Network case study (media)](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) Client results ## Proof from production commerce deployments Our directly-published retail proof comes from print-on-demand commerce. The third case is not retail — it shows the same before/after coverage mechanism in media. [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ### Multilingual AI agent-powered chatbot supporting journalist training A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic. EN + ARone agent answering in English and Arabic from ARIJ's own materials 2languages, English and Arabic, from one knowledge base Read case study ![ARIJ Network multilingual AI agent supporting journalist training](/_astro/pexels-cottonbro-3206120-scaled.BwNOCwHD.jpg) ](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [All case studies](/case-studies/) Delivery path ## From workflow audit to a production order agent TriStorm keeps engineering and operations aligned — integration risk surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Scope the order and inventory workflow We audit the target workflow, catalog complexity, and existing systems (OMS, PIM, CRM) to see where decisions get made today and where an agent can act versus where it must hand off. * Workflow audit * System & data map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build, test and validate We build against real catalog and order data, with an evaluation suite scored against your actual product logic and edge cases (stockouts, substitutions, damaged orders) before it reaches a customer. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring and support Production rollout integrated with your OMS and CRM, with monitoring and an order-level audit trail your operations team can review, plus a runbook for independent operation. * Production deployment * Order-level audit log * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own merchandising and store operations workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in retail, answered All FitUse casesComplianceDeliveryOperations Where does agentic AI actually fit into a retail operation? + In workflows where the right answer depends on checking live state (stock, order history, product compatibility) rather than reciting a policy. Guided product configuration, order exception handling, and reorder coordination are good starting points. We do not deploy agents to set final prices or approve refunds unsupervised; the agent proposes, a system or a person confirms. How is this different from the chatbot our platform already ships with? + A platform chatbot answers from a script or a knowledge base, one turn at a time. An agent plans across steps — it checks stock, cross-references order history, and completes or escalates a task instead of deflecting. Our order-recommendation build for Mixam guided customers through more than a billion possible product combinations, which a scripted flow cannot do. What does guided product configuration actually look like in production? + The agent reasons over your catalog and current stock to walk a customer through a complex or configurable purchase — bundles, sizing, substitutions during a stockout, instead of a static filter UI. That is the mechanism behind the Mixam engagement: a multi-agent product advisor with live inventory checked at every step. Can an agent handle returns and order exceptions without creating new risk? + It pulls the order, shipment, and product data, drafts a resolution, and either completes routine cases within rules you set or routes anything ambiguous to a human rep with the reasoning attached. Nothing ships outside the rule boundary you define upfront. How do you handle customer and payment data? + Agents read through your existing access layer — they do not get a separate copy of customer or payment data. Integration respects your existing PCI-DSS scope and access controls. We design the data path before we design the agent. Can this integrate with our existing OMS, PIM, or CRM? + Yes, through your existing APIs and data infrastructure, not a replacement. The Mixam system integrates with the existing catalog and order pipeline using PydanticAI and retrieval over the product data already in place. What is the realistic timeline to a working system? + A scoped Proof of Value (one workflow, real catalog and order data, a working agent) typically lands in about three weeks. Full production rollout with monitoring follows the same TriStorm phases as any other engagement. What happens when the agent is not confident, or a case falls outside the rules? + It escalates instead of guessing. Confidence thresholds and rule boundaries route the case to a human rep with the reasoning attached — the same pattern we build for order exceptions and returns triage. Start with one workflow ## Map one order or catalog workflow worth automating A 30-minute call identifies integration points, catalog complexity, and a realistic path to a working agent your operations team will trust. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in supply chain URL: https://vstorm.co/agentic-ai-in-supply-chain Auditable agents for purchase order reconciliation, vendor review, fulfillment exceptions and demand-signal triage across ERP, WMS and TMS. [Home](/)/[Industries](/industries/)/Agentic Ai In Supply Chain # Agentic AI in supply chain Agents for the coordination layer of your supply chain. We build agents that read procurement documents, cross-check vendor and shipment data, and route exceptions — with the audit trail your operations team can actually trust. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Vendor Port DC ETA +6 days exception agent Purchase orders PO-1042 wk 24 at risk PO-1043 wk 26 covered PO-1051 wk 27 covered ERP / TMS updated Planner review PO-1042 + reason 01 Signal 02 Cross-check 03 Route Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Supply chain exceptions do not follow a fixed path Every ERP already automates the happy path. What consumes planners' time is the rest: a mismatched purchase order, a vendor document that needs cross-referencing, a demand signal that contradicts the forecast. Each one is a multi-source decision made under guardrails, well past what a single-step rule trigger reaches. Automating them fails quietly, when a plausible-sounding extraction lands in an exception queue and nobody checks whether the figure was on the document at all. We map where an agent can decide, where it must escalate, and how every action gets logged — before touching your ERP or TMS integration. Sources Agent Outcomes Exception &review agent Purchase orders Vendor documents Demand signals ERP / TMS Planner review Audit log every exception routed with reasoning attached Use cases ## Where agents earn trust in supply chain operations Multi-system workflows with a clear escalation path. 01 ### Purchase order reconciliation An agent cross-checks orders against contracts and receipts, flagging mismatches with sourced reasoning instead of a blanket exception queue. 02 ### Vendor performance review Reads delivery, quality, and pricing data across suppliers and drafts a sourced review — a planner signs off, not re-derives it from scratch. 03 ### Exception handling in fulfillment Routes shipment and inventory exceptions to the right system or team, with a documented reason instead of a silent manual queue. 04 ### Demand-signal triage Surfaces where incoming demand signals diverge from the current forecast, so planners investigate the exceptions that matter first. What the mechanism delivers ## Multi-system coordination, measured in production These numbers come from shipped agentic AI engagements outside supply chain — Synera (engineering software), Mixam (print on demand) and Schmitt-Thompson (healthcare). They are here because they measure the same three things a supply chain workflow depends on: multi-step validation instead of a single pass, orchestration across many tools, and an extraction layer that stops rather than guesses. Every figure links to the case study behind it. Agents do not release a purchase order or overwrite a forecast. They compress the reading, cross-checking and drafting that sits before every planner decision. Each step is logged for review. 2 hrs → 3 min ### To generate a validated workflow Not a supply chain deployment — Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 95.4% ### Success rate in workflow results Not a supply chain deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) 44% → 98% ### raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark Not a supply chain deployment — Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Client results ## Proof from production agentic deployments We have not yet shipped a production agent inside a supply-chain-specific company. These are the closest available references — the same multi-step validation and multi-tool orchestration, shipped in engineering software, print on demand and healthcare. [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production supply chain agent TriStorm keeps integration risk and engineering aligned — surfaced before full build commitment, not after. real inputs adversarial latency VALIDATING go / no-go 1 ### Map systems and workflow We audit the target process (exception queue, procurement intake, or vendor review cycle) along with ERP, WMS and TMS boundaries and the data quality behind them, ranking candidates by impact and integration risk. * Systems & workflow audit * Data quality review * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build, test and validate We implement against real procurement and logistics data shapes, with an evaluation suite scored against your own tolerance rules before any output reaches a planner. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring and support Production rollout with monitoring, full audit logging, and a structured handoff so your operations team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own procurement and fulfillment workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in supply chain, answered All BasicsFitUse casesDeliveryComplianceOperations What is agentic AI in supply chain? + Agentic AI in supply chain is software that plans and carries out a multi-step operational task across systems — reading a supplier document, cross-checking it against a purchase order and a receipt, and drafting an exception with its reasoning attached — with a planner as the final gate. Unlike a rules engine that executes a fixed path, an agent handles the cases the rules route to a person. Unlike a forecasting model, it does not predict demand; it coordinates the work around the decision. Where does agentic AI fit into supply chain operations? + In workflows that span multiple systems and require judgment under uncertainty — demand signals that do not match a forecast, a vendor document that needs cross-checking, an exception that needs routing to the right team. We build agents for the coordination layer, not to replace planners. How is this different from the automation rules already in our ERP or TMS? + Rules engines execute a fixed path. An agent reads unstructured inputs (supplier emails, shipment documents, demand signals) reasons across them, and decides within guardrails, escalating when confidence is low. It handles the exceptions your rules engine currently routes to a person. Do you have a supply chain deployment we can look at? + Not a published one. We have not yet shipped a production agent inside a supply-chain-specific company, and we would rather say so than dress up an adjacent case as vertical proof. The closest references are Synera, where multi-step validation replaced a single generation pass, and Mixam, where a three-agent system orchestrates 15 tools against more than a billion product combinations. Both are multi-system coordination under guardrails, which is the mechanism supply chain workflows need. Which supply chain workflows see agents used in production today? + Document-heavy, multi-system workflows: purchase order reconciliation, vendor performance review, exception handling in fulfillment, and demand-signal triage across sourcing and planning teams. Can an agent actually read a supplier invoice or shipping document correctly? + It can extract and cross-reference, and it must be built so that it stops rather than guesses. The mechanism is staged retrieval and validation rather than one model answering in a single pass — the same architecture behind our guideline-executing triage pipeline for Schmitt-Thompson Clinical Content, a healthcare deployment where a wrong recommendation is a patient-safety event. On a shipping document the stakes differ, the discipline does not. Can agents replace our demand planners? + No, and we do not build them to. An agent surfaces where incoming signals diverge from the forecast and assembles the evidence, so a planner spends their time on the exceptions that matter instead of finding them. The judgment call stays with your team, and every action the agent took is in the log. Can this integrate with our existing ERP, WMS, or TMS? + Yes, through your existing APIs. We map the systems of record first, then build the agent to read and write through your current auth model — no rip-and-replace. What is the typical timeline to a working system? + A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks, following the same TriStorm phases as any Vstorm engagement. What happens when the agent is not confident? + It stops and escalates. Confidence thresholds and document coverage gaps route to a planner with the reasoning attached, and the escalation is logged. Agents operate inside your existing access controls — we do not stand up a shadow copy of your supplier or order data. Do we own the agent code after deployment? + Yes. Full source ownership of agent logic, integrations, and evaluation harnesses transfers to your team at handoff — no proprietary runtime lock-in on what we build. Start with one workflow ## Map one supply chain workflow worth automating A 30-minute call identifies integration points, data quality gaps, and a realistic path to a working agent. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Open source URL: https://vstorm.co/agentic-ai-open-source-initiatives We lead open-source agentic AI initiatives — public repositories, framework contributions, Agentic AI Foundation membership, and tooling used by developers worldwide. [Home](/)/Open Source Initiatives # Open source Agentic AI Open-Source initiatives We lead open-source agentic AI initiatives to share our expertise and support the worldwide developer community in building business solutions that solve problems once deemed unsolvable. [Browse GitHub](https://github.com/vstorm-co) [Book a discovery call](/schedule-a-meeting/) vstorm-co/pydantic-deepagents _public_ PR #42: **review** feat/memory main v0.4.0 anyone can read 1. 01Branch 2. 02Pull request 3. 03Release Our partners * ![Agentic AI Foundation](/app/uploads/2026/01/AAIF_Primary_Logo_Black.svg) * ![Pydantic AI](/app/uploads/2026/01/name-and-logo-path.svg) * ![LangChain](/app/uploads/2026/01/langchain-1.svg) * ![Linux Foundation](/app/uploads/2026/01/Linux_Foundation_logo_2013.svg.png) * ![Pydantic](/app/uploads/2026/02/Pydantic.svg) How we contribute ## Creators, supporters, and leaders in agentic AI Vstorm engages in the open-source community as creators, supporters, and thought leaders in agentic AI and LLM fields. Create ### Creators We lead public agentic AI libraries that developers and companies fork, extend, and run in production. Support ### Supporters We contribute upstream to frameworks such as Pydantic AI and LangChain — the same stack we ship for clients. Lead ### Thought leaders 25+ AI engineers publish patterns, examples, and tooling so the ecosystem moves faster than any single vendor roadmap. Leading initiatives ## Where our open-source work shows up Foundation membership, upstream contributions, and libraries led by our engineers. [ Agentic AI Foundation Vstorm is the first agentic AI engineering company in the Agentic AI Foundation — shaping how agents become reliable infrastructure. Learn about AAIF](https://aaif.io/)[ Framework contributions Active support for leading agentic AI frameworks — including Pydantic AI and LangChain — so client systems sit on audited, community-backed foundations. Browse GitHub org](https://github.com/vstorm-co)[ Led initiatives Public repositories used by AI teams worldwide — guardrails, sandboxes, orchestration, and context tooling built from real delivery work. See repositories](https://github.com/vstorm-co) Companies and organizations that use our open-source software * ![NVIDIA](/app/uploads/2026/02/NVIDIA_logo-1.svg) * ![Reddit](/app/uploads/2026/02/Frame2.svg) * ![Amazon Web Services](/app/uploads/2026/02/Amazon_Web_Services_Logo-2.svg) * ![Oracle](/app/uploads/2026/02/Frame3.svg) * ![OpenAI](/app/uploads/2026/02/OpenAI_Logo-2.svg) * ![Google](/app/uploads/2026/02/logos_google.svg) * ![Nokia](/app/uploads/2026/02/Nokia_2023-2.svg) * ![Pfizer](/app/uploads/2026/02/Pfizer_2021-2.svg) * ![TikTok](/app/uploads/2026/02/logos_tiktok.svg) In numbers ## Our open-source initiatives in numbers A global community of developers shares and uses Vstorm open-source projects when building their own agentic AI solutions. 3,900+ ### GitHub stars Stars across public, non-forked repositories on github.com/vstorm-co. 31 ### Open-source projects we lead Public libraries we build and maintain for the agentic AI ecosystem. 50k+ ### Developers using our libraries Reach figure published on About us — downloads and forks across the ecosystem. Open source ## Our open-source projects Check the initiatives and projects our engineers lead on [github.com/vstorm-co](https://github.com/vstorm-co) — guardrails, sandboxes, orchestration, and context tooling from production work. 3,900+ GitHub stars across them 1,856 on full-stack-ai-agent-template alone 31 Libraries we build and maintain Public, non-forked repositories [Counted on github.com/vstorm-co, 28 September 2026](https://github.com/vstorm-co) * [![](https://opengraph.githubassets.com/1/vstorm-co/full-stack-ai-agent-template)Full-stack agents full-stack-ai-agent-template Full-stack AI app generator — FastAPI + Next.js with AI agents, RAG, streaming, and a production-minded starter layout. 1,919 stars View repo](https://github.com/vstorm-co/full-stack-ai-agent-template) * [![](https://opengraph.githubassets.com/1/vstorm-co/pydantic-deepagents)Deep agents pydantic-deepagents Open-source, self-hosted Claude Code — a terminal AI assistant and the Python framework behind it, built on Pydantic AI. 1,068 stars View repo](https://github.com/vstorm-co/pydantic-deepagents) * [![](https://opengraph.githubassets.com/1/vstorm-co/pydantic-ai-backend)File ops & sandboxing pydantic-ai-backend File storage and sandbox backends: console file tools, Docker-isolated execution, and permission presets for access control. 129 stars View repo](https://github.com/vstorm-co/pydantic-ai-backend) * [![](https://opengraph.githubassets.com/1/vstorm-co/awesome-pydantic-ai)Ecosystem list awesome-pydantic-ai An opinionated list of awesome Pydantic AI frameworks, libraries, software, and resources. 106 stars View repo](https://github.com/vstorm-co/awesome-pydantic-ai) * [![](https://opengraph.githubassets.com/1/vstorm-co/pydantic-ai-shields)Guardrails & safety pydantic-ai-shields Guardrails for Pydantic AI — cost tracking, prompt-injection detection, PII filtering, secret redaction, tool permissions. 94 stars View repo](https://github.com/vstorm-co/pydantic-ai-shields) * [![](https://opengraph.githubassets.com/1/vstorm-co/memv)Agent memory memv Structured, temporal memory for AI agents. 91 stars View repo](https://github.com/vstorm-co/memv) * [![](https://opengraph.githubassets.com/1/vstorm-co/agentcanvas)Trace visualization agentcanvas Visualize agent workflows from Logfire traces as an interactive diagram — tools, nested sub-agents, tokens, exact cost. 84 stars View repo](https://github.com/vstorm-co/agentcanvas) * [![](https://opengraph.githubassets.com/1/vstorm-co/pydantic-ai-todo)Task planning pydantic-ai-todo Task planning and tracking toolset with hierarchical subtasks, PostgreSQL multi-tenancy, and an event system for webhooks. 76 stars View repo](https://github.com/vstorm-co/pydantic-ai-todo) * [![](https://opengraph.githubassets.com/1/vstorm-co/summarization-pydantic-ai)Context management summarization-pydantic-ai LLM-powered summarization or zero-cost sliding-window trimming for long-running conversations without context overflow. 72 stars View repo](https://github.com/vstorm-co/summarization-pydantic-ai) * [![](https://opengraph.githubassets.com/1/vstorm-co/pydantic-ai-rlm)Large contexts pydantic-ai-rlm Handle extremely large contexts with any LLM provider. 63 stars View repo](https://github.com/vstorm-co/pydantic-ai-rlm) * [![](https://opengraph.githubassets.com/1/vstorm-co/subagents-pydantic-ai)Multi-agent orchestration subagents-pydantic-ai Subagent delegation — nested subagents that spawn their own specialists on the fly, with sync/async/auto mode selection. 62 stars View repo](https://github.com/vstorm-co/subagents-pydantic-ai) * [![](https://opengraph.githubassets.com/1/vstorm-co/agenticos)Agent OS agenticos The operating system for your company's AI agents — self-hosted, open source. 49 stars View repo](https://github.com/vstorm-co/agenticos) * [![](https://opengraph.githubassets.com/1/vstorm-co/production-stack-skills)Agent skills production-stack-skills Skill pack that turns any coding agent (Claude Code, Codex, AGENTS.md-compatible) into a production-stack coworker. 26 stars View repo](https://github.com/vstorm-co/production-stack-skills) * [![](https://opengraph.githubassets.com/1/vstorm-co/content-skills)Content skills content-skills Content studio skill pack for coding agents — blog, social, slides, video briefs. 24 stars View repo](https://github.com/vstorm-co/content-skills) * [![](https://opengraph.githubassets.com/1/vstorm-co/database-pydantic-ai)Database toolset database-pydantic-ai Schema exploration, SQL queries, and data analysis with read-only mode, query validation, and row limits. 22 stars View repo](https://github.com/vstorm-co/database-pydantic-ai) * [![](https://opengraph.githubassets.com/1/vstorm-co/pydantic-ai-examples)Community examples pydantic-ai-examples A community-maintained collection of Pydantic AI examples. 19 stars View repo](https://github.com/vstorm-co/pydantic-ai-examples) * [![](https://opengraph.githubassets.com/1/vstorm-co/logfire-assistant)Observability logfire-assistant AI-powered tool that helps you debug, analyze, and understand your application telemetry. 17 stars View repo](https://github.com/vstorm-co/logfire-assistant) From the blog ## Read our blog posts Notes from the engineers who maintain the repositories above. [All articles](/ai-blog-news/) [![](/app/uploads/2026/06/Group-1777-1-3.png) Engineering ### Adding Monty: a lightweight sandbox for model-written Python Monty runs model-written Python without spinning up a container. Why we added it to the Full-Stack AI Agent Template, and where it stops. ](/open-source/adding-monty-a-lightweight-sandbox-for-model-written-python/)[![](/app/uploads/2026/03/Group-1777-2-1.png) Engineering ### Pydantic Deep Agents vs LangChain Deep Agents: Which Python AI Agent Framework Should You Choose in 2026? Two open-source Python frameworks for agents that run for hours, plan work and spawn subagents. Compared on architecture, control and production fit. ](/open-source/pydantic-deep-agents-vs-langchain-deep-agents-which-python-ai-agent-framework-should-you-choose/)[![](/app/uploads/2026/07/Group-1777-5.png) Engineering ### Pydantic AI v2 and the road to production-grade agentic AI Pydantic AI v2 bundles instructions, tools, hooks and model settings into composable capabilities, with Vstorm packages linked from the Harness. ](/pydanticai/pydantic-ai-v2-and-the-road-to-production-grade-agentic-ai/) FAQ ## Frequently asked questions about open-source software All Why open sourceLicensingContributing Why does Vstorm engage in open-source initiatives? + Open source means the source code is publicly accessible to inspect, modify, and enhance. In AI engineering, the way to stay at the forefront is to contribute transparent, reliable tools — the same foundations we use for client systems so nothing we ship depends on a black box. Can I use OSS for commercial or for-profit projects? + Yes. Most open-source licenses (MIT, Apache 2.0, GPL) allow commercial use — products, services, or operations — without licensing fees. Follow the attribution requirements in each repository license file. Is OSS secure enough for professional use? + Often yes — public code lets many eyes audit for vulnerabilities and patch faster. Security still depends on keeping versions current and following sound implementation practices. Is OSS free to use? + Yes for licensing fees in most cases — but free means freedom to use, study, modify, and share, not always zero total cost. Some projects offer paid support or enterprise options; commercial use is usually allowed explicitly. What does copyleft mean? + Copyleft requires derivative works to remain open under the same license. GPL is the classic example. It contrasts with permissive licenses such as Apache 2.0. Is free software the same as OSS? + They overlap but differ in emphasis: free software prioritises user freedoms; OSS emphasises practical collaboration benefits. Most OSS qualifies as free software, but wording can differ. Can I modify OSS? + Yes under most licenses for personal or commercial use. Redistribution often requires sharing changes under compatible terms. Upstream projects may also ask for contributor agreements. What are contributor agreements? + Contracts where contributors grant maintainers rights to their code beyond the project license. They protect the project without removing openness — for example Apache CLA or dual-licensing setups. Is OSS supported? + Support comes from communities, docs, forums, and paid vendors. Popular projects tend to have stronger ecosystems; our repos ship with READMEs and examples for production-minded teams. Build on open foundations ## Ready to see how agentic AI transforms business workflows? Book a free consultation and see how we can support you in solving business problems — on the same open foundations our community uses. [Book a discovery call](/schedule-a-meeting/)[Browse GitHub](https://github.com/vstorm-co) --- ## Agentic AI training for teams URL: https://vstorm.co/agentic-ai-team-training Hands-on agentic AI training for engineers and process owners, on your own use case, taught by the engineers who build agent systems. [Home](/)/[Services](/services/)/Agentic AI training for teams # Agentic AI training for teams Your people learn on the agent they will run Your team runs, fixes and extends its own agents. Engineers and process owners learn on your agent and your real cases until they run it without us. [Book a free consultation](/schedule-a-meeting/) [See client results](#trn-proof-heading) Vstorm leadsTogetherYour team leads build tools write evals read traces ship release handle incident 1. 01We lead 2. 02Pair 3. 03You run it 30+ production deployments since 2017 * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![ARIJ Network](/logos/clients/arij.png) ARIJ Network , Investigative Journalism, MENA Every inquiry answered in English and Arabic, from ARIJ's own knowledge base Bilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base. Why AI training does not stick ## Teams train on sandboxes, then inherit a real agent. Your agent has its own tools, evaluations and failure modes, so we teach on it. 1. 01 Where it stallsEngineers practice prompting in a sandbox. What we do Engineers build, test and release in your repositories. 2. 02 Where it stallsProcess owners get an awareness session, no role. What we do Process owners approve what the agent does. 3. 03 Where it stallsWhat builders knew leaves with them at handover. What we do Your team leads releases and incidents; we review. How the training runs ## Two groups, one workflow, one system to run Taught by engineers from a team of 25+ who build agents. 1. 0103 ### Your workflow becomes the syllabus We map one workflow for an agent, mark where a person decides, and agree what good enough means. * Agent-ready steps marked * Exercises on your data * One shared success metric 2. 0203 ### Taught by engineers who build agents Your engineers ship a release only after tests pass; process owners flag wrong answers. * Build and test agents * Tracing and cost per task * Code reviews in your repository 3. 0303 ### Your team takes the controls Your engineers lead releases and incidents while ours review; process owners review failed cases. * Release and incident runbooks * A failed-case review routine * Follow-up in Vstorm Academy your workflow, as it runs today two tracks on the same system your team runs it process owners agent-ready person decides ? intake email ? check order ? price quote ? approve ? send reply engineers no agent yet your agent 1 tools + types 2 eval set 3 traces + cost run log on call: your team v1.4 released your team eval regression fixed your team model swap tested your team 1. **01**Syllabus 2. **02**Hands-on 3. **03**Handover Engineering in the open ## We help build what agentic AI runs on Open standards, open-source tooling and published research, all public, so you can [check our engineering](/agentic-ai-open-source-initiatives/) before you hire us. ![Agentic AI Foundation](/app/uploads/2026/02/Logo.png) Shaping agentic AI ### The first agentic AI engineering company to join the Agentic AI Foundation The Linux Foundation formed it around Anthropic's MCP, OpenAI's AGENTS.md and Block's goose. AWS, Google and Microsoft are platinum members. In its own words: “Together, we're shaping the future of agentic AI.” Open source ### We build open-source agent tooling that other engineering teams install 9M+ PyPI downloads 3,900+ GitHub stars 31 public repositories fix merged Framework contributors ### When the framework breaks, we fix it at the source We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase. Research ### Our engineers and PhDs publish peer-reviewed research Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results. framework 1.0 agents live, Jul 2025 Track record ### Frontier AI teams now build on what we shipped first Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day. Case studies ## Agent systems our clients' teams run [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [All case studies](/case-studies/) Engagement models ## Ways to start Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold. ### Start with one workflow We map one workflow with your experts and agree a success threshold before any build. Best whenYou want the success test agreed before you commit to a build. [Book a free consultation](/schedule-a-meeting/) 2. [Pragmatics of AI workshop Best whenYou have not chosen the workflow yet.](/pragmatics-of-ai-workshop/) 3. [Engineering-led advisory Best whenYou need an engineering view before you commit to a build.](/services/#group-advisory) 4. [Forward Deployed Engineering Best whenYour team owns the build and wants senior agent engineers next to it.](/team-extension/) FAQ ## Questions about agentic AI training All FitScopeEngineeringOwnership Who is the training for? + Two groups. Engineers learn to build agents, write evaluation sets, read traces and release changes safely. Process owners and domain experts learn to spot the steps an agent can take, decide where a person must approve, and read an evaluation report well enough to say when the agent is wrong. Both groups work on the same workflow, so they end up with a shared vocabulary. What does agentic AI training include? + Two tracks on one workflow, then a handover. The engineering track covers building agents with tools and typed outputs, writing evaluation sets, tracing runs, controlling cost per task and releasing changes through a gate. The process owner track covers finding the steps an agent can take, setting the points where a person approves, and reading evaluation results well enough to say when the agent is wrong. In the last phase your team leads releases, incidents and model changes while our engineers review, and the routine is written down as runbooks your people keep. Afterwards, guides, the glossary and our open-source agent libraries in Vstorm Academy give each track self-paced material. Do we need a working agent before the training starts? + No. We need one workflow you want an agent to take over. If you have not chosen it yet, the Pragmatics of AI workshop picks it in one session. If you already run an agent or a pilot that stalled, we can train your team on that system instead. How is this different from the Pragmatics of AI workshop? + The workshop is one working session that ranks candidate workflows and scopes a Proof of Value. The training runs alongside the build: your people learn to build, evaluate and operate the agent for that workflow, and take it over at the end. What should our engineers know beforehand? + The engineering track assumes developers who write and review code for a living and are comfortable working with APIs. No machine learning background is needed. Process owners need no technical background at all. Which frameworks do you teach? + The ones your system runs on. On a new build we choose the framework per project and write down why; on an existing system we teach the framework already in place, such as LangGraph, so your team learns what it will maintain. What does our team have at the end? + A system it runs: the code and evaluation sets in your repositories, runbooks for releases, incidents and model changes, and a routine for reviewing failed cases. Your engineers will have led releases and handled incidents while ours reviewed. You decide how much support you still want from us. Is there material the team can use on its own? + Yes. Vstorm Academy collects our blog, glossary, guides and the agent libraries we maintain in public, and most of it needs no email address. We point each group to the parts that match its track. Training consultation ## Train your team on its own agent Bring one workflow and we show how both tracks would run on it. [Book a free consultation](/schedule-a-meeting/)[Read case studies](/case-studies/) From the blog ## Reading for teams adopting agents [More articles on the blog](/ai-blog-news/) [![](/app/uploads/2026/10/cover-lesson-4-ai-agents-cannot-replace-domain-knowledge-v3.png) Agentic AI ### Lesson 4: AI agents cannot replace domain knowledge The hardest problem in an enterprise AI project is often epistemological: the knowledge the system needs was never written down. Structuring tacit domain knowledge must be a prerequisite workstream, not something the AI figures out. ](/agentic-ai/lesson-4-ai-agents-cannot-replace-domain-knowledge/)[![](/app/uploads/2026/10/cover-lesson-1-your-experts-time-is-the-ultimate-ai-bottleneck-v3.png) Agentic AI ### Lesson 1: Your experts' time is the ultimate AI bottleneck Why subject matter experts' time is the real bottleneck in agentic AI projects, and how to secure their hours before engineering starts. ](/agentic-ai/lesson-1-your-experts-time-is-the-ultimate-ai-bottleneck/)[![](/app/uploads/2026/04/2024-01-20__Y4A0426-scaled.webp) Agentic AI ### What makes a decision-maker ready for AI adoption? Years past the ChatGPT moment, hindsight is available. What separated the decision-makers who extracted value from LLMs from those who did not. ](/agentic-ai/what-makes-a-decision-maker-ready-for-ai-adoption/) --- ## Agentic AI tech stack selection URL: https://vstorm.co/agentic-ai-tech-stack-selection Agentic AI tech stack selection: framework, models, retrieval and hosting tested on your own data, with an exit path written down for every layer. [Home](/)/[Services](/services/)/Agentic AI tech stack selection # Agentic AI tech stack selection A stack you can leave without a rewrite Agents that keep working when a model or vendor changes. Framework, models and hosting chosen on tests with your own data, with a written plan to switch any of them. [Book a free consultation](/schedule-a-meeting/) [See client results](#ats-proof-heading) your criteria lock-in cost skills compliance residency framework model vectors tracing hosting Pydantic AI LangGraph LlamaIndex Hosted API Open-weight Small model pgvector Qdrant Milvus Logfire Langfuse OTel Your cloud On-prem EU region decision record + exit path 01 Criteria 02 Spike tests 03 Decision 30+ production deployments since 2017 * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![ARIJ Network](/logos/clients/arij.png) ARIJ Network , Investigative Journalism, MENA Every inquiry answered in English and Arabic, from ARIJ's own knowledge base Bilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base. Why stack choices age badly ## Stacks picked from demos, on criteria nobody wrote down. Lock-in, cost at volume and skills gaps surface a year later, when switching is expensive. 1. 01 Where it stallsThe stack is chosen after a convincing demo. What we do Criteria agreed and weighted before any demo. 2. 02 Where it stallsOptions compared on the vendor's own examples. What we do The shortlist is tested on your data. 3. 03 Where it stallsNobody plans how to leave. What we do Every layer gets a written exit path. How we choose ## Criteria first, then evidence from your data Your criteria, tested by spikes on your own data. 1. 0103 ### Weighted criteria, then shortlists Weighted criteria rule options out before anyone falls for a demo. * Criteria weighted with your team * Shortlists for all five layers * Constraints that rule options out 2. 0203 ### Spike tests on your data Two or three options per layer run on your data, and the measurements decide. * Accuracy and cost per task * Latency and glue code measured * Hosted and open-weight models compared ![Mixam](/logos/clients/mixam.png)With Mixam's print-order agent, conversion to a final sale went from about 20% to about 40%. 3. 0303 ### A decision you can reverse The record names each layer's choice, the options rejected and why, and the trade-off you accept. * A decision record per layer * An exit path per layer * Spike code in your repositories your criteria, weighted two options, spiked decided, on record lock-in cost per task team skills compliance data residency hosted model open-weight decision chosen open-weight trade-off run it in-house exit path swap via interface yours to keep decision record spike results exit path 1. **01**Criteria 2. **02**Tested 3. **03**Decided Engineering in the open ## We help build what agentic AI runs on Open standards, open-source tooling and published research, all public, so you can [check our engineering](/agentic-ai-open-source-initiatives/) before you hire us. ![Agentic AI Foundation](/app/uploads/2026/02/Logo.png) Shaping agentic AI ### The first agentic AI engineering company to join the Agentic AI Foundation The Linux Foundation formed it around Anthropic's MCP, OpenAI's AGENTS.md and Block's goose. AWS, Google and Microsoft are platinum members. In its own words: “Together, we're shaping the future of agentic AI.” Open source ### We build open-source agent tooling that other engineering teams install 9M+ PyPI downloads 3,900+ GitHub stars 31 public repositories fix merged Framework contributors ### When the framework breaks, we fix it at the source We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase. Research ### Our engineers and PhDs publish peer-reviewed research Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results. framework 1.0 agents live, Jul 2025 Track record ### Frontier AI teams now build on what we shipped first Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day. Case studies ## Agent systems our clients run [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [All case studies](/case-studies/) Engagement models ## Ways to start Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold. ### Start with one workflow We map one workflow with your experts and agree a success threshold before any build. Best whenYou want the success test agreed before you commit to a build. [Book a free consultation](/schedule-a-meeting/) 2. [Engineering-led advisory Best whenYou need an engineering view before you commit to a build.](/services/#group-advisory) 3. [Process redesign first Best whenYou know the process, not yet the agent's steps.](/agentic-process-redesign/) 4. [Sovereign AI consulting Best whenData residency or model control decides the stack.](/sovereign-ai-consulting/) Layers we compare ## The layers a stack decision covers Typical options per layer. Your criteria pick the shortlist. 1. Agent framework Compared on your data * [Pydantic AI](/pydantic-ai-development-services/) * [LangGraph](/langchain-development-company/) * [LlamaIndex](/llamaindex-development-services/) 2. Model Swappable, behind an interface * Hosted model or Open-weight model 3. Retrieval Sized for volume and data location * pgvector * Qdrant * Milvus 4. Observability Traces, evaluations, cost per task * Logfire * Langfuse * OpenTelemetry 5. Your environment Your cloud or your region * Your cloud * Self-hosted FAQ ## Questions about agentic AI stack selection All ScopeFitEngineeringOwnership What does agentic AI tech stack selection include? + A choice for every layer an agent system depends on: the agent framework, the models, retrieval and the vector store, observability, and where the system is hosted. Each layer is chosen against criteria you agree up front, such as lock-in, cost per task, your team's skills, compliance and data residency, and shortlisted options are spike-tested on your data. You get a written decision per layer with the trade-offs and rejected options, an exit path for every layer, and the spike code in your repositories. You contribute to Pydantic AI. Will you just recommend it? + No. We recommend per use case. Our engineers contribute to Pydantic AI and Vstorm is an official Pydantic implementation partner, so we know where it fits and where it does not. LangGraph, LlamaIndex or a smaller set of plain libraries can be the better choice for a given use case, and the spike results and the decision record show the reasoning, so you can check it. Hosted or open-weight models: how do you decide? + On your use case and your constraints. Hosted models are quicker to start with and need little operations work. Open-weight models can run in your own environment, which matters for data residency, regulated data and cost at high volume, but your team has to run them. We test the shortlisted models on your tasks and keep the model behind an interface, so changing it later is planned work rather than a rewrite. What is a spike test? + A short, throwaway build that runs a shortlisted option on a slice of your real case: your documents, your tools, your edge cases. It measures what the criteria ask for, such as accuracy on your tasks, cost per task, latency and how much glue code the option needs. Spikes replace vendor benchmarks with evidence from your own data. What is an exit path, and why write it down now? + For each layer, the exit path says what you would switch to, what the switch would touch and roughly how much work it is. Writing it down while the decision is fresh keeps the option open: interfaces are drawn so the model, the vector store or the tracing tool can be replaced without rebuilding the agent. A vector store or embedding model change also means re-indexing, and the exit path prices that in. We already have a stack. Can you review it instead? + Yes. We run the same criteria against what you have, spike the alternatives only where a layer looks weak, and tell you which parts to keep. If an agent built on it has stalled, an AI agent audit is usually the better starting point. Talk to the engineers ## Bring the stack question you are stuck on Bring your constraints and options; we say what we would test first, and why. [Book a free consultation](/schedule-a-meeting/)[Read case studies](/case-studies/) From the blog ## Reading on frameworks, models and tooling [More articles on the blog](/ai-blog-news/) [![](/app/uploads/2026/10/cover-single-agent-vs-multi-agent-systems-v3.png) Agentic AI ### Single agent vs multi-agent systems: when one agent is not enough One agent or several? Deterministic orchestration beats both when the flow is predictable. Pros, cons and the architectures, with production examples. ](/agentic-ai/single-agent-vs-multi-agent-systems/)[![](/app/uploads/2026/10/cover-is-higher-accuracy-always-the-goal-v3.png) Engineering ### Is higher accuracy always the goal? A “minimum 95% accuracy” promise can be worth nothing. An interactive walkthrough of precision, recall and what to ask before any number goes in writing. ](/rag/is-higher-accuracy-always-the-goal/)[![](/app/uploads/2026/10/cover-agentic-rag-architecture-knowledge-access-control-v3.png) Engineering ### Agentic RAG: Architecture, Knowledge, Access Control How agentic RAG changes architecture, knowledge and access control: corrective vs self-reflective designs, what they cost, and where permission checks belong. ](/rag/agentic-rag-architecture-knowledge-access-control/) --- ## Transformation strategy URL: https://vstorm.co/agentic-ai-transformation-strategy Vstorm helps companies transform operations with agentic AI — ROI discovery, Proof of Value and production systems you own. [Home](/)/[Services](/services/)/Transformation Strategy # Transformation strategy You are looking for effects. We deliver them. We support companies in transforming their business and operations with agentic AI technology. [Book a call](/schedule-a-meeting/) [See our transformation stories](/case-studies/) effect time scattered pilots go / no-go Quick win Proof of Value Owned at scale 01 Quick win 02 Proof of Value 03 Scale * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) * ![HiredHelpr](/logos/clients/hiredhelpr-dark.svg) HiredHelpr , Home Services Agentic AI for operational workflow automation. * ![Saudi Arabia](/logos/clients/saudi-vision-2030.svg) Saudi Arabia , Government, Public Sector 13 Amanas unified Arabic reporting over live visual-pollution data * ![ARIJ Network](/logos/clients/arij.png) ARIJ Network , Investigative Journalism, MENA Every inquiry answered in English and Arabic, from ARIJ's own knowledge base Bilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base. Effects ## You are looking for effects. We deliver them We lead intense ideation and business discovery sessions to spot the best use cases of AI for your company, working from ROI, metrics and the effects you should expect. Take the lead in your field. Gartner predicted 30% of GenAI projects would be abandoned after proof of concept by end of 2025 — none of our projects were. Industry pattern With Vstorm ### of GenAI projects abandoned after PoC 30% Gartner predicted that by the end of 2025, roughly 30% of generative AI projects would be abandoned after proof of concept — usually because the pilot never became an operating capability. [Gartner (2024)](https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025) ### Conversion lift — Mixam 2× Our project for Mixam resulted in conversion roughly doubling within a month of going live on the multi-agent product advisor. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) ### Clinical triage — STCC 44% → 98% Raw LLM baseline to guideline-executing accuracy on the open 50-scenario benchmark — on par with a 94.4% nurse-panel correct-disposition rate. [STCC case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) ### Vstorm projects abandoned after PoC 0 abandoned We lead intense discovery, measure ROI, then ship Proof of Value on real workloads — then production systems your team can own. [Case studies](/case-studies/) ### Aimed for 80%, delivered more 95.4% To be usable, the Mixam solution aimed for 80% accuracy. We exceeded that bar, and the system held up in production. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) ### Weighted F1 on 591 expert-validated scenarios 94% Fourteen dispositions across the full acuity spectrum. Disposition accuracy is 93%. [STCC case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) How we start ## Discovery built for ROI Partners from the first workshop. You know your business; we bring agentic AI engineering so we can combine expertise. 1. 01 ### Intense ideation and business discovery We lead working sessions to spot the best agentic AI use cases for your company — ROI, metrics and the effects you should expect. 2. 02 ### Take the lead in your field Every path opens with a Proof of Value on your real workloads, so the go/no-go decision rests on evidence. 3. 03 ### From sandbox to production The result is the solution you need, that your employees will use, and that reaches production rather than stopping at a slide deck. Transformation stories ## Challenge the reality. Join the leaders [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [All case studies](/case-studies/) Talk to our team ## Map one workflow worth transforming A short consultation with our CEO or Chief Transformation Officer is available right away. [Book a call](/schedule-a-meeting/)[See transformation stories](/case-studies/) Investment ## Your dollars matter. We help you spend them wisely Following our TriStorm approach we help you spot the right use case, test the best approach, and implement the solution iteratively. What you get is the solution you need, that your employees will use, and that is ready to run in production rather than in a sandbox. 74% of companies show no tangible value from AI investments — none of them worked with us. Scattered AI spend Measured path ### of companies show no tangible AI value 74% BCG surveyed 1,000 executives: roughly three in four companies have yet to turn AI spend into measurable value. [BCG — AI Adoption in 2024](https://www.bcg.com/press/24october2024-ai-adoption-in-2024-74-of-companies-struggle-to-achieve-and-scale-value) ### Workflow generation with Vstorm — Synera 2 hrs → 3 min Synera's engineers went from hours of manual setup to a validated workflow in minutes — multi-step validation, not a single generation pass. None of the surveyed 74% worked with us; we design for the opposite: prioritized use cases, Proof of Value, then production handover. [Synera case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) TriStorm ## Spot. Prove. Augment. [84% of AI project failures are leadership-driven](https://www.rand.org/pubs/research_reports/RRA2680-1.html) (RAND, 2024) — that is why we strategize your way and bring you to full transformation. outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 1 ### Strategize We start with a workshop on how your business actually runs. Together we identify the highest-ROI cases and the quick wins that build internal confidence. * Strategic Alignment * Team & Culture Readiness * Governance & Risk Readiness * Processes Maturity real inputs adversarial latency VALIDATING go / no-go 2 ### Build We test the best approach on your data and workflows, so you spend on evidence rather than another abandoned experiment. You know your business best; we bring the agentic engineering. * Working prototype on real workloads * Accuracy, latency and cost against agreed criteria * Go/no-go with a clear next investment step logfire traces BUILD eval deploy LIVE SHIPPING 3 ### Transform Do not leap into unknown waters — we cover the tech for you. We implement iteratively: data readiness, systems and integration, and the AI experience your operators will actually run. * Data Readiness * Systems & Integration * AI Experience & Experimentation Credentials ## You know your business, we know agentic AI Let us combine our expertise. Vstorm is the first agentic AI engineering company in the Agentic AI Foundation — the Linux Foundation body behind MCP and AGENTS.md — an official Pydantic implementation partner, and an open-source builder. Agentic AI Foundation The first agentic AI engineering company in the Agentic AI Foundation — the standards and community conversation for production agents. Official Pydantic partner Official implementation partner of Pydantic.dev (the company behind Pydantic and Pydantic AI) for typed, production-grade agent systems. Open source used worldwide Company behind agentic AI open-source solutions used by thousands of developers around the world — patterns that survived production. Linux Foundation ecosystem Member of the Agentic AI Foundation under the Linux Foundation — open collaboration, not vendor lock-in as a business model. Independence ## You need independence. We can deliver it The model is just a part of a system. Our engineers design model-agnostic solutions on open-source foundations — more ways to remain operational. Replaceable ### Model as a component The model is one component, replaceable and switchable as needs and business goals change. System prompt, environment, orchestration and architecture stay yours on open-source foundations. On-prem SLM ### Small language models when you need them Where it fits, our engineers deliver a tailored, fine-tuned small language model (SLM) that runs in your environment — more immunity to token-cost shifts and changing LLM vendor offers. Perimeter ### Total security From healthcare and finance: an ecosystem where no data leaves your domain and you keep total control over sensitive information — clients, patients or employees. No lock-in ### Open source, no lock-in As an official Pydantic implementation partner and open-source contributors, we give you solutions you control — no vendor lock-in or dependency. Your path ## You choose your way. We support your choice We are reliable partners for you. It is your journey and your transformation. [Book a call ↗](/schedule-a-meeting/) 01 Top-down transformation Start with a C-level-driven vision. Our consultants help forge a grounded strategy so the organization can execute. 02 Bottom-up transformation Transform through a chain of small wins — our consulting and tech teams can take the lead so each win compounds into a coherent capability. 03 Middle-tier transformation For a vision that does not fit a standard engagement model. We work from your business and your team's competence, and we map the path with you rather than fitting you to a template. “ "My wish was to come to at least an 80% success rate… by the time we finished, over 95.4%, so it definitely exceeded expectations." **Lucian Puca**, Digital Product Manager, Mixam ![Mixam](/_astro/mixam-logo.B59_GcxD.png) Common questions ## Frequently asked questions All OutcomesApproachIndependenceEngagement What kinds of effects do you actually deliver? + Published examples include roughly 2× conversion lift and 15 tools working across a billion product combinations at Mixam, workflow generation cut from three hours to three minutes at Synera, and clinical triage with 44% to 98% accuracy on the open 50-scenario expert benchmark at Schmitt-Thompson. Every engagement starts from your metrics — not a generic demo script. How do you avoid another abandoned GenAI pilot? + We lead discovery for ROI and operating fit, then Proof of Value on real workloads before wider spend. TriStorm keeps strategic alignment, validation and process augmentation in one path, so you do not fund a sandbox that never becomes an operating system. Do we need a finished AI strategy before we start? + No. We begin with a workshop on your business, then map readiness across strategy, team, governance and processes, then cover data, systems and experimentation so the roadmap is executable. Will we be locked into one model vendor? + No. We design model-agnostic systems on open-source foundations. The model is a replaceable component; orchestration, prompts, evals and ownership stay with you. Where needed we can deliver fine-tuned small language models that run in your environment. Can systems stay inside our security perimeter? + Yes. From healthcare and finance we build ecosystems where no data leaves your domain and you keep total control over sensitive information. Security and compliance constraints shape architecture from day one — not as an afterthought. Top-down or bottom-up — which do you support? + Both, and hybrids. C-level grounded strategy programmes, chains of operational wins where our teams can take the lead, or a middle path that does not fit a standard template — we design alongside your leadership and operators. Let us have a talk ## Which workflow should go first? A short consultation with our CEO or Chief Transformation Officer is available for you right away. [Book a call](/schedule-a-meeting/)[See transformation stories](/case-studies/) --- ## Agentic AI transformation consulting URL: https://vstorm.co/agentic-ai-transformation Agentic AI transformation consulting — strategy, operating-model redesign and production systems. 30+ production deployments, no lock-in. [Home](/)/[Services](/services/)/Agentic AI Transformation # Agentic AI transformation consulting Transform operations with agentic AI. Most AI programmes stall between ambition and production. Vstorm takes companies from strategy and operating-model redesign to production-grade agent systems — measured in ROI. [Book a consultation](/schedule-a-meeting/) [See our process](/tristorm/) rework Intake Re-key Check Chase Approve Close agent your team 01 As-is 02 Redesigned 03 Agents placed * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) * ![HiredHelpr](/logos/clients/hiredhelpr-dark.svg) HiredHelpr , Home Services Agentic AI for operational workflow automation. * ![Saudi Arabia](/logos/clients/saudi-vision-2030.svg) Saudi Arabia , Government, Public Sector 13 Amanas unified Arabic reporting over live visual-pollution data * Healthcare US Healthcare , Medicare Advantage 5 hrs+ saved per doctor per week Multi-channel pre-appointment agent for 100,000+ members. The transformation gap ## Why most agentic AI transformations stall — and what closing the gap looks like Adding AI to a fragmented operation does not remove the fragmentation. The programmes that reach production redesign the workflow first (ownership, reliable data, and agreed measurement), then put agents where they create leverage. These numbers show the gap and what closing it looks like in production. We close that gap before technology commitments become expensive — and hand you a system your team owns. 30% ### of generative-AI projects are abandoned after proof of concept Gartner projected that at least 30% of generative-AI projects would be abandoned after proof of concept by end of 2025 — on poor data quality, unclear business value and weak controls. The blocker is rarely the model. It is the transformation around it: ownership, reliable data and agreed measurement. [Gartner (2024)](https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025) 2 hrs → 3 min ### workflow generation for Synera — with multi-step validation We rebuilt Synera's engineering workflow around agents with RAG and validators. Workflow generation dropped from two hours to three minutes — multi-step validation sits between the model and Synera's interpreter, so a fluent but illegal graph never reaches the canvas. [Synera case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) +11.76% ### orders on day 1 of Mixam's Australian launch, at a 95.4% workflow success rate A three-agent product advisor guides Mixam customers through 1B+ product combinations, lifting orders 11.76% on day 1 of the Australian launch at a 95.4% workflow success rate — a redesign that made them an acquisition target for a global industry leader. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) Proof in practice ## Real transformations that reached production Every figure is from a live production deployment. [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [Read case studies](/case-studies/) Next step Map your first transformation workflow A 30-minute call with our engineers and consultants — one operation, the friction inside it, and a realistic path to production. No pitch deck. [Book a free consultation](/schedule-a-meeting/) Method ## From ambition to an executable, production-ready plan We begin with the outcome, map the real operation, and design a model that can be validated and implemented — not only presented. real inputs adversarial latency VALIDATING go / no-go 1 ### Map the real operation We start with the operational outcome, document how work actually moves today, and identify where redesign or AI creates leverage — not where a tool was already chosen. * Outcome and process owner * Current-state workflow map * Transformation opportunities outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Design the future operating model We define the target workflow, decision architecture, human–agent boundaries, integrations and controls — specified to the level an engineer can build from. * Future-state operating model * Roles and governance * Integration requirements BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Validate and prepare delivery We test the critical assumptions, recommend a Proof of Value scope, and outline the path to production with adoption and enablement built in. * Proof of Value recommendation * Assumption testing plan * Implementation roadmap Our process ## From ambition to production — the TriStorm methodology Finding the right thing to build comes before building it. TriStorm takes organizations from agentic AI assessment to deployed, observable production systems, with the same team across strategy and engineering. [Learn more about our process ↗](/tristorm/) Stage 1 Strategize We work with leadership and operations to find where agents create the highest operational leverage, build the business case, and produce a prioritized roadmap with an ROI model per use case — before any engineering. Stage 2 Build Before a full build, we prove the approach on your real workloads — a working prototype, tested on real and adversarial inputs, measured for value, latency and fit, so the go/no-go rests on measured evidence. Stage 3 Transform With the approach proven, we architect and build the production system with observability from day one, and run structured knowledge transfer as we go, so production means a system your team owns and can extend. No vendor lock-in. Your path ## Four ways to start an agentic AI transformation We adapt to how your organization actually makes decisions — there is no single correct entry point. 01 ### Top-down, C-level led A leadership-driven vision cascades down. We align executives, define the AI strategy, and build the roadmap the organization executes against. 02 ### Bottom-up, team led A chain of small wins that compound. We embed with your teams, find quick wins, and build the momentum that earns executive trust. 03 ### Single department Focused start in one business function. Prove the impact inside one silo before committing to a wider rollout. 04 ### Portfolio Coordinated transformation across multiple companies or units — shared governance, adapted per entity, sequenced for knowledge transfer. Leaders who build ## A transformation partner that ships production systems Vstorm is the first agentic AI engineering company in the Agentic AI Foundation and an official Pydantic implementation partner. Our PhD engineers and consultants take one team from strategy through production — with knowledge transfer and no vendor lock-in. Not sure where to start? See our [transformation strategy](/agentic-ai-transformation-strategy/) or an [AI readiness assessment](/ai-readiness-assessment/). 30 + Production deployments since 2017 90 + Projects delivered since 2017 25 + AI engineers on the team 50,000 + Developers using our open source “ "My wish was to come to at least an 80% success rate… by the time we finished, over 95.4%, so it definitely exceeded expectations." **Lucian Puca**, Digital Product Manager, Mixam ![Mixam](/_astro/mixam-logo.B59_GcxD.png) Common questions ## Frequently asked questions All ScopeFitDeliveryMeasurementBasicsApproach Is workflow redesign the same as process automation? + No. Automation applies technology to an existing task. Transformation first asks whether the process should change — removing steps, shifting responsibilities or simplifying decisions before any automation is introduced. Does every redesigned workflow need an AI agent? + No. Agents belong only where their capabilities match the requirement. Some steps are better served by conventional software, integration or clearer operating procedures. Can you transform only one workflow? + Yes. A focused, high-value workflow is often the best starting point. It creates a manageable scope and lets the organization validate its approach before expanding. When is transformation the right starting point? + When the process is fragmented across teams and systems, automation has added complexity without improving the outcome, or an AI initiative lacks a clear owner, success metric and operating context. Can you work with our internal transformation team? + Yes. Vstorm can provide agentic AI, architecture and implementation expertise alongside an existing internal or external transformation program. What happens after the future-state operating model is designed? + The next step may be internal implementation, a Proof of Value, architecture design or a wider roadmap. Vstorm can support each stage — the engagement does not require you to continue into engineering. How do you measure whether the transformation works? + Measures depend on the workflow — cycle time, throughput, quality, cost, adoption, escalation rates or customer outcomes. Baseline and target measures should be agreed before implementation. How does this relate to TriStorm? + Transformation can be a standalone engagement or the strategy phase of TriStorm. When assumptions are validated, the same team continues through Proof of Value and production engineering — no strategy-to-build handoff. What is agentic AI transformation? + It is redesigning how work gets done around AI agents that can plan, decide and act across multi-step workflows — not layering a chatbot on top of an existing process. It spans strategy, operating-model redesign, architecture and production engineering, measured by operational and financial outcomes. Who is agentic AI transformation for? + Companies across Europe, the US and the Middle East — in healthcare, manufacturing, logistics, professional services and more. You do not need to be a technology company; you need a workflow with real friction and a clear owner. How do you measure transformation success? + Against outcomes agreed before build. Real examples from production: Mixam saw +11.76% orders on day 1 of the Australian launch at a 95.4% workflow success rate; Synera cut workflow generation from two hours to three minutes through multi-step validation; STCC ran clinical triage with 98% accuracy on the open 50-scenario expert benchmark. What makes Vstorm different from a digital transformation consultancy? + We are engineers who consult, not strategists who subcontract the build. PhD engineers and senior consultants take one team from strategy through production, with knowledge transfer and no vendor lock-in. We have 30+ production deployments since 2017 — and we do not ship proofs of concept that never reach production. How do you handle AI adoption across the organization? + Adoption is where most AI initiatives stall — culture, skills and change management, not the model. We build enablement into every engagement: upskilling teams on agentic AI, designing rollout that fits your actual decision-making, and adapting to a top-down or bottom-up transformation. The goal is a system that is used, not shelved. How long does an agentic AI transformation take? + It is phased, so you see value early rather than waiting for a big-bang release. Strategy and roadmap typically run a few weeks; a first Proof of Value follows in weeks, not quarters; production rollout depends on integration and governance scope. We sequence for compounding wins, not one long program. Lead your market ## Turn agentic AI ambition into production Bring us an operation that is fragmented, hard to scale or blocked by repeated manual decisions. We will define what should change, where AI belongs, and the path to a production system your team owns — with ROI measured at each step. [Book a consultation](/schedule-a-meeting/)[Read case studies](/case-studies/) --- ## 4 Lessons from failed AI adoption ideas URL: https://vstorm.co/agentic-ai/4-lessons-from-failed-ai-adoption-ideas Four AI adoption failures drawn from 30+ agentic projects, some of them ours, and the specific decision that caused each one. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/4 Lessons from failed AI adoption ideas [Agentic AI](/ai-blog-news/#agentic-ai) # 4 Lessons from failed AI adoption ideas Four AI adoption failures drawn from 30+ agentic projects, some of them ours, and the specific decision that caused each one. ![Bartosz Adam Gonczarek](https://vstorm.co/app/uploads/2025/02/2024-01-20__Y4A0464_small-1-e1738697918189.jpg) [Bartosz Adam Gonczarek](/author/bartosz-adam-gonczarek/) Chief Transformation Officer and Co-founder of Vstorm June 12, 2026 Updated July 15, 2026 4 min read ![4 Lessons from failed AI adoption ideas](/app/uploads/2026/04/2024-01-20__Y4A0426-scaled.webp) On this page0% 1. [Lesson 1: The sky is the limit for AI](#lesson-1-the-sky-is-the-limit-for-ai) 2. [Lesson 2: Skewed perspective of 'AI taking on the human roles'](#lesson-2-skewed-perspective-of-ai-taking-on-the-human-roles) 3. [Lesson 3: Ambitious expeditions do require a guide](#lesson-3-ambitious-expeditions-do-require-a-guide) 4. [Lesson 4: Slow down with your success, turbo](#lesson-4-slow-down-with-your-success-turbo) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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After 30+ agentic AI projects across industries, from healthcare to automotive, we've picked up a fair share of stories where the path to value wasn't a straight line. Some of these stumbles were ours. Others belonged to clients who walked through our door after trying to forge ahead on their own, with mixed results. Either way, the lessons stuck. Each of the stories we share today is one that we lived through. And since we're elaborating on ineffective patterns of thinking, we refrain from using any names or brands. ## Lesson 1: The sky is the limit for AI [#](#lesson-1-the-sky-is-the-limit-for-ai) The utopian promise of all-encompassing AI is unhinged. The companies that made a big bet on the overarching capabilities of AI burned their fingers (Apple Intelligence, anyone?). What we're betting on at Vstorm, with a lot of success, is more modest. > Vstorm is in the business of 'defining parameters for AI to work', not in the business of 'solving any problem with potential intelligence explosion'. Remember Maverick's (played by Tom Cruise) saying when confronted with a vision of the future in which drones rule the sky instead of human pilots. He said, "Maybe, but not today." The few years in the LLM adoption journey taught us that each of our 30+ successful projects had one thing in common: we agreed with our customer exactly what the isolated playground for AI to excel is, so we could make the model play the rules of the game well, and achieve a success rate above human actors. We repeated the same across industries, from healthcare to automotive. Maybe someday, the sky will be the limit. For now, we advise limiting your ambition to a local playground and winning there consistently. ## Lesson 2: Skewed perspective of 'AI taking on the human roles' [#](#lesson-2-skewed-perspective-of-ai-taking-on-the-human-roles) _"I need AI to replace my team"_ — it is something we too often hear from top management. And it is the first thing that autocorrects itself during a successful project. Your management might feel like it's a good idea to put AI into human roles, but that's a temporary thing. This changes the moment we begin to ground the discussion in pragmatics and current state-of-the-art technology. One key revelation here is that LLMs are great pattern spotters that can leverage the set of patterns your business operates on. These patterns consist of records of data: how fulfilment happened, how a purchase order was registered, how the customer was answered. But even the largest store of such records does not account for what was never verbalised, and that is experience. LLMs cannot draw from that resource and are limited to recorded patterns. This is already a significant space, but if plotted as a bell curve, these patterns occupy the middle. The place where the experience of your team counts is on the edges. It is the unexpected, non-trivial, unforeseen situations where LLMs are prone to fail, but your trusted team is not. And for the foreseeable future, management needs both: AI to handle the commonalities, and people to look after it and solve for the extremities. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Lesson 3: Ambitious expeditions do require a guide [#](#lesson-3-ambitious-expeditions-do-require-a-guide) Building production-grade AI can be imagined as an expedition. One climbs a mountain to reach the business value at the top. In this analogy, Vstorm is a guide. However, it is surprising how many projects we open are actually failed expeditions, ones where the customer has tried alone, or with some underqualified IT vendor, to forge a path forward that simply did not work. At Vstorm, we like those projects over others, because they create circumstances in which our customer actually expects and hopes for us to lay out the path forward. Such a path is built in the early stage of the project, and we like building it by kicking off with an on-site workshop that joins both business and technical perspectives. Vstorm can be your sherpa if you take your preparation seriously. ## Lesson 4: Slow down with your success, turbo [#](#lesson-4-slow-down-with-your-success-turbo) We have seen situations where our customers, after getting a reliable proof-of-concept working at 80–90% accuracy, felt that the solution was good enough for productive use and simply ran with it. While we were happy with their enthusiasm, such an approach usually backfires. Any AI agent is a stochastic element in a business process, very unlike rule-oriented, deterministic systems. And because the workings of AI are probability-based, they require a different approach to testing. The same result passing a test twice does not mean it would not change on the third run. So an agent working 'good enough' is not actually good enough just yet. Even with a success, a working proof-of-concept, around the corner, we take a well-crafted path to ensure that the AI can be deployed with good oversight, protective measures, and full observability. Only then can we expect to gain the benefits of using AI instead of exposing ourselves to unforeseen problems. #### **Are you ready to discuss your company's transformation with us?** We have a white-glove treatment for executives and business owners that, surprisingly, is human-first. Contact our executive leaders to schedule your 20-minute call so we can help you get the journey started. ![Bartosz Adam Gonczarek](https://vstorm.co/app/uploads/2025/02/2024-01-20__Y4A0464_small-1-e1738697918189.jpg) [Bartosz Adam Gonczarek](/author/bartosz-adam-gonczarek/) Chief Transformation Officer and Co-founder of Vstorm [Author profile](/author/bartosz-adam-gonczarek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Agentic AI for production scheduling in manufacturing](/app/uploads/2026/06/Group-1777-6.png) ### Agentic AI for production scheduling in manufacturing Read article ](/agentic-ai/agentic-ai-for-production-scheduling-in-manufacturing/)[ ![Agentic AI services market growth: who is leading](/app/uploads/2026/06/Group-1777-2.png) ### Agentic AI services market growth: who is leading Read article ](/agentic-ai/agentic-ai-services-market-growth-who-is-leading/)[ ![Why manufacturing AI stalls before it reaches the shop floor](/app/uploads/2026/06/Group-1777-1.png) ### Why manufacturing AI stalls before it reaches the shop floor Read article ](/agentic-ai/why-manufacturing-ai-stalls-before-it-reaches-the-shop-floor/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI company URL: https://vstorm.co/agentic-ai/agentic-ai-company Vstorm is an agentic AI engineering company — production-grade agents delivered through the TriStorm methodology, with full code ownership. [Home](/)/[Industries](/industries/)/Agentic Ai Company # Agentic AI company An engineering partner, not a slide deck. Vstorm is the first agentic AI engineering company in the Agentic AI Foundation, and a Pydantic AI partner since its beta versions. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Plan Act Observe Reflect iter **1** erp.fetch() GoalGoal met Reconcile invoices * Fetch invoices * Match payments * Report gaps Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most agentic AI vendors sell a demo. We ship a system someone operates. Picking a partner for agentic AI usually comes down to a demo and a deck — neither tells you whether the vendor can get a system through evaluation, integration, and a compliance review, and hand it off so your team can run it without them. That gap is where most engagements stall after the contract is signed. We map the engineering questions before the sales questions: what does the eval suite look like, who owns the code, what happens when the model changes. Sources Agent Outcomes TriStorm engagement Businessrequirement Existing systems Risk tolerance Production agent Your team, trained Full codeownership every engagement ends in a handoff, not a retainer Engagement models ## How companies work with us The same TriStorm methodology, applied at the scope you need. 01 ### Strategize A workflow audit and use-case feasibility assessment that turns an AI vision into a prioritised, ROI-ranked implementation plan. 02 ### Build A working system on your real data with an evaluation suite and guardrails — evidence for a build decision, not a slide deck. 03 ### Embedded engineering Our engineers work inside your existing team, contributing agentic-specific expertise while transferring capability rather than creating a dependency. 04 ### Production augmentation and handoff Production rollout with monitoring and governance, ending in full ownership transfer — your team runs the system independently. Delivery path ## The TriStorm methodology After 30+ production deployments, we codified what separates shipped systems from stalled pilots. 01 ### Strategize Workflow audit, use-case feasibility, and an ROI model — a prioritised implementation plan before any build starts. Workflow & data auditFeasibility assessmentROI projection 02 ### Build A working system on your data, with an evaluation suite and guardrails — evidence for a build decision. Working prototypeEvaluation suiteProduction path 03 ### Transform Production rollout with monitoring and governance, ending in ownership transfer to your team. Production deploymentMonitoring & alertingOwnership transfer Client results ## Proof across industries [View all case studies](/case-studies/) [![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) Engineering platform **1,000+**workflows used as training data A text-to-workflow agent reads design intent and generates complete engineering workflows automatically, compressing multi-hour tasks to seconds. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/)[ Healthcare, Guideline-executing triage **44% → 98%**raw LLM vs guideline-executing accuracy on the open benchmark Guideline-executing medical triage reaching expert-level accuracy on an open, nurse-validated benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/)[ Print on demand **95.4%**agent routing success rate A multi-agent product advisor guiding customers through more than a billion product combinations, with orders up after go-live. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) FAQ ## Choosing an agentic AI company, answered All FitUse casesOperations What actually distinguishes an agentic AI company from a general software or AI consultancy? + Depth in the specific engineering agentic systems require: orchestration across multiple agents, evaluation harnesses that catch failures before production, and guardrails calibrated to your risk tolerance. General software consultancies build features; agentic AI companies build systems that take autonomous action and are accountable for it. How is Vstorm different from other agentic AI vendors? + We are the first agentic AI engineering company in the Agentic AI Foundation and a Pydantic AI partner since its beta versions. We work as an embedded engineering partner — you own the code, the architecture, and the operational knowledge by the time we hand off. What does an engagement with Vstorm actually look like? + TriStorm: strategic alignment and planning, a Proof of Value on your real data, then process augmentation into production with monitoring and ownership transfer. The same three phases regardless of industry or use case. Do you only work with large enterprises? + No. We work with companies that need production-grade agentic AI without enterprise consulting overhead or a bloated internal AI team. Do we own the code after the engagement? + Yes. Full ownership of agent logic, integrations, and evaluation harnesses — no proprietary runtime lock-in, and your team is trained to operate and extend what we build. What if we already have an internal AI team? + We work as an embedded extension of it — bringing agentic-specific patterns your team may not have built before, and transferring that knowledge rather than creating a dependency. Start with a conversation ## Talk to the engineers who will build your system. Book a free consultation. We will map one real workflow worth automating — no pitch, no deck. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AI agents can act: can you still control them? URL: https://vstorm.co/agentic-ai/agentic-ai-control-plane Agents that issue refunds need identity, authorisation, approval and audit before the call lands. What an agentic AI control plane is, and when you need one. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/AI agents can act: can you still control them? [Agentic AI](/ai-blog-news/#agentic-ai) # AI agents can act: can you still control them? Agents that issue refunds need identity, authorisation, approval and audit before the call lands. What an agentic AI control plane is, and when you need one. ![Karol Piekarski](https://vstorm.co/app/uploads/2025/09/profile_picture.jpeg) [Karol Piekarski](/author/karol-piekarski/) PhD, Agentic AI & Data Engineer September 11, 2026 Updated September 21, 2026 13 min read ![AI agents can act: can you still control them?](/app/uploads/2026/10/cover-agentic-ai-control-plane-v3.png) On this page0% 1. [Three versions of the same agent](#three-versions-of-the-same-agent) 2. [Borrowed authority](#borrowed-authority) 3. [Where the rules live](#where-the-rules-live) 4. [An approval must name the act](#an-approval-must-name-the-act) 5. [The retry that paid twice](#the-retry-that-paid-twice) 6. [Reconstructing €63,000](#reconstructing-63-000) 7. [The nine meanings of stop](#the-nine-meanings-of-stop) 8. [The plane appears](#the-plane-appears) 9. [The same shape, different verbs](#the-same-shape-different-verbs) 10. [Not every agent needs one](#not-every-agent-needs-one) 11. [Sources](#sources) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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Agents issue refunds, cancel orders, and alter customer records. A mistake can therefore reach a live business system before anyone is in a position to stop it. Suppose a customer writes in to say a parcel never arrived. Software reads the order, checks the carrier's tracking, weighs the customer's history against the claim, judges the complaint honest and sends back €729. No human approves the payment, because the system was built so that none would have to. The money is gone before anyone knows the conversation took place. Selling the machinery to keep such decisions inside their bounds is now a business. By late 2025 Microsoft had announced a product it called a control plane for AI agents, and Forrester, a research firm, had made the agent control plane a distinct functional plane in its model of enterprise architecture. Six months later Forrester was publishing a market landscape covering 34 vendors. Fiddler, an observability firm inside the category itself, describes the same stretch less charitably. The phrase went “from a phrase nobody said out loud to one on nearly every vendor's keynote slide”, it says, and “has become a catch-all that every vendor bends toward whatever they already sell”. Scepticism about the label is warranted, then, and the thing underneath it is still real. Gartner's reference architecture for multi-agent platforms, published in May 2026, treats the category as settled enough to name an agent control plane and an agent runtime authorisation component as distinct components, alongside agent gateways, asset registries, observability and guardrails. Neither analyst firm sells the software. What vendors still disagree about is where in the stack the controls belong. One ordinary customer-support agent is enough to show why it is at all necessary. ## Three versions of the same agent [#](#three-versions-of-the-same-agent) ![Three tinted cards for the same agent (talks, looks, acts), each listing the tools it adds; only version 3 moves money itself, calling refund\_order() for €729, and the question shifts from how well it reasons to what it may do and on whose authority.](/app/uploads/2026/10/fig-agentic-ai-control-plane-ex-cp-versions.png) The model stays the same; its authority does not. The first version only talks. It searches the help centre, summarises tickets and answers questions. Its failures are embarrassing but cheap. A wrong answer costs an apology. The second version can look. The business connects it to the customer relationship management (CRM) system, the order database, the shipping provider, and the customer's history. Now it investigates rather than guesses. It can see that a parcel has been sitting at a depot for nine days, and it hands that finding to a support employee, who decides what to do and clicks the button. The third version can act. The reasoning is hard to resist: the agent already understands the problem, and the employee is only approving what it found. So the business removes the click, and the agent gains a refund API, a coupon system, and the power to modify orders and shipments. Which returns us to the lost parcel. The agent works through the order, the tracking status and the customer's record, decides the complaint is genuine and calls `refund_order()` for €729. Nothing about the model changed between version one and version three. What changed is the question the business must answer. In version one the question was whether the model reasons well. In version three the question is what this thing may do, on whose authority, and how anyone would know. Every component that vendors argue about falls out of that question. ## Borrowed authority [#](#borrowed-authority) The refund service logs that `support-agent-17` made the call. That names a credential. The actor behind it stays hidden. The same agent may be working for a customer in a self-service portal, for a support employee handling a ticket, or unattended, as part of a nightly batch that no person triggers but someone has still authorised. Those three cases should carry different rights. An agent should not inherit a finance director's powers merely because the finance director built the workflow. This is not only a vendor preoccupation. America's National Institute of Standards and Technology [published a concept paper in February 2026](https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd) on agent identity and authorisation, seeking comment on “identification, authorization, auditing and non-repudiation of AI agents, as well as controls to prevent and mitigate prompt injection techniques.” It frames the problem as one of identity and authorisation rather than reaching for the language of a control plane, which makes it the more useful corroboration. The requirements arrive at the same place from a different direction. The plumbing is entering standards work too. [An Internet-Draft on AI agent authentication and authorisation](https://datatracker.ietf.org/doc/html/draft-klrc-aiagent-auth-03), filed in July 2026, applies existing OAuth-style identity machinery to agents rather than inventing new protocols. Its authors come from Amazon Web Services, Okta, Ping Identity, Zscaler and OpenAI. Their affiliations show the approach has drawn contributors from across the identity, cloud and AI industries. It is an individual submission, not an adopted standard, and should be read as evidence of convergence rather than of consensus. The requirement itself is old. The action must carry a chain: the human who started the task, the agent and its version, the service account it used, and the scope delegated for this run. Long-lived API keys in a configuration file carry none of that. ## Where the rules live [#](#where-the-rules-live) A common early answer is to write the rules into the system prompt: “Never refund more than €1,000 without approval.” That shapes the model's reasoning, and the model is the thing being controlled, so the rule can only ever be as strong as the model's compliance. A prompt injection in a customer's message, a model upgrade, or a badly phrased ticket can talk the agent out of it. The rule should live outside the model, in something that reads the proposed action and decides: Refund amount What happens Under €100 Executes €100 to €1,000 Supervisor approval Over €1,000 Finance approval ![A refund scale in three blue tiers (under €100 executes, €100–€1,000 supervisor approval, over €1,000 finance approval) with the €729 refund marked in red, above two flows: a rule in the system prompt (dashed) versus a policy check in the request path before the refund service.](/app/uploads/2026/10/fig-agentic-ai-control-plane-ex-cp-policy.png) Put the refund limit outside the model. Paulo Cavallo, in [a paper published in April 2026](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6592238), gives the distinction a useful pair of names. **Described governance** is what the policy documents say. **Established governance** is what the architecture actually enforces at runtime. Frameworks describing what firms should do have multiplied; the tooling that enforces any of it while the agent is running has not kept pace. A refund limit in a system prompt is described governance. The gap between the two is where the money goes. OWASP, the open security community, began closing that gap in September 2026 with an [Agent Control Standard](https://genai.owasp.org/resource/agent-control-standard-acs/). It specifies how agent platforms should expose middleware hooks so that safety policies become “declarative controls that are portable across agent frameworks and enforced at runtime.” The ambition matters more than the current maturity. An independent body is now trying to standardise the control interface itself, rather than each vendor shipping its own. Vendors differ on where the hook belongs. [Fiddler](https://www.fiddler.ai/guides/ai-agent-control-plane-architecture) puts enforcement “at the agent gateway, the one point in the request path that can hold, redact, or block a call.” [OpenHands](https://www.openhands.dev/blog/agent-control-plane), which builds coding agents, consolidates “LLM routing policies, MCP access, secrets access, budgets, user management and auth” into one platform, covering both the language model and the Model Context Protocol that connects agents to tools. The insertion points differ. The principle does not: the check happens before the call reaches the production system. ## An approval must name the act [#](#an-approval-must-name-the-act) A typical architecture diagram contains the same box. Agent proposes a risky action, arrow to human approval, arrow to execution. The box hides the important question of what, precisely, the human approves. If the answer is “let the agent continue,” the approval is close to worthless. The agent can carry on reasoning and execute something else. The approval must bind to a concrete, frozen proposal: Field Value Action refund Order 483922 Amount €729 Reason shipment lost Once approved, that intent should be immutable. If the agent later wants €900, it needs a new approval. This is why “human in the loop” is a data-model problem before it is a user-interface problem. The system needs a durable record for each proposed action, which moves through the states proposed, approved, executed or rejected, and which names whoever moved it between them. ## The retry that paid twice [#](#the-retry-that-paid-twice) Say the refund executes, but the network drops the response. The agent sees a timeout. It does what any well-built client does. It retries. The customer receives €1,458. Engineers who have run payment systems will recognise this as a distributed-systems problem, and it is. Idempotency keys, durable execution state, and compensation logic are decades old. What is new is who issues the retry. An agent decides at runtime whether to try again, with what arguments, and it may not know that the first attempt succeeded. Once an agent can cause side effects, a retry is a business decision, and the control layer must classify actions by their side effects. A genuinely read-only call can usually be retried. A refund must not be. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Reconstructing €63,000 [#](#reconstructing-63-000) Three weeks later the finance director asks why agents issued €63,000 in refunds last week. The API log says `POST /refund → 200`, 340 times. The finance director already knew that much. Each refund needs a record of six things: the agent and its version, the person it acted for, the model version, the inputs it saw, the tools it called in order and the policy rule that let it through. And the record must also show whether a human approved the action and what that person was shown, whether the call was retried and what finally happened. Fiddler's second control problem is “observability (what happened, and whether it was any good).” NIST reaches the same requirement through a legal word, non-repudiation: the record must be good enough that no party can plausibly deny what the agent did on their behalf. The artefact is a trace that links a business outcome back to a decision, a policy and an identity. Observability reveals that an agent did something wrong. Control stops the action before it happens, and a trace, however rich, does neither on its own. A firm with excellent dashboards and no enforcement point will merely be able to explain its losses very precisely. ## The nine meanings of stop [#](#the-nine-meanings-of-stop) The refunds look excessive. Someone says: “Stop the agent.” The instruction sounds simple, but technically it has at least nine readings: 1. Stop assigning new tickets. 2. Kill runs in progress. 3. Revoke credentials. 4. Block all tool calls. 5. Block only refunds. 6. Allow reads to the CRM but deny writes. 7. Cancel queued jobs. 8. Halt sub-agents the main agent spawned. 9. Prevent new workflows from starting. And setting `agent.status = stopped` in a database does none of these unless something on the request path checks that field before every call. A kill switch is only as real as its enforcement point. That is the same enforcement point that checks policy, binds approvals and blocks the duplicate refund. Fiddler argues the controls belong at a single point in the request path, since they all interrogate the same call. Others disagree: Forrester treats its planes as functional rather than as strict layers, and the IETF draft assumes identity checks may be spread across several systems. Britain's [National Cyber Security Centre](https://www.ncsc.gov.uk/blogs/thinking-carefully-before-adopting-agentic-ai) states the test more sharply than any vendor would: “If you cannot understand, monitor or contain an agent's actions, it is not ready for deployment.” Containment is a capability a firm either has before the incident or improvises during it. ## The plane appears [#](#the-plane-appears) Line up the checks the refund demanded and the shape is clear. An agent proposes an action, and seven questions stand between the proposal and the production system: * Identity — who is acting, and for whom * Authorisation — what they may do * Risk — whether this action is dangerous * Approval — whether a human must confirm it * Execution — how it runs, and whether a retry is safe * Audit — how it is reconstructed later * Enforcement — how the next one is stopped Whatever performs those checks, whether concentrated in one component or spread across several, is the control plane. Forrester's definition is the most careful: “a common enterprise governance and control platform that sits above and across a heterogeneous estate of AI agents and agentic skills,” applying one envelope of oversight regardless of who built each agent. [IBM](https://www.ibm.com/think/topics/agent-control-plane), more plainly, calls it an intermediary that “routes requests, enforces permissions and applies policies before actions are run.” Fiddler compresses it best: “the part of the system that decides, kept separate from the part that moves.” The model reasons. The tools move. The control plane decides whether the movement is permitted. Governments have converged on the same list without the vocabulary. At the end of April 2026, six cyber-security agencies from America, Britain, Australia, Canada and New Zealand issued their [first joint guidance on agentic AI](https://www.cisa.gov/resources-tools/resources/careful-adoption-agentic-ai-services). It sorts the dangers into five categories: privilege, design and configuration, behavioural, structural and accountability. Those map almost exactly onto the questions the refund raised. Privilege is the identity chain. Behavioural is the retry. Accountability is the audit trail. Agencies with nothing to sell arrived at the architecture by cataloguing what goes wrong. That framing also explains the vendor noise. An identity vendor sees the “who.” A gateway vendor sees the “may.” An observability vendor sees the “reconstruct.” A workload-automation vendor sees its scheduler with a control plane bolted on. Each describes one face of the layer. Forrester's own caution is the corrective: the architecture is real, but “the standards stack underneath it is incomplete,” and the “connective tissue between the planes” that would let a governance decision in one product propagate reliably into another “remains underdeveloped.” The message being that a firm buying a control plane today is adopting one vendor's architecture and should expect to revise it as the standards mature. ## The same shape, different verbs [#](#the-same-shape-different-verbs) The refund is one instance. Ask a procurement agent to “order 40 laptops for the new Warsaw team” and the same questions return. Its authority has to be bounded before it runs: a spending ceiling, a list of approved vendors, a rule on payment terms, and a rule on what employee data may leave the company. The two orders differ by one zero. The authority they require differs entirely. Firms already know how to delegate purchasing authority to a human. Delegation to software is the same policy with a stricter enforcement point. Give a site-reliability agent access to Kubernetes, AWS and the CI pipeline and it will, during an incident, diagnose latency, restart workloads, scale replicas and roll back deployments. Now ask what happens if the diagnosis is wrong, if two agents respond to the same alert, or if a human operator is changing the same system at the same time. The business verbs differ: `refund_customer()`, `create_purchase_order()`, `scale_cluster()`. The control questions do not. ## Not every agent needs one [#](#not-every-agent-needs-one) The need for a control plane grows with scale, autonomy and side effects. A firm that builds one before it has an agent that can act is buying insurance against a risk it does not yet carry. Environment Need One experimental agent Low Agents that only generate content Low to medium Agents that read internal APIs Medium Agents that modify business systems High Many agents across teams and frameworks High Regulated or sensitive environments Very high ![Six environments rated from low to very high need for a control plane, teal for agents that suggest or read and violet for agents that write, split by a red line at the move from reading to writing.](/app/uploads/2026/10/fig-agentic-ai-control-plane-ex-cp-need.png) The need for a control plane starts when the agent writes. The line to watch is the one between reading and writing. The moment an agent's output stops being a suggestion and becomes a call to a production API, the organisation has hired an operator. You would not give a new employee an unlimited company card, root access to AWS and authority to sign contracts, with nothing more than a note asking them not to misuse any of it. Yet privilege is the first risk category the Five Eyes agencies list, and the pattern they describe is almost exactly that: a handful of API keys and a system prompt listing things not to do. This is also where the work of production goes. Writing the agent is the smaller task. Most of the effort at Vstorm goes into making sure our agents survive contact with real customers and real infrastructure: guardrails that bound its authority, validation rules that check each proposal and observability that shows what it did and why. The result joins the agent's judgment to the determinism that engineers have relied on for decades. That combination, rather than the agent alone, is what Vstorm delivers to its clients. Agents can act. The open question is whether the organisation stays in charge when they do. The vendors will keep arguing about where the control plane sits. The refund does not care. It only needs something that can say no before the money moves. At Vstorm we help clients draw that line: deciding which actions need an enforcement point at all, and designing the control plane around what their agents are actually allowed to do. ## Sources [#](#sources) ### Independent research and standards * The Agentic Control Plane Solutions Landscape, Q2 2026 — Forrester, June 24th 2026. Category landscape covering 34 vendors. Subscription required. * Reference Architecture Brief: Multi-Agent Platform (Outer Architecture) — Steve Deng and Gary Olliffe, Gartner, May 2026. Subscription required. * Agent Control Standard (ACS) — OWASP Gen AI Security Project, September 2026. * Accelerating the Adoption of Software and Artificial Intelligence Agent Identity and Authorization — NIST, February 2026. Concept paper. * Careful Adoption of Agentic AI Services — CISA, NSA, ASD ACSC, CCCS, NCSC-NZ and NCSC-UK, April 2026. First joint Five Eyes guidance on agentic AI. * Thinking carefully before adopting agentic AI — Martin R and Kate S, UK National Cyber Security Centre, May 2026. * Described vs. Established Governance in Agentic AI — Paulo Cavallo, April 2026. * AI Agent Authentication and Authorization — Internet-Draft, July 2026. An individual submission, not an adopted IETF standard. ### Vendor material, cited as competing implementations * Microsoft Agent 365: the control plane for AI agents — Microsoft, November 18th 2025. Product announcement. * The control plane for AI agents: the architecture behind the hype — Fiddler. AI observability. * The software agent control plane — OpenHands, April 2026. Coding agents. * What is an agent control plane? — Matthew Finio and Amanda Downie, IBM Think. ![Karol Piekarski](https://vstorm.co/app/uploads/2025/09/profile_picture.jpeg) [Karol Piekarski](/author/karol-piekarski/) PhD, Agentic AI & Data Engineer [Author profile](/author/karol-piekarski/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. 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Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI doctor notes generators: ambient scribes beyond transcription URL: https://vstorm.co/agentic-ai/agentic-ai-doctor-notes-generators What an agentic AI doctor notes generator does beyond transcription, and the security, accuracy and compliance problems of building one for healthcare. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI doctor notes generators: ambient scribes beyond transcription [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI doctor notes generators: ambient scribes beyond transcription What an agentic AI doctor notes generator does beyond transcription, and the security, accuracy and compliance problems of building one for healthcare. ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer September 29, 2026 6 min read ![Agentic AI doctor notes generators: ambient scribes beyond transcription](/app/uploads/2026/10/cover-agentic-ai-doctor-notes-generators-v3.png) On this page0% 1. [From human scribes to ambient notetaking](#from-human-scribes-to-ambient-notetaking) 2. [What ambient notetaking in healthcare is](#what-ambient-notetaking-in-healthcare-is) 3. [Key challenges in building agentic ambient notetaking for healthcare](#key-challenges-in-building-agentic-ambient-notetaking-for-he) 4. [Why off-the-shelf is not enough](#why-off-the-shelf-is-not-enough) 5. [FAQ](#faq) 6. [Sources](#sources) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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A physician spends about 16 minutes in the electronic health record (EHR) for every patient visit, according to [a study in the Annals of Internal Medicine](https://www.acpjournals.org/doi/10.7326/M18-3684). Roughly a quarter of that, about 4 minutes, goes on writing documentation; the rest is reviewing the patient's history and entering orders and recommendations. ![Sixteen one-minute blocks: four blue for writing documentation, twelve stone for reviewing history and entering orders; below, the $380,415 in tuition and fees behind a typical US medical graduate.](/app/uploads/2026/10/fig-agentic-ai-doctor-notes-xp-dn-time.png) About 4 of 16 EHR minutes per visit go on notes. This is repetitive work that keeps a specialist away from the core of the job, and the patient spends much of the visit watching the doctor type. It is also expensive. A typical US medical graduate has paid $380,415 in tuition and fees, including the bachelor's degree before medical school, according to the [Education Data Initiative](https://educationdata.org/average-cost-of-medical-school). Spending that training on paperwork is a poor use of it. ## From human scribes to ambient notetaking [#](#from-human-scribes-to-ambient-notetaking) The usual answer has been to move paperwork elsewhere. Nurses and registration staff complete triage and intake so the patient reaches the doctor with most of the record already filled in. Some clinics added human scribes who sit in on the visit, but that means extra staff for every doctor, and the cost adds up. Notes taken during the examination itself mostly stayed with the doctor. Agentic AI closes that gap. These tools take notes during the encounter, so the doctor does not have to stop and type. The category goes by several overlapping names, medical transcription software, AI medical dictation software or simply dictation for doctors, but the mechanism is the same: audio in, structured clinical notes out. ## What ambient notetaking in healthcare is [#](#what-ambient-notetaking-in-healthcare-is) Ambient notetaking combines ambient listening with transcription. The system picks up the natural conversation between doctor and patient, with no dictation prompts and no pauses to talk to the computer, and turns it into usable [clinical documentation](https://vstorm.co/automation/what-is-ambient-clinical-documentation/). That makes it a form of AI clinical documentation software, and "AI scribe for doctors" has become the shorthand many vendors and clinicians use for it. Such a system usually has three parts: speech-to-text, a summarization engine and a component that fills in the documents. Together they take over the work that pulls the doctor's attention away from the patient. ![Pipeline from visit to speech-to-text, summarization, record filling and the EHR, with an agentic step pulling earlier records back into the summary.](/app/uploads/2026/10/fig-agentic-ai-doctor-notes-xp-dn-arch.png) Audio in, structured notes out, written into the EHR. The hardest engineering problem is speaker diarization: telling the speakers apart and attributing every word to the right person. Beyond transcription, agentic setups go further. They can pull earlier records for context, or check a transcribed dosage against the EHR before writing it down. An ambient clinical documentation system should support the doctor with three things: * Recording the visit. The system records the whole visit in the background, so the doctor does not touch the computer while talking to the patient. * Generating a summary. The visit is condensed into structured notes a colleague can read in seconds, the layer most people mean by "AI for clinical notes". * Filling in the record. The system is connected to the EHR and writes the notes into it, and the doctor only verifies them. This is a doctor notes generator in the full sense: it populates the record instead of only transcribing. Seen this way, ambient notetaking sits between two older categories: dictation for doctors, which has existed for decades, and AI for medical documentation, the newer layer that adds structure, summaries and EHR write-back on top of raw speech-to-text. ## Key challenges in building agentic ambient notetaking for healthcare [#](#key-challenges-in-building-agentic-ambient-notetaking-for-he) Healthcare is regulated, so these systems face a higher bar than software in most other settings. ### Security The system handles patients' health and personal data, which makes security the first priority. A leak means fines and regulatory liability, and it can also cause serious harm to the patients themselves. ### Accuracy A clinical record leaves little room for ambiguity. If the system confuses a condition or a recommendation, the error can carry into treatment, and in the worst cases the patient bears the consequences. That is why accuracy benchmarks matter when evaluating an AI clinical documentation tool: a scribe that mishears a dosage is not a minor bug. ### Hallucinations Hallucinations are the clearest example of the accuracy problem: the transcription contains sentences nobody said. [A study of OpenAI's Whisper](https://arxiv.org/abs/2402.08021), the speech-to-text model behind several medical scribes, found hallucinated content in about 1% of transcriptions, and 38% of those hallucinations contained explicit harms, such as invented medications. ![A grid of 1,000 dots with 10 marked as hallucinated and 4 of those filled as containing explicit harms; a side panel restates about 1% hallucinated and 38% of those with harms, as 10 squares with 4 filled.](/app/uploads/2026/10/fig-agentic-ai-doctor-notes-xp-dn-whisper.png) Whisper invented text in about 1 in 100 transcriptions; 38% of those contained harms. ### Integration The notetaker has to fit into the systems the clinic already runs. That means EHR integration, compliance, database connections and, in some cases, pulling relevant information from existing records. ### Independence Healthcare systems have to hold up against attackers and outages. Depending on the setting, that can mean running internally hosted [small language models](https://vstorm.co/agentic-ai/the-future-of-small-language-models-in-the-middle-market/) and processing the incoming data on infrastructure the organisation owns, so the system stays under its control. ### Compliance Regulations differ from region to region. Most require informed consent and clear information about where and how the recorded data is stored and processed. Depending on the personal and medical data rules that apply, such as GDPR or HIPAA, storing transcripts in the cloud or using SaaS tools can be restricted or come with extra conditions. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Why off-the-shelf is not enough [#](#why-off-the-shelf-is-not-enough) Ambient notetaking is available as SaaS, but an off-the-shelf product is rarely tuned to the doctors who use it. In healthcare this shows quickly: terminology is specific and workflows differ widely between specialties. Medicine is also deeply specialised, and general-purpose knowledge often does not help. A large general-purpose language model used out of the box can be more than a workflow needs when what it needs is precision in a narrow clinical vocabulary. ## FAQ [#](#faq) ### What is the difference between medical transcription software and an AI scribe for doctors? Traditional medical transcription software converts speech to text, often after the visit and sometimes with a human transcriptionist in the loop. An AI scribe for doctors listens during the encounter, structures the conversation into a clinical note and, in more advanced setups, writes that note into the EHR without a separate transcription step. ### Is ambient clinical documentation the same as dictation for doctors? No. Dictation usually requires the physician to speak the notes aloud, often after the patient has left the room. Ambient clinical documentation listens passively to the conversation during the visit, which lets the doctor stay focused on the patient instead of narrating notes. ### Can AI for clinical notes be used for therapy notes as well? The same listening and summarization approach can apply to therapy and behavioural health sessions, but the requirements differ. Therapy notes need to capture more nuance about mood, risk indicators and progress over time, and the privacy bar for behavioural health data is often higher than for general medical records. A system built for general physician visits should not be assumed to work for therapy documentation without an evaluation against those needs. ### What should a practice look for in an AI medical scribe? There is no single best tool independent of context. The right choice depends on EHR compatibility, specialty-specific vocabulary, data residency and security requirements, and whether the practice needs a general-purpose tool or one tailored to a narrow clinical domain. A vendor-neutral evaluation answers this better than a ranked list. ### Does an AI medical dictation system replace the doctor's clinical judgement? No. These systems are documentation tools. They capture, structure and file the conversation, while diagnoses, decisions and recommendations still come from the physician. Their job is to remove the paperwork, not to make medical decisions. ## Sources [#](#sources) Physician time and cost * Annals of Internal Medicine, Physician Time Spent Using the Electronic Health Record During Outpatient Encounters (16 minutes in the EHR per encounter, about a quarter of it on documentation), 2020 * Education Data Initiative, Average Cost of Medical School ($380,415 in tuition and fees including undergraduate study), 2026 Transcription accuracy * Koenecke et al., Careless Whisper: Speech-to-Text Hallucination Harms (about 1% of transcriptions hallucinated, 38% of those with explicit harms), arXiv 2402.08021, 2024 ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer [Author profile](/author/konrad-budek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Why agentic AI transformation strategy decides who wins](/app/uploads/2026/10/cover-why-agentic-ai-transformation-strategy-decides-who-wins-v3.png) ### Why agentic AI transformation strategy decides who wins Read article ](/agentic-ai/why-agentic-ai-transformation-strategy-decides-who-wins/)[ ![Prompt injection is not the vulnerability. Your agent’s tools are.](/app/uploads/2026/10/cover-ai-agent-security-prompt-injection-v3.png) ### Prompt injection is not the vulnerability. Your agent’s tools are. Read article ](/agentic-ai/ai-agent-security-prompt-injection/)[ ![Agentic tokenization: how AI agents will pay](/app/uploads/2026/10/cover-agentic-tokenization-how-ai-agents-will-pay-v3.png) ### Agentic tokenization: how AI agents will pay Read article ](/agentic-ai/agentic-tokenization-how-ai-agents-will-pay/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI Engineering Consultancy vs General Custom Software Developer: Pricing and Service Comparison URL: https://vstorm.co/agentic-ai/agentic-ai-engineering-consultancy-vs-general-custom-software-developer-pricing-and-service-comparison-2025 Agentic AI Engineering Consultancy vs General Custom Software Developer pricing and services — explore key differences now [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI Engineering Consultancy vs General Custom Software Developer: Pricing and Service Comparison [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI Engineering Consultancy vs General Custom Software Developer: Pricing and Service Comparison Agentic AI Engineering Consultancy vs General Custom Software Developer pricing and services — explore key differences now ![Antoni Kozelski](https://vstorm.co/app/uploads/2026/09/antoni-kozelski-2026.jpg) [Antoni Kozelski](/author/antoni-kozelski/) CEO & Co-founder October 22, 2025 Updated May 9, 2026 10 min read ![Agentic AI Engineering Consultancy vs General Custom Software Developer: Pricing and Service Comparison](/app/uploads/2025/10/scaled-2-2.png) On this page0% 1. [Why your business needs tailored Agentic AI](#why-your-business-needs-tailored-agentic-ai) 2. [How design approach influences ROI](#how-design-approach-influences-roi) 3. [Specialized engineering consultancies dominate complex AI transformations](#specialized-engineering-consultancies-dominate-complex-ai-tr) 4. [General software providers perform well in platform integration](#general-software-providers-perform-well-in-platform-integrat) 5. [How to select the best provider for your business needs](#how-to-select-the-best-provider-for-your-business-needs) 6. [Choose specialized engineering consultancies when:](#choose-specialized-engineering-consultancies-when) 7. [Choose general software providers when:](#choose-general-software-providers-when) 8. [Summary of strategic recommendations for AI success](#summary-of-strategic-recommendations-for-ai-success) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. 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Many companies launch ambitious AI projects with high expectations, treating them like ordinary software or apps. Mistaking vision for strategy, they pick processes, set bold targets, and fund them. Yet when implementation begins, they discover their "strategy" was disconnected from the technological reality. > I do think of it as a workforce. This is a workforce that will conduct end-to-end processes, replacing many tasks being performed today by the human workforce. * [Jorge Amar, McKinsey Senior Partner](https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-future-of-work-is-agentic), June 3 2025, on [The future of work is agentic](https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-future-of-work-is-agentic) According to market estimates [presented by Rand](https://www.rand.org/pubs/research_reports/RRA2680-1.html), more than 80% of AI projects fail. That is double the rate of failure for information technology projects that do not involve AI. But having the right development partner on board from the beginning who can provide tailored AI agents with a custom fit approach can yield production-grade agents for your core workflows, greatly enhancing efficiency while providing significant gains in ROI. According to [Deloitte predictions for 2025](https://www.deloitte.com/us/en/about/press-room/deloitte-technology-media-telecom-2025-predictions.html), 25% of enterprises using GenAI are forecast to deploy AI Agents by 2025, growing to 50% by 2027. The wide adoption of agentic AI solutions to fill the expectation gap of AI implementations across industries is ever more clear. But how do you choose the right agentic AI development partner to best fit your business needs? Below you will find a direct comparison of dedicated engineering consultancy firms which provide both AI consultation and development versus general software development companies with wide skill sets across many disciplines and technologies. ## Why your business needs tailored Agentic AI [#](#why-your-business-needs-tailored-agentic-ai) The numbers are quite clear. With over 80% of attempted AI implementations failing outright, 87% of AI related projects never reaching production ( [MIT Sloan Review](https://sloanreview.mit.edu/article/achieving-return-on-ai-projects/)), and 42% of companies choosing to cut their losses and abandon AI initiatives before delivery ( [Fortune](https://fortune.com/2025/08/21/an-mit-report-that-95-of-ai-pilots-fail-spooked-investors-but-the-reason-why-those-pilots-failed-is-what-should-make-the-c-suite-anxious/)), there is a significant gap in common visions of AI’s potential applications and the technical reality which governs application. But where is this wide gap between vision and outcomes originating from? Taking a deeper look at the numbers provided by [MIT](https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf), we can see that specialized engineering consultancies dramatically outperform general software solution providers in AI implementations, achieving an industry average of 67% successful implementation rates compared to the estimated 22% success rate of general providers. The most common reasons for project failure and abandonment include problem misalignment, insufficient data quality, technology-first approaches over solving user’s problems, poor integration with existing processes, and inadequate human oversight in development processes. Well publicized failures like the McDonald's AI drive-thru shutdown, IBM Watson Health's $4 billion discontinuation, and Zillow's $500+ million in losses show the full extent of potential misalignment of vision and technology. The best solution to overcome these pitfalls is to embed a dedicated Agentic AI squad that engineers and deploys production-grade agents for your core workflows right from the start. Vstorm leverages practiced and proven tactics to narrow the gap and achieve meaningful results. Our strategy begins with two locked blueprints. First comes the business blueprint, which ranks high-value use cases; and then follows the technical blueprint, which tests each potential use-case for feasibility, measuring complexity, data, integration, and compliance needs alongside setting realistic timelines, while determining required tools and necessary up-skilling. This blueprint allows us to build tailored AI agents that seamlessly integrate with your existing workflows, data, and software stack. ## How design approach influences ROI [#](#how-design-approach-influences-roi) When defining needs and choosing a provider, one must consider the exchange between overall cost and desired performance. The right solution in the hands of a company prepared for the agentic AI transformation can provide revolutionary opportunities for greater scalability; while generally cheap and quick to implement, off-the-shelf tools tend to cap out quickly, and enterprise-grade platforms and big-consultancy fees tend to break the budget. Agentic AI represents an emerging field, with 62% of organizations expecting 100%+ ROI from planned implementations ( [IBM](https://newsroom.ibm.com/2024-12-19-IBM-Study-More-Companies-Turning-to-Open-Source-AI-Tools-to-Unlock-ROI)). Specialized AI agent engineering consultancies are capable of delivering tailored multi-agent orchestration systems with complex agent ecosystems, custom architectures using advanced reasoning capabilities, and cross-functional integration across business processes. Such providers maintain a focus on custom model development, training domain-specific models on proprietary data with sophisticated RAG systems and multi-modal integration. Agentic AI solutions can execute complex workflows from beginning to end, referencing and utilizing cross departmental data sources of various data types, as well as handle interactions with an array of different user types. Due to their complexity and custom tailored specifications, applying these solutions tend to take more time and greater up front investment, particularly when cooperating with the big names among engineering and consultancy providers. Meanwhile, general providers take a fundamentally different approach. Their primary focus is to connect third-party AI models (like GPT-5 or Claude) to existing platforms through standardized APIs. Solutions like Salesforce Agentforce and Microsoft Copilot exemplify this model, with general custom software developers typically deploying template-based applications and leasing platform-native features rather than engineering deep integrations into company-specific workflows. These solutions tend to work best when dealing with simple use cases requiring optimization which deal with only one type of data, integrate with a single internal system, and are expected to perform structured, repetitive tasks. So while these systems are cheaper and quicker to implement, their dependence on finding just the right use case leaves achieving any meaningful returns largely to chance. This is further compounded by the fact that general providers typically deliver solutions on request, without any internal exploration of the potential viability of suggested use-cases prior to implementation. Agentic AI engineering companies like Vstorm fill the gap. As we are capable of providing SMB-friendly pricing designed to turn cash positive within months, allowing mid-market competitors to get enterprise-grade AI without the enterprise costs while maintaining complete ownership of your code and data with zero lock-in contracts. Vstorm follows the TriStorm development approach \[summary of approach and its value here\]. Having gone over the unique approaches of these providers lets take a closer look into what sort of use case these providers can best meet. No company is one size fits all, and there is no need to pay top dollar for a comprehensive agentic transformation when a simple out-of-the-box solution will do. ## Specialized engineering consultancies dominate complex AI transformations [#](#specialized-engineering-consultancies-dominate-complex-ai-tr) Specialized engineering consultancies focus exclusively on custom AI strategy development, proprietary algorithm creation, and comprehensive business transformation to dramatically scale operations. These firms, such as Vstorm, often employ PhD-level data scientists, Agentic AI engineers, and domain specific experts who develop bespoke solutions using advanced architectures like multi-agent systems, RAG pipelines, on-premise or cloud-based, and open-source LLM deployments. A few of the top benefits of partnering with specialized engineering and consultancy firms include: * Custom model development with domain-specific training data * Advanced RAG systems with vector databases * Multi-modal integration combining text, image, and structured data * Context-adaptive systems engineered to retain feedback and evolve with organizational workflows through continuous refinement—the #1 feature demanded by 66% of executives * Workflow-embedded solutions starting at high-value pain points before scaling to core processes, avoiding the 95% failure rate of generic implementations * Complex back-office integration across multiple legacy and native systems (that generic tools cannot handle), delivering measurable financial returns [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## General software providers perform well in platform integration [#](#general-software-providers-perform-well-in-platform-integrat) General software solution providers typically embed out-of-the-box AI solutions into existing enterprise systems, like ERP, CRM, and business applications. Microsoft, SAP, Oracle, and IBM are providers in this way, leveraging existing client relationships and infrastructure to deploy standardized AI modules through pre-built templates and API integrations. Their delivery model prioritizes rapid deployment using the above established enterprise software methodologies. General software solution providers often achieve best results through: * Platform-embedded AI with simplified governance through existing controls * Template-based LLM deployment for common business functions * Standardized high-level agent frameworks operating within platform boundaries * Subscription-based pricing with AI features included in licensing tiers * 80% faster implementation timelines than custom solutions ## How to select the best provider for your business needs [#](#how-to-select-the-best-provider-for-your-business-needs) Quantitative analysis of 1,000+ enterprise implementations, provided by MIT, reveals dramatic performance disparities between provider types. The MIT research shows specialized vendor partnerships succeed 67% of the time, while internal builds and general provider approaches succeed only 33% as often (setting final success rates at around 22%). The research further points out that the industry is facing significant systemic challenges with 95% of generative AI pilots failing to produce any impact on operations, while McKinsey data claims that nearly 80% of companies have deployed GenAI but report no material impact on earnings. And while the numbers paint a fairly clear picture, the choice between specialized [Agentic AI providers](/agentic-ai/deep-dive-top-10-ai-agent-development-firms-for-business-automation/) and general AI solution providers should align with organizational objectives, AI maturity, and complexity requirements. Organizations seeking to utilize the full potential of AI and gain revolutionary outcomes from complex internal systems should partner with specialized consultancies, but general providers can still achieve results if employed wisely. Below we present a breakdown of the top aspects you should consider when choosing your provider. ## Choose specialized engineering consultancies when: [#](#choose-specialized-engineering-consultancies-when) * Complex AI transformations require custom solutions to link multiple internal data systems and domains * Cutting-edge requirements demand latest AI research and practiced solutions * AI is intended to be a core competitive differentiator rather than a simple operational enhancement * Highly regulated industries require custom governance frameworks to meet compliance requirements * Innovation focus prioritizes breakthrough capabilities to dramatically increase operational scale ## Choose general software providers when: [#](#choose-general-software-providers-when) * Organizations are heavily invested in specific enterprise platforms requiring seamless integration * Risk mitigation favors supported out-of-the-box solutions over integrated and owned approaches * The use case is simple and repetitive, operating in a limited environment on one data type * Budget constraints require cost-effective, standardized solutions * Rapid deployment is a critical success factor Specialized consultancies focus on custom model development, training domain-specific models on proprietary data, leveraging sophisticated RAG systems, and multi-modal integration to achieve superior results. These implementations typically require 6-18 months and cost $100,000-$500,000 on average but achieve significantly higher accuracy and domain relevance. While general software providers emphasize API integration with third-party models like GPT-5 and Claude through standardized APIs, deploying template-based applications and platform-native features. These implementations can sometimes be completed as quickly as 6-12 weeks and cost $50,000-$150,000 on average but far less frequently manage to achieve any significant financial impact. Boutique consultancies bridge these extremes, offering custom-engineered solutions with more agile processes and flexible pricing. These firms typically focus on mid-market companies, delivering purpose-built agentic AI systems in 2-4 months for between $50,000-$250,000, combining the deep integration capabilities of larger consultancies with faster deployment and lower overhead. Pricing and Service Comparison General Custom Software Provider Agentic AI Engineering Consultancy Vstorm Service Offer API template based integration of third-party models Domain specific custom model development Tailored agentic AI with agile development and flexible scope Time to Delivery 1.5-3 months 6-18 months 2-4 months Average Cost $50,000-$150,000 $100,000-$500,000 $50,000-$250,000 An observable trend on the market also suggests that the hybrid approach seems to often deliver desirable outcomes, with large companies often allocating 60-70% of complex, high-value transformations to specialists while leveraging general providers for standardized, platform-integrated AI implementations and off-the-shelf solutions, where applicable. In fact, tinkering with various solutions in low risk, low impact settings can provide companies with the internal knowledge required to properly leverage more advanced and lucrative AI transformations, as identified by Lucian Puca, Digital Product Manager and Automation and Workflow Lead of Mixam, in his [top 5 tips for launching the Agentic AI transformation](/ai/top-5-tips-from-lucian-puca-of-mixam-on-launching-agentic-ai-transformation/). ## Summary of strategic recommendations for AI success [#](#summary-of-strategic-recommendations-for-ai-success) Success in AI implementation requires strategic vendor selection hand-in-hand with ongoing organizational transformation. Based on the analysis of 1,000+ case studies, organizations achieve the best results by following tested patterns regardless of their choice of provider. Universal success factors include: * Start with specific, high-value use cases demonstrating clear ROI rather than broad AI initiatives * Invest heavily in data quality and governance frameworks before model development * Implement gradual scaling with continuous validation rather than big-bang deployments * Maintain human-AI collaboration instead of pursuing full automation * Focus on business outcomes and user problems over technical sophistication But to hedge your bets and get the best returns for your AI investment, the following strategies concerning providers should be considered: * **Large enterprises** with $500+ million revenue are best served engaging in hybrid models, combining Tier 1 general providers with specialized consultancies to optimize outcomes. Partner with established providers like IBM, Accenture, and Deloitte for core AI transformation, while engaging specialized boutiques, like Vstorm, in innovation projects for breakthrough applications. * **Mid-market companies and SMBs** with around $50M-$500M revenue achieve best results by partnering with specialist engineering companies like Vstorm who provide end-to-end support, from strategy to deployment. The focus should be on firms with 10-100 employees who offer specialized expertise without the bureaucratic overhead, where projects range from $25,000-$250,000 and have clear ROI expectations. * **Startups** should prioritize boutique specialists with direct startup experience, emphasizing technology transfer and internal capability building over ongoing dependencies. Budget-conscious approaches of $15,000-$50,000 to launch pilot projects enable fast iteration cycles with flexible engagement models. Vstorm is uniquely positioned to support both SMBs and enterprise level businesses achieve dynamic transformation of their internal workflows, allowing businesses to dramatically scale operations and achieve new growth by utilizing internal data and streamlining processes with sophisticated AI agents precisely tailored to business needs at low cost, no lock in. The AI consulting market's 26% annual growth and expanding sophistication create unprecedented opportunities for organizations that navigate their provider selection strategically, balancing specialized expertise with implementation pragmatism to join the successful minority achieving transformational AI value. ![Antoni Kozelski](https://vstorm.co/app/uploads/2026/09/antoni-kozelski-2026.jpg) [Antoni Kozelski](/author/antoni-kozelski/) CEO & Co-founder [Author profile](/author/antoni-kozelski/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Best AI Process Automation Use Cases for Healthcare](/app/uploads/2025/12/Group-1793.png) ### Best AI Process Automation Use Cases for Healthcare Read article ](/agentic-ai/best-ai-process-automation-use-cases-for-healthcare-2/)[ ![What is agentic AI? A simple guide for small and medium businesses](/app/uploads/2025/09/Group-1787-1-2-768x552.png) ### What is agentic AI? A simple guide for small and medium businesses Read article ](/agentic-ai/what-is-agentic-ai-a-simple-guide-for-small-and-medium-businesses/)[ ![Building Production-Grade AI Agents: How We Brought Deep Agent Patterns to Pydantic](/app/uploads/2025/12/pexels-gdtography-277628-911738-1-768x513.png) ### Building Production-Grade AI Agents: How We Brought Deep Agent Patterns to Pydantic Read article ](/agentic-ai/building-production-grade-ai-agents-how-we-brought-deep-agent-patterns-to-pydantic/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in government URL: https://vstorm.co/agentic-ai/agentic-ai-for-government Agentic AI for government agencies and public sector bodies — auditable agents for benefits review, permit processing, records requests, and compliance checks, with a full audit trail on every action. [Home](/)/[Industries](/industries/)/Agentic Ai For Government # Agentic AI in government Agents that work inside public sector casework. We build agents that read case files against statute and policy, draft sourced determinations, and log every step — with the human sign-off and audit trail a records officer or IG audit will actually accept. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Permit application casework agent Authorities Statute s.12 Regulation 4 Policy 2.3 Draft determination s.12(1) reg. 4 pol. 2.3 no clause Officer review 01 Read case 02 Cite 03 Escalate Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most government AI pilots die in legal review, not in engineering Benefits eligibility, permit review, records requests and procurement compliance are rule-bound, document-heavy decisions where being wrong is expensive. Legal and records review is where the pilots die, and technical capability is rarely the reason. What sinks them is an agent that cannot show its work: no citation back to the policy clause it applied, no log of what it read, no clean escalation path when a case falls outside the rule set. We map where an agent can draft, where it must escalate, and how every action gets logged before any integration work starts. Sources Agent Outcomes Casework agent Case documents Statute & policy Priordeterminations Human reviewer Case managementsystem Audit log every determination traces to a cited policy clause Use cases ## Where agents earn trust in public sector operations Workflows with a documented rule set and a review step already built into the process. 01 ### Benefits & permit eligibility review An agent reads the application and supporting documents, checks them against the eligibility rules, and drafts a sourced determination for a caseworker to approve. 02 ### Public records & FOIA response assembly Searches across document stores, applies redaction and exemption rules, and assembles a response packet with a log of every inclusion and exclusion. 03 ### Procurement & compliance checks Cross-references vendor submissions against procurement rules and prior awards before a contracting officer signs off — flags gaps instead of missing them. 04 ### Correspondence & inquiry triage Classifies incoming citizen correspondence, drafts a response grounded in current policy, and routes anything ambiguous to the right department. What the mechanism delivers ## The reliability bar a public body has to clear before go-live None of these figures come from a government deployment — we have not shipped one, and we will not dress up a number from another sector as public sector proof. They come from Schmitt-Thompson (healthcare), Synera (engineering software) and Mixam (print on demand), and they are here because they measure what casework depends on: staged retrieval and validation instead of a single generation pass, multi-step process orchestration, and coordination across many tools without drifting. Every figure links to the case study behind it. Agents do not issue determinations, grant benefits, or release records. They gather the evidence, apply the documented rule set, and draft what a caseworker, records officer, or contracting officer signs off on — with every step logged. Manual workflow setup With agents in production ### Manual setup per multi-step workflow 2 hrs Engineers on Synera's platform assembled each complex workflow by hand, node by node. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) ### With a text-to-workflow agent in production 3 min Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 44% → 98% ### raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark Not a government deployment — Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) 95.4% ### Success rate in workflow results A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) Client results ## Proof from production agentic deployments We have not shipped a production agent inside a government body. These are the closest available references: Schmitt-Thompson is the strongest regulated-environment case on this site (nurse-triage guidance where a wrong recommendation is a safety event), and Synera and Mixam show the same orchestration discipline at process and tool scale. [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production casework agent TriStorm keeps legal, records, and security review aligned with engineering, so a pilot survives contact with an audit. real inputs adversarial latency VALIDATING go / no-go 1 ### Map workflow and compliance We audit the target casework process, the statute or policy it runs against, and your security and records-retention boundary — ranking workflows by volume and review complexity. * Workflow & policy audit * Security boundary map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We implement against real case-file shapes, with an evaluation suite scored against your own policy documents before any draft reaches a reviewer. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout inside your existing security boundary, with audit logging and a structured handoff so your team operates the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own casework and compliance-reporting workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in government, answered All FitUse casesComplianceDeliveryOperations Where does agentic AI actually fit inside a government agency? + In workflows that are high-volume, rule-bound, and already generate a paper trail — permit and benefits eligibility review, records requests, procurement compliance checks, correspondence triage. We do not deploy agents to make final determinations on citizen-facing decisions. We deploy them to gather evidence, apply the documented rule set, and draft a recommendation that a caseworker or officer signs off on. How is this different from the chatbots agencies already tried and abandoned? + A citizen-facing chatbot answers one question against a knowledge base and stops there. An agent works the back office: it reads a case file, cross-references it against statute and policy, checks prior determinations for consistency, and produces a sourced draft with every step logged. A chatbot that could not answer off-script says nothing about whether an agent can work a case file against statute with every step logged — the two fail for different reasons. What does an agent actually do on a benefits or permit application? + It reads the submitted documents, checks them against eligibility rules and required fields, flags missing or inconsistent information, and drafts a determination with citations back to the specific policy clause. A human reviewer approves, edits, or rejects — the agent never issues the final decision unsupervised. Can agents help with public records or FOIA-style requests? + Yes. An agent can search across document stores, apply redaction and exemption rules, and assemble a response packet with a log of what was included, excluded, and why. The redaction logic and its reasoning are reviewable line by line, which is the part records officers usually cannot get from keyword search alone. How do you handle audit and records-retention requirements? + Every agent action (the input it read, the rule it applied, the output it produced, and any escalation) is logged and retained to your records schedule. We treat the audit trail as a first-class deliverable, not an afterthought bolted on before go-live. That is the same rigor bar we hold in other zero-tolerance-for-error environments: our clinical triage system for Schmitt-Thompson Clinical Content reached 98% on the open 50-scenario benchmark before production use. What happens when the agent is uncertain or the case falls outside policy? + It escalates instead of guessing. Confidence thresholds and policy-coverage gaps route the case to a human reviewer with the full reasoning chain attached, so the reviewer sees exactly why the agent stopped rather than starting the review from a blank file. Can this run on infrastructure that meets FedRAMP, state, or agency security requirements? + The agent layer is built to sit inside your existing security boundary — your cloud environment, your identity and access controls, your data residency requirements. We do not ask an agency to move data to a new platform to get agentic automation; we integrate with the systems and authorization boundary already in place. What is the typical path from a pilot to something IT and legal will actually approve? + A scoped Proof of Value on one workflow, using real (or properly de-identified) case data, typically takes a few weeks. From there, TriStorm's discover-build-deploy phases add the monitoring, audit logging, and reviewer handoff a security and compliance sign-off will require before wider rollout. Start with one workflow ## Map one casework or compliance workflow worth automating A 30-minute call identifies the security and records-retention constraints, the review points, and a realistic path to a working agent your legal and IT teams will sign off on. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI for ITSM URL: https://vstorm.co/agentic-ai/agentic-ai-for-itsm Agentic AI for ITSM — agents that triage, resolve, and route incidents and service requests grounded in your knowledge base and systems. [Home](/)/[Industries](/industries/)/Agentic Ai For Itsm # Agentic AI for ITSM Resolve tickets, do not just route them. We build agents that read incidents and service requests against your knowledge base and systems, resolving what they can and routing the rest with context attached. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) INC 2041 VPN access denied status open resolved cites runbook RB-12 Runbook: access reset verify identity check group reset token notify user 01 Read 02 Run 03 Resolve Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most ITSM automation routes the ticket and stops there Workflow rules match a ticket to a queue based on a fixed pattern. An agent reads the actual incident, cross-references your knowledge base and system state, and either resolves the standard case directly or routes the exception with the reasoning attached — handling the variation a rule set cannot. We start by mapping which ticket categories the agent can resolve directly and where a technician stays in the loop. Sources Agent Outcomes Triage &resolution agent Incident tickets Knowledge base System state Auto-resolution Technician queue Audit log every resolution traces to a documented runbook Use cases ## Where agents earn trust in IT service management Ticket categories with a documented resolution path. 01 ### Access provisioning & password resets Resolves routine identity and access requests directly against your documented policy, without a technician touching every ticket. 02 ### First-line incident triage Reads incoming incidents against your knowledge base, resolving documented cases and routing the rest with a summary attached. 03 ### Change-request documentation review Cross-checks change requests against policy and prior incidents, drafting a review summary instead of a manual read-through. 04 ### Knowledge-base grounded answers Answers employee questions from your actual runbooks and documentation — sourced, not generic advice. What the mechanism delivers ## Routing and resolution under load, measured in production None of these numbers come from an ITSM deployment — we have not shipped one yet, and we are not going to dress up a proxy as one. They come from Mixam (print on demand), Synera (engineering software) and Schmitt-Thompson (healthcare). They are here because each measures a mechanic a service desk runs on: evaluating and routing work in real time across many tools, executing a multi-step procedure on top of systems that already exist, and stopping rather than guessing when the source material runs out. Every figure links to the case study behind it. Agents do not close a major incident, approve a change, or grant standing access on their own. They read the ticket, pull system state and runbook coverage, draft the resolution or the routing decision — and log every step for the technician who signs off. Manual workflow setup With agents in production ### Manual setup per multi-step workflow 2 hrs Engineers on Synera's platform assembled each complex workflow by hand, node by node. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) ### With a text-to-workflow agent in production 3 min Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 95.4% ### Success rate in workflow results Not an ITSM deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) 44% → 98% ### raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Client results ## Proof from production agentic deployments We have not yet shipped a production agent inside an ITSM organization. These are the closest structural references we have — a multi-agent system evaluating and routing work across 15 tools, multi-step automation running on top of existing systems, and an agent that escalates instead of answering when confidence drops. [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production agent TriStorm keeps resolution accuracy and engineering aligned. real inputs adversarial latency VALIDATING go / no-go 1 ### Map ticket categories and knowledge base We audit ticket volume, resolution patterns, and knowledge-base coverage — ranking automation candidates by volume and documentation quality. * Ticket & KB audit * System integration map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We implement against real ticket data, with an evaluation suite scored for resolution accuracy before production. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout with monitoring and audit logging, plus a structured handoff so your IT team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own ticket-triage and change-management workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI for ITSM, answered All FitUse casesDeliveryOperations Where does agentic AI fit into IT service management? + In the ticket triage and resolution layer — reading an incident or request, cross-referencing it against your knowledge base and systems, and either resolving it directly or routing it with the right context attached. How is this different from the workflow rules our ITSM platform already runs? + A workflow rule routes a ticket that matches a fixed pattern. An agent reads the actual ticket content, reasons across your knowledge base and system state, and resolves what it can — handling the variation a fixed rule set cannot. What ITSM workflows are realistic first projects? + Routine service request fulfillment (access provisioning, password resets), first-line incident triage with knowledge-base grounding, and change-request documentation review. Can an agent actually resolve a ticket, not just route it? + Yes, for the categories where the resolution path is documented — an agent reads your runbooks and knowledge base and completes standard resolutions directly, escalating anything outside that scope. Can this integrate with our existing ITSM platform? + Yes, through your existing APIs — we connect to your ticketing and CMDB systems without requiring a platform migration. Do we own the system after it is built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in. Start with one workflow ## Map one ticket category worth automating A 30-minute call identifies ticket volume, knowledge-base coverage, and a realistic path to a working agent. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI for mid-market companies: a structural advantage URL: https://vstorm.co/agentic-ai/agentic-ai-for-mid-market-companies-a-structural-advantage Why mid-market companies are well placed for agentic AI, where they stall in pilots, and the incremental path from one back-office process to scale. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI for mid-market companies: a structural advantage [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI for mid-market companies: a structural advantage Why mid-market companies are well placed for agentic AI, where they stall in pilots, and the incremental path from one back-office process to scale. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst July 24, 2026 Updated July 27, 2026 6 min read ![Agentic AI for mid-market companies: a structural advantage](/app/uploads/2026/07/THE-GAP-ISNT-TECHNOLOGY.-ITS-IMPLEMENTATION.webp) On this page0% 1. [The structural advantage of mid-market companies](#the-structural-advantage-of-mid-market-companies) 2. [Where most mid-market companies stall](#where-most-mid-market-companies-stall) 3. [How these processes are handled today](#how-these-processes-are-handled-today) 4. [Where the scale opportunity lives](#where-the-scale-opportunity-lives) 5. [What the data and our experience show](#what-the-data-and-our-experience-show) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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We at Vstorm see this gap as an implementation problem, not a technology one. Mid-market companies hold a structural advantage: their processes are complex enough to justify custom systems, yet their decision-making is fast enough to reach production without committee review. This article sets out where mid-market companies stall, how these processes are handled today, and the incremental path that turns a company's biggest resource drags into workflows that compound over time. According to [Everest Group research commissioned by R Systems](https://www.businesswire.com/news/home/20260303095653/en/Over-40-of-Mid-Market-Enterprises-Leapfrog-AI-Adoption-to-Accelerate-Competitiveness-New-Report-Commissioned-by-R-Systems-and-Produced-by-Everest-Group-Finds), more than 40% of mid-market enterprises are bypassing the traditional stages of artificial intelligence adoption and moving directly toward agentic AI for mid-market companies as a strategic priority rather than an experiment. The companies that scale successfully over the next 12 months will hold a compounding advantage: [93% of business leaders surveyed by Capgemini](https://www.capgemini.com/news/press-releases/trust-and-human-ai-collaboration-set-to-define-the-next-era-of-agentic-ai-unlocking-450-billion-opportunity-by-2028/) agree on this point. And yet Everest found that only 15% of mid-market enterprises have operationalised agentic AI across functions. Our experience in the field shows that this gap is not a technology problem, but an implementation one. AI power on its own does not close it: the difficulty lies in wiring agents into the business processes that actually run the company. Mid-market companies are structurally better positioned to do this than either large enterprises or small businesses operating without the same operational complexity. We first set out this argument in [Antoni Kozelski's Forbes Technology Council column](https://www.forbes.com/councils/forbestechcouncil/2026/07/14/the-structural-advantage-mid-market-companies-hold/); this piece develops it with the practical steps we apply in the field. ## The structural advantage of mid-market companies [#](#the-structural-advantage-of-mid-market-companies) Large enterprises are navigating what [Deloitte's 2026 State of AI in the Enterprise survey](https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2026/state-of-ai-2026.pdf) identifies as the primary barrier to integration: the AI skills gap, which most organisations are addressing through education programmes rather than workflow redesign. The result is slow, internally contested transformation cycles that stretch across entire quarters and require sign-off from centres of excellence, procurement committees, and security teams. Small businesses face the opposite problem. They often lack the cross-departmental operational complexity where the hidden value of agentic AI lies, and their single-department workflows of limited volume are better served by off-the-shelf automation tools. Mid-market companies sit in the space that agentic AI workflow automation is best suited to address. Their processes are complex workflows that reach across departments and draw on multiple data sources, and they are domain-specific enough that no platform tool handles them out of the box. A goal-driven agent can reason across billing, analytics, and support records at once, provided it is integrated with the enterprise systems that hold them. This is the ground an agentic AI system is built to cover, and it is exactly the ground off-the-shelf tools cannot reach. At the same time, mid-market decision-making structures are flat enough that a C-level sponsor can move a project from roadmap to production without navigating committee review cycles. That combination, unique operational complexity paired with implementation agility, is where the greatest potential is hidden. ![Quadrant of operational complexity against decision speed: small business (ochre) fast but simple, large enterprise (violet) complex but slow, mid-market (solid blue) in the shaded fast-and-complex quadrant where custom agentic systems fit.](/app/uploads/2026/10/fig-agentic-ai-for-mid-st-mm-position.png) Mid-market pairs enterprise complexity with fast decisions. **Dimension** **Mid-market ($25M to $500M)** **Large enterprise ($500M+)** **Small business (under $25M)** **Operational complexity** Cross-departmental, multi-source, domain-specific High, but siloed across many legacy systems Mostly single-department, low volume **Fit for custom agentic systems** Strong: complex enough to justify, bounded enough to scope Justified, but slowed by scale and governance Rarely justified; off-the-shelf tools suffice **Decision speed** Flat: a C-level sponsor moves roadmap to production Committee cycles across CoE, procurement, security Fast, but limited need to move **Primary barrier** Implementation know-how and partner selection Skills gap addressed by training, not workflow redesign Insufficient complexity and infrastructure ## Where most mid-market companies stall [#](#where-most-mid-market-companies-stall) Everest Group data shows [57% of mid-market companies remain in controlled pilot phases](https://www.businesswire.com/news/home/20260303095653/en/Over-40-of-Mid-Market-Enterprises-Leapfrog-AI-Adoption-to-Accelerate-Competitiveness-New-Report-Commissioned-by-R-Systems-and-Produced-by-Everest-Group-Finds). We have encountered more than a few stuck in this gap, and the causes are consistent. ![Bars on a 0–100% axis: 40%+ moving straight to agentic AI and 57% still in pilots (light blue, ambition) above 15% operationalised across functions (red value) and 7% with agentic-specific governance (solid blue, production).](/app/uploads/2026/10/fig-agentic-ai-for-mid-st-mm-gap.png) Over 40% are moving to agentic AI; 15% run it across functions. They start with technology rather than the problem. A use case is chosen because it looks promising, not because it removes a critical bottleneck. The real return lives in back-office processes with high manual load, where gains in operational efficiency are largest but least visible in a technology-led scoping session. They treat the proof of concept as proof rather than discovery. Early deployments answer questions no strategy document can. Using them only to validate a predetermined plan skips the learning phase that makes production deployable. They allow governance to arrive late. Only 7% of mid-market enterprises have agentic-specific governance policies in place. Governance and security must be designed into the architecture from the first deployment, with human oversight and audit trails in place before the system goes live, not retrofitted afterwards. They choose the wrong implementation partner. A partner who can confirm they can build something is not the same as one who can explain exactly how, based on prior implementations in comparable environments. Fluency in the latest AI technologies matters far less than a well understood roadmap, forged in experiments and detailed scoping, which is what produces measurable performance in a critical workflow. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## How these processes are handled today [#](#how-these-processes-are-handled-today) At present, most mid-market companies manage their highest-complexity processes through manual coordination, shared spreadsheets, and siloed software tools. Much of the work consists of repetitive tasks handled by people, and much of the operational knowledge sits in individual heads rather than in shared knowledge bases. An order query that touches three departments requires three handoffs. A compliance check drawing on four systems requires four people. The process works, but at the cost of time and headcount, and it does not scale without adding more of both. This is the baseline every agentic AI business case should start from. Before automating anything, we map who performs the process today, how long it takes, and where it breaks down. That current-state map is what grounds a project in the real world rather than in a demo, and it is what makes the value of automation measurable rather than assumed. ## Where the scale opportunity lives [#](#where-the-scale-opportunity-lives) The use cases that deliver the fastest returns are not always the most obvious. Everest Group's mid-market research identifies IT operations as the most deployment-ready function, with [software engineering delivering close to 30% efficiency improvement](https://www.businesswire.com/news/home/20260303095653/en/Over-40-of-Mid-Market-Enterprises-Leapfrog-AI-Adoption-to-Accelerate-Competitiveness-New-Report-Commissioned-by-R-Systems-and-Produced-by-Everest-Group-Finds) across monitoring, requirements gathering, and testing. Customer service, finance, and accounting follow, with their structured workflows and clearly defined action boundaries. ![Four rising cards: map the current state (stone), start narrow and show measurable output (blue tint), expand scope (solid blue, next workflows), with a teal human-in-the-loop line under the agent steps.](/app/uploads/2026/10/fig-agentic-ai-for-mid-st-mm-path.png) Start narrow, prove the output, then expand. The practical path is incremental. Begin with a high-volume, well-defined back-office process, typically one built from routine tasks. Demonstrate measurable output. Use that output as the foundation to expand scope. Where a workflow still involves judgement, a human-in-the-loop review keeps a person on any step that requires human approval, so autonomy expands only as fast as trust allows. With the right use case, a workflow that once required navigating several systems by hand can be reduced to a task measured in seconds, and the saving compounds every day the system runs. This is the pattern behind our [text-to-workflow platform, which cut engineers' tedious task time to seconds](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) by letting an agent translate a plain-language request into an executed engineering workflow. ## What the data and our experience show [#](#what-the-data-and-our-experience-show) Across more than 30 agentic AI implementation projects we have delivered, the organisations that reached production shared three characteristics. They started with a clearly defined operational problem. They treated the first deployment as a discovery mechanism, not a verdict. And they chose a partner with applied experience in production-grade systems. Our [TriStorm methodology](/tristorm/) is built around exactly this sequence, moving from use-case discovery to a deployed, observable system. > "The gap between mid-market ambition and mid-market results is not a technology gap. It is an implementation gap, and it is the one thing a company cannot outsource. Use external capability to build the foundations of your transformation, and then replace key building blocks with your owned solutions." Antoni Kozelski, CEO and founder, Vstorm Companies that get ahead of the curve, matching their ambition with a partner who can supply the necessary skills, security, and governance, are best placed to turn their biggest resource drags into practical agentic AI workflows. The returns compound over the long term, and with them, a sustained competitive advantage. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Lesson 1: Your experts' time is the ultimate AI bottleneck](/app/uploads/2026/10/cover-lesson-1-your-experts-time-is-the-ultimate-ai-bottleneck-v3.png) ### Lesson 1: Your experts' time is the ultimate AI bottleneck Read article ](/agentic-ai/lesson-1-your-experts-time-is-the-ultimate-ai-bottleneck/)[ ![Artificial Intelligence in Finance: How Agentic AI Is Changing Banking, Trading, and Compliance](/app/uploads/2026/07/pexels-gdtography-277628-911738-1-768x513.jpg) ### Artificial Intelligence in Finance: How Agentic AI Is Changing Banking, Trading, and Compliance Read article ](/agentic-ai/agentic-ai-in-finance-applications-benefits-risks/)[ ![The future of Small Language Models in the middle-market](/app/uploads/2026/07/IMG_8222-768x473.jpg) ### The future of Small Language Models in the middle-market Read article ](/agentic-ai/the-future-of-small-language-models-in-the-middle-market/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI for predictive maintenance URL: https://vstorm.co/agentic-ai/agentic-ai-for-predictive-maintenance A prediction is only an alert; someone still has to act. How agentic AI closes the loop between predictive maintenance and the work order. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI for predictive maintenance [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI for predictive maintenance A prediction is only an alert; someone still has to act. How agentic AI closes the loop between predictive maintenance and the work order. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst June 23, 2026 6 min read ![Agentic AI for predictive maintenance](/app/uploads/2026/06/Group-1777-9.png) On this page0% 1. [Moving from scheduled downtime to condition-based intervention](#moving-from-scheduled-downtime-to-condition-based-interventi) 2. [How maintenance is handled today](#how-maintenance-is-handled-today) 3. [Why predictive maintenance stalls before it scales](#why-predictive-maintenance-stalls-before-it-scales) 4. [From prediction to intervention: what the agentic layer adds](#from-prediction-to-intervention-what-the-agentic-layer-adds) 5. [The integration problem is the real problem](#the-integration-problem-is-the-real-problem) 6. [How we build condition-based intervention at Vstorm](#how-we-build-condition-based-intervention-at-vstorm) 7. [Where to start: sequence by asset criticality](#where-to-start-sequence-by-asset-criticality) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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Predictive maintenance can cut machine downtime by 30 to 50% ( [McKinsey](https://www.mckinsey.com/capabilities/operations/our-insights/manufacturing-analytics-unleashes-productivity-and-profitability)), yet most efforts stall in pilot because a prediction is only an alert. Someone still has to act on it. Agentic AI closes that loop: it contextualises the anomaly, checks parts, schedules a technician, and raises the work order, with a person supervising. The hard part is integration with the CMMS, ERP, and MES. We at Vstorm build that integration as production-grade, observable systems through our TriStorm methodology. ## Moving from scheduled downtime to condition-based intervention [#](#moving-from-scheduled-downtime-to-condition-based-interventi) Unplanned downtime is still a board-level cost. At the start of 2026, stopping a production line costs more than it ever has. Siemens' _The True Cost of Downtime 2024_ report puts the annual loss for the world's 500 largest companies at roughly $1.4 trillion, equal to 11% of total revenues and up from 8% in 2019 and 2020, a 62% increase in five years ( [Siemens, The True Cost of Downtime 2024](https://assets.new.siemens.com/siemens/assets/api/uuid:1b43afb5-2d07-47f7-9eb7-893fe7d0bc59/TCOD-2024_original.pdf)). For an automotive plant, an idle line can cost up to $2.3 million per hour ( [Siemens, 2024](https://assets.new.siemens.com/siemens/assets/api/uuid:1b43afb5-2d07-47f7-9eb7-893fe7d0bc59/TCOD-2024_original.pdf)). For a mid-market manufacturer the per-hour figure is smaller, but the exposure is not. Margins are thinner, schedules carry less slack, and a handful of unplanned hours can wipe out a week of production targets. Downtime is no longer a maintenance line item. It is a board-level number that shapes capital decisions and customer commitments. ## How maintenance is handled today [#](#how-maintenance-is-handled-today) Most plants run on a mix of two strategies. Reactive maintenance fixes a machine after it breaks. Preventive, or scheduled, maintenance replaces parts at fixed intervals whether or not they are worn. Both work, up to a point, and both waste money. Reactive maintenance trades a low upfront cost for unplanned stoppages. Scheduled maintenance trades predictability for parts replaced too early and lines shut down that did not need to stop. Condition-based maintenance (CBM) changes the trigger. Instead of a calendar, the trigger is the equipment's actual state, so a machine comes offline only when its readings warrant it. This depends on continuously monitoring equipment performance: IoT sensors, together with techniques such as vibration and oil analysis, feed performance data that shows how each asset is behaving. McKinsey notes this increases the time between repairs compared with fixed-interval preventive maintenance ( [McKinsey, Establishing the right analytics-based maintenance strategy](https://www.mckinsey.com/capabilities/operations/our-insights/establishing-the-right-analytics-based-maintenance-strategy)). Done well, predictive maintenance cuts machine downtime by 30 to 50% and extends machine life by 20 to 40% ( [McKinsey, Manufacturing: Analytics unleashes productivity and profitability](https://www.mckinsey.com/capabilities/operations/our-insights/manufacturing-analytics-unleashes-productivity-and-profitability)), and over the long term it reduces maintenance costs. The prize is real. The difficulty is in reaching it. ## Why predictive maintenance stalls before it scales [#](#why-predictive-maintenance-stalls-before-it-scales) The uncomfortable truth is that most predictive-maintenance efforts never leave the pilot stage. McKinsey identifies three recurring barriers: data that is insufficient, inaccessible, or of low quality; technology that is inadequate, with too few sensors or weak IT infrastructure; and the difficulty of prioritising which assets to cover ( [McKinsey, Prediction at scale](https://www.mckinsey.com/capabilities/operations/our-insights/prediction-at-scale-how-industry-can-get-more-value-out-of-maintenance)). There is a fourth barrier that receives less attention. A prediction, on its own, is only an alert. Someone still has to read it, check the maintenance history, confirm the parts are in stock, find a qualified technician, and raise the work order. The model does the easy part. The coordination does the rest. There is also a quieter risk: a model that over-predicts can erase its own savings, as McKinsey found in one case where a 10% false-positive rate cancelled out the gains ( [McKinsey, analytics-based maintenance strategy](https://www.mckinsey.com/capabilities/operations/our-insights/establishing-the-right-analytics-based-maintenance-strategy)). This pattern is not unique to maintenance. MIT's NANDA research found that despite $30 to $40 billion invested, 95% of organisations see no measurable return from generative AI, with poor integration into existing workflows named as a central cause ( [MIT NANDA, The GenAI Divide: State of AI in Business 2025](https://www.artificialintelligence-news.com/wp-content/uploads/2025/08/ai_report_2025.pdf)). The failure is rarely the model. It is everything around it. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## From prediction to intervention: what the agentic layer adds [#](#from-prediction-to-intervention-what-the-agentic-layer-adds) The difference between predictive analytics and an agentic system is the difference between insight and execution. A predictive model forecasts equipment failures: it tells you a bearing is likely to fail. An agent does something about it. This is the core of agentic AI for predictive maintenance. In practice, an AI-powered agentic layer takes the anomaly and closes the loop. It contextualises the signal against the asset's maintenance history, checks spare part availability and cost, identifies a technician with the right skills and availability, schedules the maintenance activities into the next planned maintenance window, and generates a fully populated work order for the maintenance tasks that follow. A human stays in the loop where judgment matters, approving the action or handling the exception. This is supervised autonomy, not unsupervised automation. The agent runs the routine coordination; the team keeps control of the decisions that carry risk. > "The prediction model is the easy 10% of the problem. The value lives in the 90% that follows: reading the plant's own systems, acting inside them, and learning from what happened. That is an engineering problem, not a data science one." — Antoni Kozelski, CEO and Founder, Vstorm ## The integration problem is the real problem [#](#the-integration-problem-is-the-real-problem) The hard part of condition-based intervention is not the prediction. It is AI agent integration with existing systems: the platforms that already run the plant. A monitoring system can monitor equipment and flag a fault, but on its own it stops at the alert. Maintenance history sits in the CMMS, parts and cost in the ERP, and the production schedule in the MES. An agent that cannot read and write across all three can detect a fault but cannot act on it. Off-the-shelf platforms assume the data is clean and consistent: that a machine in the MES maps to the same asset in the CMMS and the same cost centre in the ERP. In most plants it does not. Bridging that gap is where projects either deliver or stall, and it is the work that templates do not do. The table below sets out what changes when the loop is closed. **Dimension** **Conventional predictive maintenance** **Agentic intervention** **Output** Anomaly alert for a person to review Completed, scheduled work order **Who acts** A person coordinates the response manually The agent acts; a person approves **Systems touched** Monitoring dashboard only CMMS, ERP, and MES, read and write **Human role** Read, interpret, coordinate, dispatch Supervise and handle exceptions **Common failure mode** Alert ignored or actioned too late Escalation where confidence is low ## How we build condition-based intervention at Vstorm [#](#how-we-build-condition-based-intervention-at-vstorm) We build agents that act inside existing workflows, not dashboards that wait for someone to act. We saw this directly in our text-to-workflow platform for an engineering client, where agents convert plain instructions into validated, executed workflows rather than recommendations a person has to retype ( [Vstorm case study: text-to-workflow agentic AI platform](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/)). The same principle applies to maintenance: the value is in the agent completing the task, not flagging it. Production-grade systems also need to be observable. Every agent decision should be traceable and auditable, which is why we instrument our systems from the first build rather than adding monitoring later. Our journey from a single agent to a hybrid agent-graph architecture with Pydantic AI shows how we keep complex agent behaviour transparent in production ( [Vstorm case study: hybrid agent-graph architecture](/case-study/from-single-agent-to-hybrid-agent-graph-architecture-our-journey-with-pydantic-ai-and-text-to-sql/)). This sits inside our [TriStorm methodology](/tristorm/). Transformation Consulting identifies which assets justify intervention and builds the ROI case; Agentic AI Engineering builds the closed loop and integrates it with the plant's systems. One team carries the work from roadmap to deployed system, so the strategy and the build do not drift apart. ## Where to start: sequence by asset criticality [#](#where-to-start-sequence-by-asset-criticality) The mistake is trying to instrument everything at once. The better path is to start where downtime costs the most, prove the closed loop on a bounded set of critical assets, then scale once the value is demonstrated. This mirrors how the most successful predictive-maintenance programmes are built, and it keeps the first investment small and the first result measurable. The end state is a shift in what the maintenance team does: less time spent coordinating reactive work, more time spent supervising a system that handles the operational layer, delivers operational efficiencies, and escalates only what needs a person. That is the move from scheduled downtime to condition-based intervention. Not a better alert, but a faster and more reliable response. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![From RPA to agentic AI: rebuilding the revenue cycle](/app/uploads/2026/06/Group-1777-2-2.png) ### From RPA to agentic AI: rebuilding the revenue cycle Read article ](/agentic-ai/from-rpa-to-agentic-ai-rebuilding-the-revenue-cycle/)[ ![The real cost of skipping transformation consulting](/app/uploads/2026/03/95012D22-051B-4AA2-B47D-51FC15D1C707_1_105_c-768x512.jpeg) ### The real cost of skipping transformation consulting Read article ](/agentic-ai/the-real-cost-of-skipping-transformation-consulting/)[ ![Agentic AI Transformation Consultancy vs AI modeling shop](/app/uploads/2026/06/Group-1777-1-2.png) ### Agentic AI Transformation Consultancy vs AI modeling shop Read article ](/agentic-ai/agentic-ai-transformation-consultancy-vs-ai-modeling-shop/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI for production scheduling in manufacturing URL: https://vstorm.co/agentic-ai/agentic-ai-for-production-scheduling-in-manufacturing Weekly planning meetings cannot keep pace with machine failures and order changes. How agentic AI reschedules production in between them. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI for production scheduling in manufacturing [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI for production scheduling in manufacturing Weekly planning meetings cannot keep pace with machine failures and order changes. How agentic AI reschedules production in between them. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst June 16, 2026 7 min read ![Agentic AI for production scheduling in manufacturing](/app/uploads/2026/06/Group-1777-6.png) On this page0% 1. [How production scheduling works in mid-market manufacturing today](#how-production-scheduling-works-in-mid-market-manufacturing-) 2. [The cost of planning by meeting](#the-cost-of-planning-by-meeting) 3. [What changes with agentic AI production scheduling](#what-changes-with-agentic-ai-production-scheduling) 4. [Weekly planning meeting versus continuous agentic scheduling](#weekly-planning-meeting-versus-continuous-agentic-scheduling) 5. [What this looks like in practice: a Vstorm build](#what-this-looks-like-in-practice-a-vstorm-build) 6. [How to start without replacing your planners](#how-to-start-without-replacing-your-planners) 7. [Where the planning meeting goes next](#where-the-planning-meeting-goes-next) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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Schedule adherence below 92% is common, and reactive maintenance alone can raise equipment failures by 15 to 30%. Agentic AI production scheduling maintains the plan continuously, reworking disrupted sequences in minutes and surfacing them for a planner to approve. We outline how scheduling works today, what it costs, and how to start with one production line rather than a full system replacement. In most mid-market factories, the production schedule is set in a weekly planning meeting and rebuilt by hand for the rest of the week. **Agentic AI production scheduling** replaces that reactive cycle with continuous replanning that adjusts as conditions change, while leaving final approval with your planners. This article explains how scheduling works today, what it costs, and what changes when an agent maintains the plan instead of a meeting. ## How production scheduling works in mid-market manufacturing today [#](#how-production-scheduling-works-in-mid-market-manufacturing-) We at Vstorm see the same pattern across mid-market manufacturers. The enterprise resource planning (ERP) or material requirements planning (MRP) system holds the official plan, yet the real planning happens beside it. Even in plants running mature ERP, planners do their actual planning in spreadsheets, because the system publishes a fixed plan rather than reacting to live conditions ( [production-scheduling.com](https://production-scheduling.com/)). The weekly planning meeting exists to close that gap. Production, sales, maintenance, and procurement gather to reconcile what the plan assumed against what actually happened: a late material delivery, a machine down for repair, a rush order accepted on Thursday. The meeting produces a new plan for the week ahead, which is then maintained manually until the next meeting. This is not a failure of discipline. It is the structure the tools impose. ERP was built to record transactions and run material calculations, not to replan continuously as the floor changes ( [MRPeasy](https://www.mrpeasy.com/blog/production-planning-excel/)). So **AI production planning in manufacturing** starts from a baseline that is part software, part spreadsheet, and part standing meeting. Understanding that baseline matters, because it defines what an agent has to replace and what it does not. ## The cost of planning by meeting [#](#the-cost-of-planning-by-meeting) The cost shows up in schedule adherence: the share of production orders completed on time, in the right quantity, and in the planned sequence. For discrete manufacturing, adherence above 92% is considered best in class, while anything below 75% points to systemic planning or execution problems ( [Symestic](https://www.symestic.com/en-us/what-is/schedule-adherence)). A common target band is 90 to 100%, and a figure below 90% signals delays, low equipment effectiveness, or work orders the floor cannot realistically meet ( [SCW.AI](https://scw.ai/blog/schedule-adherence/)). The reason adherence slips is rarely the plan itself. It is that the plan does not survive the week. A useful way to frame the metric is to ask whether the plan an ERP published on Monday morning survived contact with the shop floor by Friday evening ( [Symestic](https://www.symestic.com/en-us/what-is/schedule-adherence)). Disruptions compound the problem. Reactive maintenance alone can raise unexpected equipment failures by 15 to 30%, and a single breakdown ripples across multiple work centres, not only the one that stopped ( [POWERS](https://www.thepowerscompany.com/resources/maintenance-scheduling/)). Between meetings, a planner absorbs each of these events by hand, recalculating sequences and shuffling jobs in a spreadsheet. A large share of the week goes to this recalculation rather than to the judgement that requires their expertise. The work is slow, the plan ages quickly, and the next meeting starts from a position that is already out of date. The cost is not one bad week. It is a planning cadence that is structurally slower than the rate at which conditions change. ## What changes with agentic AI production scheduling [#](#what-changes-with-agentic-ai-production-scheduling) Agentic AI production scheduling changes the cadence rather than the goal. Instead of a plan rebuilt once a week and patched by hand in between, an agent maintains the schedule continuously. IBM describes agentic AI in manufacturing as autonomous systems that continuously balance constraints such as capacity, labour, and material availability, and dynamically adjust production when disruptions occur ( [IBM](https://www.ibm.com/think/topics/agentic-ai-manufacturing)). When a machine goes down or a rush order arrives, the agent reworks the affected sequence in minutes and surfaces the change for review. This is where **agentic AI workflow automation** differs from a faster spreadsheet. The agent reads from the same sources a planner consults, the ERP, the maintenance log, and the order book, and proposes a revised schedule against the same constraints a planner would weigh. The planner approves, edits, or rejects. Authority over the plan does not move to the machine; the manual recalculation does. The direction of the market reflects this shift. Gartner projects that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024 ( [IBM, citing Gartner](https://www.ibm.com/think/topics/agentic-ai-manufacturing)). The agentic AI market itself is forecast to grow from USD 7.06 billion in 2025 to USD 93.20 billion by 2032 ( [MarketsandMarkets](https://www.marketsandmarkets.com/Market-Reports/agentic-ai-market-208190735.html)). [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Weekly planning meeting versus continuous agentic scheduling [#](#weekly-planning-meeting-versus-continuous-agentic-scheduling) The contrast is clearest set side by side. The goal of both is a schedule the floor can execute; the difference is how current that schedule stays. **Dimension** **Weekly planning meeting** **Continuous agentic scheduling** **Cadence** Fixed, once per week Continuous, triggered by events **Data sources** ERP plus manual spreadsheet reconciliation ERP, maintenance log, and order book read directly **Disruption response** Absorbed by hand until the next meeting Reworked within minutes, surfaced for approval **Planner role** Rebuilds and maintains the schedule manually Reviews, approves, or overrides agent proposals **Currency of the plan** Ages between meetings Reflects current conditions **Record of decisions** Held in meeting notes and spreadsheets Logged with each scheduling change ## What this looks like in practice: a Vstorm build [#](#what-this-looks-like-in-practice-a-vstorm-build) The closest proof point in our portfolio is not a scheduling deployment, and we will be precise about that. We at Vstorm built a text-to-workflow system for Synera, an agentic AI platform for engineering automation used by manufacturers including BMW, Airbus, and Hyundai ( [Vstorm case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/)). The system lets an engineer express intent in plain language, which the agent converts into a working node-based workflow inside the platform. Over 100,000 workflows have been built on that platform by organisations including NASA and Henkel. The relevance to scheduling is the mechanism, not the domain. The build compressed expert work that was previously slow and manual: one Synera user described the agent creating a workflow that would otherwise take him "up to an hour to put together" ( [Vstorm case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/)). Synera reports its agents accelerate engineering work by up to 10 times. Production scheduling is the same shape of problem. A planner holds expert judgement about sequences, constraints, and trade-offs, and spends hours applying it by hand. An agent does not replace the judgement; it removes the manual recalculation, so the judgement is applied to a current plan rather than an ageing one. That is the pattern we build toward in manufacturing engagements. ## How to start without replacing your planners [#](#how-to-start-without-replacing-your-planners) Continuous scheduling does not begin with a system replacement. It begins with a bounded problem. We at Vstorm start every engagement with discovery through our [TriStorm framework](/tristorm/), mapping how the schedule is built today, where it breaks, and which decisions a planner would be willing to delegate to a reviewed proposal. A sensible first scope is narrow: one production line, one disruption type, one constraint set. The agent runs alongside the existing process, proposing reschedules that planners approve before anything reaches the floor. This keeps the gap between pilot and production in view. Manufacturing technology leaders report that only around 41% of AI prototypes reach production ( [SiliconANGLE](https://siliconangle.com/2026/04/14/german-startup-synera-lands-40-million-automate-engineering-workflows-ai-agents/)), and a 2026 Grant Thornton survey found that manufacturers concentrate AI in operations more than any other sector, with 62% of manufacturing leaders citing operations as the function most in need of additional AI focus, yet many cannot scale results beyond pilots ( [Grant Thornton](https://www.grantthornton.com/insights/survey-reports/manufacturing/2026/manufacturing-insights-2026-ai-impact-survey-report)). The difference between a pilot and a production system is usually integration, governance, and ownership, not the model. We build with those three in mind: integration with the existing ERP and data sources, governance aligned to the EU AI Act, and knowledge transfer so your team owns and maintains the system. The goal is not a dependency. It is a planner who spends the week on decisions rather than recalculation. ## Where the planning meeting goes next [#](#where-the-planning-meeting-goes-next) The weekly planning meeting does not disappear. Its purpose changes. When the schedule is maintained continuously and disruptions are absorbed as they happen, the meeting no longer exists to rebuild a stale plan. It exists to review the exceptions the agent escalated, to settle trade-offs that need human authority, and to look further ahead than the next seven days. That is the practical promise of agentic scheduling for mid-market manufacturers: not a factory that runs itself, but a planning function that operates at the speed its conditions actually change. The plan stays current, the planner stays in control, and the meeting becomes a place for judgement rather than reconciliation. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![The mid-market manufacturing AI gap in 2026](/app/uploads/2026/06/Group-1777-2-1.png) ### The mid-market manufacturing AI gap in 2026 Read article ](/agentic-ai/the-mid-market-manufacturing-ai-gap-in-2026/)[ ![4 Lessons from failed AI adoption ideas](/app/uploads/2026/04/2024-01-20__Y4A0426-scaled.webp) ### 4 Lessons from failed AI adoption ideas Read article ](/agentic-ai/4-lessons-from-failed-ai-adoption-ideas/)[ ![Agentic AI services market growth: who is leading](/app/uploads/2026/06/Group-1777-2.png) ### Agentic AI services market growth: who is leading Read article ](/agentic-ai/agentic-ai-services-market-growth-who-is-leading/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI governance in regulated industries: what actually binds you URL: https://vstorm.co/agentic-ai/agentic-ai-governance-in-regulated-industries The EU AI Act delay moved high-risk deadlines to 2027, but agents still answer to Article 50, DORA and their audit trail. Seven controls to build now. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI governance in regulated industries: what actually binds you [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI governance in regulated industries: what actually binds you The EU AI Act delay moved high-risk deadlines to 2027, but agents still answer to Article 50, DORA and their audit trail. Seven controls to build now. ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer October 13, 2026 11 min read ![Agentic AI governance in regulated industries: what actually binds you](/app/uploads/2026/10/cover-agentic-ai-governance-in-regulated-industries-v3.png) On this page0% 1. [The delay was narrower than the headlines](#the-delay-was-narrower-than-the-headlines) 2. [There is no agentic AI regime](#there-is-no-agentic-ai-regime) 3. [What the deferral does not change](#what-the-deferral-does-not-change) 4. [In financial services, DORA is the live obligation](#in-financial-services-dora-is-the-live-obligation) 5. [The seven controls that close the gap](#the-seven-controls-that-close-the-gap) 6. [The failure modes are documented now](#the-failure-modes-are-documented-now) 7. [Staging the rollout](#staging-the-rollout) 8. [Frequently asked questions](#frequently-asked-questions) 9. [What to do with the sixteen months](#what-to-do-with-the-sixteen-months) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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For two years, every roadmap for agentic AI in a regulated industry had the same milestone on it: 2 August 2026. That was when the EU AI Act's high-risk obligations were supposed to bite, and it gave compliance teams a date to organise around. It moved. The [Digital Omnibus on AI](https://eur-lex.europa.eu/eli/reg/2026/1744/oj/eng) entered into force on 27 July 2026, six days before the original deadline, and pushed high-risk obligations for standalone Annex III systems out to 2 December 2027, with product-embedded Annex I systems, which include medical devices, following on 2 August 2028. If your programme was paced against August 2026, you now have sixteen extra months. What you do not have is a reprieve, because the obligations that actually govern an agent taking actions in your business were either never deferred or never existed in the first place. This piece is about the EU position, and about deployers rather than model providers. ## The delay was narrower than the headlines [#](#the-delay-was-narrower-than-the-headlines) The Omnibus deferred a specific thing: the Chapter III regime for high-risk systems. That covers the provider requirements and conformity assessment, and also the deployer obligations that sit alongside them, including the fundamental rights impact assessment. It did not defer the Act. It was not the only date that moved. The deadline for national regulatory sandboxes shifted to 2 August 2027, and the machine-readable marking duty for generative systems already on the market was given until 2 December 2026. The Omnibus also added obligations: two new prohibited practices, covering systems that generate non-consensual intimate imagery or child sexual abuse material, apply from 2 December 2026. And the AI literacy duty in Article 4 was softened. Providers and deployers must now take measures to support AI literacy among their staff, rather than ensure a specified level of it. ### Transparency obligations landed on time The Article 50 transparency duties [took effect on schedule](https://www.morganlewis.com/pubs/2026/06/eu-approves-delays-and-other-amendments-to-certain-eu-ai-act-obligations-what-businesses-should-know) on 2 August 2026. They apply based on what a system does rather than which risk tier it sits in, which means a customer-facing agent falls under them regardless of whether anyone has classified it as high-risk. Providers of generative systems already on the market before that date received a four-month transition, to 2 December 2026, on the Article 50(2) machine-readable marking obligation, and nothing more. The duty to tell people they are interacting with an AI system applied from 2 August 2026 without a grace period. ### What got lighter for smaller deployers The Omnibus also brought changes that cut the other way, though most of them land on providers rather than deployers. For deployers, the relevant change concerns the fundamental rights impact assessment. Where a data protection impact assessment already meets some of its obligations, the deployer may cross-reference those sections of the DPIA or include them in the FRIA. The original Act already required the two assessments to complement each other, so this clarifies how rather than creating a new saving. It also only matters to deployers who are required to carry out a FRIA in the first place. For providers, the reductions are more substantial. SMEs, start-ups and small mid-caps may supply technical documentation in a simplified form the Commission will publish. Quality management obligations are explicitly proportionate to the size of the provider's organisation. If you build and deploy your own agent, you may be the provider as well as the deployer, and both sets of changes can apply to you. In that case, check your headcount and turnover against the EU's SME and small mid-cap definitions before you budget for the old requirements. ## There is no agentic AI regime [#](#there-is-no-agentic-ai-regime) Here is the part that changes how you should plan. ### The Commission's own position is preliminary The European Commission, [through the AI Act Service Desk](https://ai-act-service-desk.ec.europa.eu/en/ai-act/faq/how-are-ai-agents-addressed-within-ai-act-0), states that because developments around AI agents are recent and fast evolving, its regulatory considerations remain preliminary. The same guidance is clear on scope, though: agents are not a separate category under the Act, but they fall within its existing definitions of an AI system and a general-purpose AI model, so the rules for both apply to them. Where agency does appear in the framework, it appears indirectly. Autonomy and tool use are among the Annex XIII criteria that can push an underlying general-purpose model into the systemic-risk category, which is an obligation on the model provider, not on you. ### Named in the nomenclature, defined nowhere The Omnibus introduced a nomenclature in Annex XIV for scoping what notified bodies are competent to assess, and agentic AI is named in it. Commentators disagree on whether it holds a code of its own or appears as an example inside a [residual category for emerging technologies](https://www.nicfab.eu/en/posts/digital-omnibus-ai-official-journal/), and the distinction matters less than what all readings share: the phrase is named and nothing is attached to it. No definition. No classification criteria. No obligation that scales with how many steps a system takes without a human involved. ### So classification falls back to use case The question "is our agent high-risk" has no agent-specific answer. It resolves the way it would for any other AI system, by use case under Annex III, or under Annex I if the agent is part of a regulated product. The obligations that follow from that classification apply from 2 December 2027 for Annex III systems and 2 August 2028 for Annex I. The practical consequence: if your governance programme is waiting for the regulator to define an agent and prescribe controls for it, you will be waiting past 2027. In the meantime you are not unregulated. You are regulated by rules written for something else, which apply anyway. ## What the deferral does not change [#](#what-the-deferral-does-not-change) Three articles of the AI Act carry most of the weight for an acting system. Their deadline moved with the rest of Chapter III, to December 2027 for Annex III systems, but they describe what any acting system will have to evidence, and an agent you deploy this year will still be running when they apply. ### Article 12: record-keeping High-risk systems must log events across their lifecycle. For a model that returns an answer, this is a manageable obligation. For an agent, a log is not a transcript of what the model said. It is the sequence of tool calls, the inputs to each, the outputs returned, and the state of the system at each step. Logging an agent's actions is often assumed to be hard, but it does not have to be: tool calls are structured events and easy to capture. The hard part is explaining why the agent took a path, because runs cannot be reproduced. ### Article 14: human oversight The requirement is that oversight be effective, not that a human exists somewhere in the org chart. An approval screen a reviewer clicks through at 200 actions a day does not constitute oversight, and it will not survive a supervisor asking how many of those approvals were refused. ### Article 26: deployer obligations Most companies buying agentic AI are deployers, not providers, and deployer obligations are the ones people skip because the loudest parts of the Act are aimed at providers. They include using the system according to its instructions, assigning oversight to people with the competence and authority to exercise it, and monitoring operation. Note the pattern across all three. None of them require you to know whether your agent is high-risk. They require you to know what it did and who was accountable. ## In financial services, DORA is the live obligation [#](#in-financial-services-dora-is-the-live-obligation) Ask a bank's compliance team about AI regulation and they will talk about the AI Act. The rule that actually binds their agentic deployment today is DORA, which [has applied since 17 January 2025](https://www.fma.gv.at/en/cross-sectoral-topics/dora/dora-managing-of-ict-third-party-risk/). ### Your model vendors are ICT third parties DORA governs ICT third-party risk, and AI and language model providers can be captured as ICT third-party service providers. Once they are, Articles 28 to 30 apply: [a register of information](https://www.digital-operational-resilience-act.com/Article_28.html) covering every contractual arrangement for ICT services, distinguishing those that support critical or important functions from those that do not; due diligence before contracting; mandated contractual terms including audit rights and exit provisions; and concentration risk analysis. ### Now count your agent A single deployment might call two model providers, a vector database, an email API, a CRM, and three internal services through an MCP layer. Under DORA, several of those are contractual ICT arrangements that belong in a register submitted to your national competent authority. Most agentic pilots were built by an innovation team that never saw the register, and the register is the artefact a supervisor asks for first. ### The exit strategy nobody has tested Demonstrating that you can move off a model provider is qualitatively harder than demonstrating you can move off a cloud region, because behaviour is not portable. A prompt suite tuned against one model does not transfer, and "we would rewrite the prompts" is not a tested transition plan. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## The seven controls that close the gap [#](#the-seven-controls-that-close-the-gap) No regulation will tell you how to build this. The architecture below is what fills the space between what the law requires and what an agent does. ### 1\. Authority scoping Least privilege expressed as configuration, not policy language: per tool, per data class, per action type. The test is whether you can produce the list of actions the agent is permitted to take without reading the prompt. ### 2\. Where the human sits Choose pre-approval, post-review or exception-only by reversibility and blast radius, not by how important the process feels. A reversible action affecting one record can be reviewed after the fact. An irreversible action affecting many cannot. ### 3\. The determinism boundary Some steps must be code. Limits, eligibility checks, mandatory disclosures and regulatory calculations do not belong in model judgment, and moving them out shrinks what you have to evaluate. ### 4\. Audit trail as a product requirement Tool calls, inputs, outputs, model and prompt versions, and the approver. The working test: can someone reconstruct a single decision twelve months from now, when the prompt has changed four times and the model version has rolled twice? ### 5\. Trajectory monitoring Agents fail as sequences, not as single bad outputs. A run in which every step looks individually reasonable can still end somewhere it should not, which means alerting on output quality misses the failure mode entirely. ### 6\. Change control A new tool connected to an existing agent changes what that agent can do. Treat it with the same seriousness as a model change, or your approved system quietly becomes a different system. ### 7\. Dependency inventory List every model, tool, API and library the agent relies on, with its version, owner and contract. DORA requires this map for relevant ICT arrangements, and it is what you reach for when a package such as LiteLLM is compromised and you have hours to find your exposure. Can you name every dependency in a given run and replace each critical one? ## The failure modes are documented now [#](#the-failure-modes-are-documented-now) Agent security stopped being hypothetical during 2025 and 2026. ### Indirect prompt injection The payload sits in content the agent retrieves rather than in a user's message, with [disclosed findings](https://www.sysdig.com/learn-cloud-native/prompt-injection) against widely deployed enterprise assistants and coding agents. OWASP's agentic-applications risk list [added categories](https://labs.cloudsecurityalliance.org/research/csa-research-note-indirect-prompt-injection-in-the-wild-2026/) for goal hijacking and tool misuse specifically because the generic LLM risk list did not cover the case where the output is an action. ### The supply chain underneath In March 2026 a backdoored build of LiteLLM, a gateway used by several agent frameworks, sat on PyPI for two hours and thirty-two minutes and was downloaded more than 119,000 times, [according to PyPI's incident report](https://blog.pypi.org/posts/2026-04-02-incident-report-litellm-telnyx-supply-chain-attack/). ### The dull one that reaches a tribunal The regulatory failure mode is older than any of these: an agent commits the organisation to something it was not authorised to commit to. A Canadian tribunal [held an airline](https://arxiv.org/pdf/2604.18658) to refund terms its chatbot had stated. That is the whole problem in one sentence. The system did not need to be compromised. It needed to be believed. This is why authority scoping and the determinism boundary are the two controls to build first. They are the only ones that constrain what an agent can do once everything else has already failed. ## Staging the rollout [#](#staging-the-rollout) Four stages, each with an exit criterion you can measure rather than a feeling of readiness. ### Stage 1: Shadow The agent runs against live inputs and writes nothing. Exit when its proposed actions match what the human actually did, at a rate and over a volume you agreed before starting rather than after seeing the results. ### Stage 2: Recommend-only The agent proposes, a human executes. Exit when the override rate has stabilised and, more importantly, when you can explain the remaining overrides. An unexplained override rate means you do not yet understand the failure distribution. ### Stage 3: Bounded autonomy The agent acts within a scope defined by value, reversibility and data class, with everything outside that scope escalated. Exit when the audit trail has survived a review conducted by someone who did not build the system. ### Stage 4: Expanded scope Widen the bounds one dimension at a time, and treat each widening as a change requiring the same approval as the original deployment. ### The shortcut that costs the most Most organisations try to jump from pilot to bounded autonomy directly, because shadow mode produces no business value and is therefore hard to fund. That is the shortcut that produces deployments which work correctly and cannot be evidenced. ## Frequently asked questions [#](#frequently-asked-questions) ### Did the EU delay the AI Act? Partly. High-risk obligations moved to 2 December 2027 for Annex III systems and 2 August 2028 for Annex I systems. The Act's general application date and the Article 50 transparency duties were not moved. ### What applies from August 2026? Transparency obligations, including disclosure that a person is interacting with an AI system and marking of generated content, alongside the prohibited-practice rules already in force. ### Does the AI Act define an AI agent? No. Agentic AI is named in the Annex XIV nomenclature used to scope notified body designation, but no article defines it and no obligation is keyed to autonomy. ### Is our AI vendor a DORA third party? That depends on whether you are an in-scope financial entity and whether the service meets DORA's definition of an ICT service. Where it does, the arrangement belongs in your register of information and the contract falls under the Article 30 requirements. It is an assessment each entity has to make and document, not a question with a general answer. ## What to do with the sixteen months [#](#what-to-do-with-the-sixteen-months) The deferral is useful only if it buys something. The organisations that will be ready in December 2027 are not the ones writing longer policies. They are the ones whose controls produce evidence as a by-product of running, where the audit trail exists because the system cannot execute a tool call without writing one, not because someone remembers to export logs before a review. That is a design decision, and it is considerably cheaper to make now than to retrofit onto a deployment that has been running for a year. If you are working out which of the seven controls your deployment is missing, [book a call](https://vstorm.co/schedule-a-meeting/). ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer [Author profile](/author/konrad-budek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Process mapping for agentic AI: a step-by-step guide](/app/uploads/2026/10/cover-process-mapping-for-agentic-ai-v3.png) ### Process mapping for agentic AI: a step-by-step guide Read article ](/agentic-ai/process-mapping-for-agentic-ai/)[ ![An engineering voice at PRINTING United Expo in Las Vegas](/app/uploads/2026/10/cover-an-engineering-voice-at-printing-united-expo.jpg) ### An engineering voice at PRINTING United Expo in Las Vegas Read article ](/agentic-ai/an-engineering-voice-at-printing-united-expo/)[ ![AI agent evaluation and reliability](/app/uploads/2026/10/cover-ai-agent-evaluation-and-reliability-v3.png) ### AI agent evaluation and reliability Read article ](/agentic-ai/ai-agent-evaluation-and-reliability/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in agriculture URL: https://vstorm.co/agentic-ai/agentic-ai-in-agriculture Agentic AI for agriculture and AgTech — auditable agents for equipment maintenance, procurement documents, and compliance across distributed field operations. [Home](/)/[Industries](/industries/)/Agentic Ai In Agriculture # Agentic AI in agriculture Agents for the coordination gap in field operations. We build agents that read equipment, procurement, and compliance data across distributed field operations — with the audit trail your operations team can actually trust. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) spray window 1416182022 week Field N4 ready Fungicide delivery Sprayer 2 service draft, service in wk 16 operator review 01 Read 02 Clash 03 Draft Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Field data does not turn itself into a decision Sensor readings, equipment logs, supplier documents and compliance records arrive from separate places and rarely line up on their own. Most operations already collect all of it. The work that stays manual is connecting that data to a decision, with someone cross-referencing every source by hand. We map where an agent can act directly, where it needs a human sign-off, and how every decision gets logged — before touching your field systems. Sources Agent Outcomes Coordination agent Sensor & yielddata Equipment logs Supplier documents Maintenance system Operator review Audit log every recommendation traces to its source data Use cases ## Where agents earn trust in agricultural operations Distributed, multi-source workflows with a clear escalation path. 01 ### Equipment maintenance triage An agent reads sensor and usage data across equipment, flagging maintenance needs before failure with reasoning attached. 02 ### Input & supply procurement documents Reads supplier documents and purchase records, cross-checks against contracts, and drafts orders for a human to approve. 03 ### Compliance record-keeping Maintains sourced, auditable records across distributed field operations for regulatory and certification review. 04 ### Yield and forecast reconciliation Cross-references incoming yield data against forecasts, surfacing the exceptions that need agronomic attention first. What the mechanism delivers ## Cross-system coordination, measured in production None of these numbers come from agriculture. Vstorm has not shipped a production agent inside an agriculture business, so every figure below is from another industry — Synera (engineering software), Mixam (print on demand) and Schmitt-Thompson (healthcare). They are here because they measure the three things an agronomy or supply-coordination workflow depends on: a validated multi-step run instead of a single pass, orchestration across a distributed operation, and staged validation before a recommendation is acted on. Every figure links to the case study behind it. Agents do not set an application rate, approve a purchase order, or sign off a certification record. They gather, cross-check and draft what sits before an agronomist's or operations lead's decision. Every step is logged for review. Manual workflow setup With agents in production ### Manual setup per multi-step workflow 2 hrs Not an agriculture deployment — engineers on Synera's platform assembled each complex workflow by hand, node by node. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) ### With a text-to-workflow agent in production 3 min Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 95.4% ### Success rate in workflow results A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) 44% → 98% ### raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Client results ## Proof from production agentic deployments We have not yet shipped a production agent inside an agriculture business. These are the closest available references — the same multi-step workflow generation, distributed-operation coordination and staged validation, shipped in engineering software, print on demand and healthcare. [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production agent TriStorm keeps data quality and engineering aligned — integration and data-quality risks surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Map systems and data sources We audit target workflows, field-system boundaries, and data quality — ranking automation candidates by impact and integration risk. * Systems & data audit * Data quality review * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We implement against real field and procurement data shapes, with an evaluation suite scored before any output reaches an operator. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout with monitoring and audit logging, plus a structured handoff so your team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own agronomy and supply-coordination workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in agriculture, answered All FitUse casesDeliveryOperations Where does agentic AI actually help in agricultural operations? + In workflows that combine field-distributed sensor or yield data with a decision that needs cross-referencing — input procurement, equipment maintenance scheduling, or compliance documentation. We build agents for that coordination layer, not to replace agronomic judgment. How is this different from the dashboards and alerts we already have? + A dashboard shows you a signal. An agent reads the signal alongside other data (equipment logs, weather, supplier documents) and completes a multi-step task: drafting a purchase order, flagging a maintenance need, escalating a compliance gap. What agricultural workflows are realistic first projects? + Equipment maintenance triage from sensor and usage data, input and supply procurement document processing, and compliance record-keeping across distributed field operations. Can an agent work with data spread across disconnected field systems? + Yes — this is fundamentally a retrieval and integration problem: an agent reads from multiple systems of record and reasons across them, which is the same mechanism we have engineered for other multi-system, document-heavy operations. What is the typical timeline to a working system? + A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks, following the same TriStorm phases as any Vstorm engagement. Do we own the system after it is built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in, and your team is trained to operate and extend it. Start with one workflow ## Map one field operations workflow worth automating A 30-minute call identifies data sources, integration points, and a realistic path to a working agent. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in automotive URL: https://vstorm.co/agentic-ai/agentic-ai-in-automotive Agentic AI for automotive manufacturing and operations — auditable agents for production-line quality triage, warranty claims verification, and supply-chain exception handling. [Home](/)/[Industries](/industries/)/Agentic Ai In Automotive # Agentic AI in automotive Agents that query production data directly. We build agents that query production, claims, and supplier data directly — turning a plant manager's or claims adjuster's question into a validated database query, not a chatbot layered on top. Every query and action is logged, so quality and engineering teams can trace exactly what the agent looked at and why. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) query: claim vs repair order Warranty claim Repair order VIN …4471 VIN …4471 op B12 op B12 pn 4482 pn 4501 mismatch Adjuster query attached 01 Query 02 Cross-check 03 Adjuster Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Automotive engineering runs on production data most AI never touches Manufacturing execution systems, quality telemetry, warranty claims and supplier records are highly structured, and scattered across MES, ERP and dealer systems that do not talk to each other. A chatbot layered over that will answer confidently and cite nothing. A plant floor, where a wrong call has real cost, needs an agent that queries the underlying data directly, cross-references specs and bulletins, and escalates anything it is not confident about. We start by mapping the production or claims workflow your team already runs manually, then decide what an agent can query directly and where a human has to sign off. Sources Agent Outcomes Productionquery agent Plant sensor data Warranty claims Supplier data MES / ERP systems Human reviewer Audit log every query traced back to source data Use cases ## Where agents earn trust in automotive operations Workflows where production and claims data already live in structured systems — the agent queries them directly instead of guessing. 01 ### Production-line quality triage An agent reads sensor and inspection data, checks it against the spec sheet, and drafts a defect report for a line supervisor to confirm — before a bad batch reaches the next station. 02 ### Warranty claims verification Cross-checks a claim against the repair order, vehicle history, and manufacturer service bulletins before it reaches an adjuster — catching mismatches a single-system lookup would miss. 03 ### Supply-chain exception handling Monitors supplier and inventory data for part shortages and drafts a reroute or substitution recommendation for a procurement lead to approve, instead of a line stoppage nobody saw coming. 04 ### Technician diagnostic assist Retrieves the service bulletins and wiring diagrams tied to a diagnostic trouble code and drafts a repair path for the technician to verify, replacing a manual lookup across multiple manuals. Evidence from production ## Engineering-workflow orchestration, measured in production None of these numbers come from an automotive deployment — we have not shipped a production agent inside an OEM or a tier-one supplier yet. Synera is the closest adjacent match: engineering software, where the agent automates multi-step engineering work of the same class as an engineering-change or validation workflow. Mixam measures multi-tool orchestration at scale, and Schmitt-Thompson measures validation before a safety-relevant output. Every figure links to the case study behind it. Agents do not release an engineering change, approve a warranty payout, or act on the line. They query, cross-check and draft what sits before an engineer's sign-off. Every step is logged for review. Manual workflow setup With agents in production ### Manual setup per multi-step workflow 2 hrs Not an automotive deployment — engineers on Synera's platform assembled each complex workflow by hand, node by node. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) ### With a text-to-workflow agent in production 3 min Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 95.4% ### Success rate in workflow results A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) 44% → 98% ### raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Client results ## Proof from production agentic deployments We have not shipped a production agent inside an automotive manufacturer or supplier. Synera is the closest match — engineering software, automating the same class of multi-step engineering work as change and validation workflows on a vehicle program. Mixam shows the multi-tool orchestration and Schmitt-Thompson the validation gate that work depends on. [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production agent on your plant floor TriStorm keeps engineering, quality, and IT aligned, so the agent works within existing MES and ERP boundaries, not around them. real inputs adversarial latency VALIDATING go / no-go 1 ### Map workflow and data access We audit the target workflow (production, claims, or supplier data) and the systems of record it touches, then rank use cases by engineering impact and integration risk. * Workflow audit * Data access map * Prioritized use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We build against your real production data shapes (schemas, sensor formats, claims fields) and validate every output against an evaluation suite before it reaches a reviewer. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout with logging on every query and action, plus a structured handoff so your engineering and quality teams run the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own engineering-change and supplier workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in automotive, answered All FitUse casesComplianceDeliveryOperations Where does agentic AI actually fit into automotive operations? + In workflows where production, claims, or supplier data already lives in a structured system of record — quality triage, warranty verification, supply exception handling, diagnostic lookup. We do not deploy agents to make unsupervised decisions on the line; we deploy them to query, cross-check, and draft, with an engineer or reviewer as the final gate. How is this different from the automation already running on our production line? + Rule-based automation reacts to a fixed condition. An agent reasons across multiple data sources (sensor readings, spec sheets, historical claims) and adapts its query path to what it finds, the same reasoning-plus-validation pattern behind Synera's agent platform, which reads engineering intent and generates a fully validated workflow through multi-step checks rather than a single pass. What automotive workflows do agents typically handle first? + Quality triage on inspection data, warranty claims cross-checked against service bulletins, supply-chain exception flagging, and diagnostic assist for technicians. We start with whichever workflow has the clearest data access and the most manual hours behind it. How do you handle data governance across MES, ERP, and dealer systems? + Agents connect through your existing access controls — role-based permissions on what they can query, no write access without a human step, and a log of every query and action taken. We map the data path and governance boundary before we design the agent. What happens when the agent hits data it is not confident interpreting? + It escalates. Confidence thresholds and schema or data gaps route to a human reviewer with the reasoning and underlying query attached, so nothing acts on an uncertain read. Can this integrate with our existing MES, ERP, or dealer management system? + Yes, through your existing APIs and database views, not a rip-and-replace. The same multi-step reasoning-and-validation pattern we shipped for Synera's engineering-workflow platform applies to querying production data directly, without altering the underlying schema. What is the typical path from pilot to a production agent? + A scoped Proof of Value (one workflow, real data, a working agent) typically lands within a few weeks. Full production rollout with monitoring and an operator handoff follows the same TriStorm phases as any other Vstorm engagement. Start with one workflow ## Map one production workflow worth automating A 30-minute call identifies data access, integration points, and a realistic path to a working agent your engineering team will trust. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in education URL: https://vstorm.co/agentic-ai/agentic-ai-in-education Agentic AI for education and EdTech — agents that handle student support, adaptive learning, and multilingual training delivery, integrated with the LMS and student systems you already run. [Home](/)/[Industries](/industries/)/Agentic Ai In Education # Agentic AI in education Agents that scale student support. We build agents that read your course content, student records, and program guidelines, then answer, route, or adapt in real time — with the escalation path an academic team will actually trust. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Module 1 Module 2 Practice set Module 3 Module 4 learning agent How do I cite sources? APA. See Module 2 quiz 2: 2/5 01 Ask 02 Answer 03 Adapt Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most EdTech AI is a chatbot wearing a syllabus Education has the shape these systems handle well: a defined body of course content or policy, a stream of individual student questions or performance signals, and a decision that improves when more than one source is checked. The common failure is treating it as a single-turn FAQ bot, which answers the easy questions and fails silently on the rest, leaving no record of what it missed or why. We start by mapping which student-facing decisions the agent can answer directly, which need a human in the loop, and how every interaction gets logged for review. Sources Agent Outcomes Support &learning agent Student questions Course content Progress data LMS / SIS Instructor review Audit log every answer traces to a source document Use cases ## Where agents earn trust in education operations Workflows with a defined knowledge base and a clear point to escalate to a person. 01 ### Multilingual student support An agent answers student and trainee questions across languages by reasoning over course content and program documentation, not a single scripted flow. 02 ### Adaptive learning paths Reads a learner's progress and prior responses, then sequences or adjusts content — flags stalled learners to an instructor instead of guessing. 03 ### Admissions and enrollment triage Cross-checks applications or enrollment requests against program requirements and prior records, routing edge cases to an admissions officer. 04 ### Instructor feedback drafting Drafts rubric-aligned feedback from a student's submission and history for an instructor to review and send — not to grade unsupervised. The cost of unanswered questions ## What a training knowledge base answers once an agent sits in front of it ARIJ Network is the closest thing to direct education proof we have: a bilingual agent embedded in ARIJ's Moodle LMS, answering learners only from ARIJ's own knowledge base. It served journalist training rather than a school or university, so treat it as adjacent, not vertical-exact. The Synera and Mixam figures sit further out (engineering software and print on demand) and are here because they measure what a learner-facing agent depends on: multi-step validation instead of a single retrieval pass. Every figure below links to the case study behind it. Agents do not grade high-stakes assessments or decide admissions on their own. They retrieve, cross-check and draft what sits before a person's decision — and escalate to an instructor or administrator, reasoning attached, when the content does not cover the question. 2 hrs → 3 min ### to generate a validated workflow Not an education deployment — Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 95.4% ### Success rate in workflow results Not an education deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) Client results ## Proof from the closest learning-platform deployment we have We have not yet shipped an agent inside a school, university or EdTech vendor. ARIJ Network is a learning-platform deployment in everything but the client's sector label — a bilingual agent living inside a Moodle LMS, answering learners from the institution's own knowledge base across 22 countries — built for journalist training rather than a degree program. Synera and Mixam are further out still, and are here for the mechanism, not the vertical. [ ### Multilingual AI agent-powered chatbot supporting journalist training A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic. EN + ARone agent answering in English and Arabic from ARIJ's own materials 2languages, English and Arabic, from one knowledge base Read case study ![](/_astro/The-LLM-Book-Vstrom-2.CDz2_UY9.png) ](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production support agent TriStorm keeps academic ownership and engineering aligned, so the agent launches with people who trust it. real inputs adversarial latency VALIDATING go / no-go 1 ### Map workflow and content sources We audit the target workflow, the course or policy content it needs, and any student-data constraints — ranking use cases by student impact and integration effort. * Workflow audit * Content & data map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We build against your real course content and student-data shapes, with an evaluation set scored against known-good answers before it reaches a student. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout into your LMS or support channel, with audit logging and a handoff so your academic and support teams can operate it independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own learner-support and course-content workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in education, answered All FitUse casesComplianceDeliveryOperations What kind of institution is this actually for? + Training organizations, EdTech platforms, and universities with a workflow that already has a defined answer space — course content, student records, program guidelines, a knowledge base. We are not selling a generic AI tutor. We are building an agent around a specific workflow you already run manually or through rigid rules. How is this different from the AI chatbot our LMS vendor already offers? + Most LMS chatbots retrieve a single document and paraphrase it. An agent plans across steps — checks a student's history, cross-references multiple sources, decides whether it has enough to answer or needs to escalate. Our multilingual chatbot for ARIJ Network reasons across a full training knowledge base in English and Arabic, not a single FAQ page. What education workflows actually qualify for an agent, versus simple automation? + Anything with real reasoning across sources: student support answering questions against a knowledge base and student record, adaptive learning paths that adjust based on performance and multiple content sources, admissions or enrollment triage, and instructor-facing tools that draft feedback from rubrics and submission history. A single-lookup FAQ does not need an agent — a rules engine handles that fine. Can an agent actually personalize learning, or is that marketing language? + An agent can read a learner's progress, prior responses, and stated goals, then select or sequence content accordingly — that is a real, buildable pattern. What it cannot do responsibly is grade high-stakes assessments or make enrollment decisions unsupervised. We draw that line explicitly during scoping, not after launch. How do you handle student data and privacy? + We map data residency, retention, and access-control requirements before writing integration code. For institutions under FERPA, GDPR, or regional student-data regulations, the agent is scoped to only the fields it needs, with role-based access and a logged audit trail on every action it takes. What happens when the agent does not know the answer? + It escalates rather than guesses. Confidence thresholds and gaps in the underlying knowledge base route to a human (instructor, support staff, or administrator) with the agent's reasoning attached, so the reviewer is not starting from zero. Can this integrate with our existing LMS and student information system? + Yes, through existing APIs and data exports rather than a platform migration. The ARIJ Network deployment runs as an agent layered on existing training content inside ARIJ's Moodle, with no replacement system. What is the realistic timeline and what have you actually shipped in this space? + A scoped Proof of Value on one workflow typically lands in about three weeks. On the ARIJ Network deployment, a bilingual English and Arabic agent inside ARIJ's Moodle handles first-line questions from ARIJ's own materials. Start with one workflow ## Map one student-facing workflow worth automating A 30-minute call identifies your content sources, data constraints, and a realistic path to a working agent your academic team will trust. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in energy & utilities URL: https://vstorm.co/agentic-ai/agentic-ai-in-energy Agentic AI for energy and utility operators — auditable agents for outage response, maintenance work orders, and engineering document retrieval, integrated with existing SCADA and asset systems. [Home](/)/[Industries](/industries/)/Agentic Ai In Energy # Agentic AI in energy & utilities Agents that work inside grid operations. We build agents that read sensor and maintenance data, draft work orders, and retrieve engineering documentation during time-pressured operations — with the audit trail an operations or safety team will actually sign off on. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) substation T-14 SCADA maint. log drawings ops agent work order draft asset T-14 operator approves 01 Anomaly 02 Cross-check 03 Work order Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most energy AI stops at a dashboard A sensor anomaly that needs a work order, an outage that needs the right procedure fast, a maintenance backlog that needs prioritizing against real risk: grid and plant operations already produce these decisions under time pressure. Before an agent touches physical assets, an operations or safety team wants the escalation path, the audit log and the human approval gate already in place. That layer, rather than model quality, is what most energy deployments are missing. We start every energy engagement by mapping where an agent can draft and where it must stop and escalate — before any control-system integration is scoped. Sources Agent Outcomes Ops support agent Sensor & SCADAdata Maintenance logs Engineering docs Work order system Human operator Audit log every recommendation logged and reviewable Use cases ## Where agents earn trust in energy operations Workflows with a clear decision boundary and a record already required by operations or safety practice. 01 ### Sensor anomaly to work order An agent correlates a sensor deviation against maintenance history and equipment specs, then drafts a work order with the supporting evidence attached — not just a threshold alert. 02 ### Engineering document retrieval During outage response, an agent pulls the relevant procedure or as-built drawing from thousands of engineering documents in seconds, sourced and citable, instead of a manual search. 03 ### Field service dispatch A dispatch agent evaluates crew location, job priority, and equipment access to route the next field job — the same real-time evaluate-and-route pattern we have shipped in other high-throughput operations. 04 ### Maintenance backlog triage An agent ranks open maintenance items against failure risk and asset criticality, drafting a prioritized schedule for an engineer to approve rather than a flat first-in-first-out queue. What the mechanism delivers ## The mechanism an outage workflow depends on, measured in production None of these numbers come from an energy or utilities deployment — we have not shipped a production agent inside one. They come from Synera (engineering software), Mixam (print on demand) and Schmitt-Thompson (healthcare), and they are here because each measures something an outage-response or maintenance workflow already depends on: multi-step validation instead of a single generation pass, real-time evaluate-and-route across a distributed operation, and staged retrieval that stops rather than guesses where a wrong answer is a safety event. Every figure below links to the case study behind it. Agents do not switch a feeder, move a setpoint, or close out a work order. They gather, cross-check and draft what sits before an operator's decision. Every step is logged for review. Manual workflow setup With agents in production ### Manual setup per multi-step workflow 2 hrs Not an energy deployment — engineers on Synera's platform assembled each complex workflow by hand, node by node. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) ### With a text-to-workflow agent in production 3 min Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 95.4% ### Success rate in workflow results A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) 44% → 98% ### raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Client results ## Proof from production agentic deployments We have not shipped a production agent inside an energy or utilities company. These are the closest references we have — the same multi-step validation, real-time routing, and staged retrieval under a safety constraint, shipped in engineering software, print on demand and healthcare. [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production ops agent TriStorm keeps operations and safety review aligned with engineering — risk surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Map workflow and constraints We audit the target workflow, safety and access-control boundaries, and the systems it touches (SCADA, historian, EAM) ranking use cases by operational impact and integration risk. * Workflow & risk audit * System access map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We implement against real operational data shapes, with an evaluation suite scored against your own procedures before any recommendation reaches an operator. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout with monitoring, audit logging, and a structured handoff so your operations and engineering teams run the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own outage-response and maintenance workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in energy & utilities, answered All FitUse casesComplianceDeliveryOperations Where does agentic AI actually fit into an energy or utility operation? + In workflows where an operator today manually cross-references sensor data, work orders, and procedures under time pressure — outage triage, maintenance scheduling, engineering documentation lookup. We do not deploy agents to make unsupervised control-system decisions; we deploy them to gather, cross-check, and draft, with an operator or engineer as the final gate. How is this different from the SCADA alerting and rules engines we already run? + A rules engine fires a fixed alert on a fixed threshold. An agent reasons across multiple sources (sensor history, maintenance logs, engineering documents, prior incidents) and drafts a next action, then escalates when its confidence drops. It extends what your control systems already flag; it does not replace them. What energy-sector workflows have the clearest path to production? + Anything with a clear decision boundary and a record already required: work-order generation from sensor anomalies, engineering-document retrieval during outage response, field-service dispatch, and vegetation or asset-inspection triage. Each already carries an audit requirement, which is the structure an agent needs to work inside. How do you handle grid-critical and safety-relevant systems? + We keep agents out of direct control-system actuation. They read from historians, SCADA, and asset-management systems through existing access controls, draft a recommendation or work order, and log every step. A human operator or engineer approves anything that touches physical assets or grid state. What happens when the agent is not confident in its recommendation? + It escalates instead of guessing. Confidence thresholds and coverage gaps route to a human reviewer with the full reasoning chain attached, so the reviewer sees why the agent flagged it, not just that it did. Can this integrate with our existing SCADA, EAM, and historian systems? + Yes, through your existing data infrastructure and APIs, not a rip-and-replace. Our text-to-workflow platform for Synera automates complex multi-step engineering and operational processes on top of existing systems, which is the same integration pattern we bring to energy operators. What is the typical timeline to a working system? + A scoped Proof of Value (one workflow, real operational data, a working agent) typically lands in about three weeks. Full production rollout with monitoring and operator handoff follows the same TriStorm phases as any other Vstorm engagement. Do you have energy-sector case studies? + Not yet inside an energy or utility company specifically. What we can point to is production agent work in comparably infrastructure-heavy, always-on operations — Synera's text-to-workflow platform (2 hrs → 3 min to generate a validated workflow) and Mixam's multi-agent system (95.4% success rate in workflow results) which evaluates and routes in real time across a distributed operation. Start with one workflow ## Map one operational workflow worth automating A 30-minute call identifies the systems it touches, the escalation boundary, and a realistic path to a working agent your operations team will trust. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Artificial Intelligence in Finance: How Agentic AI Is Changing Banking, Trading, and Compliance URL: https://vstorm.co/agentic-ai/agentic-ai-in-finance-applications-benefits-risks How artificial intelligence in finance is moving from automation to autonomous agents — real use cases, benefits, and where firms go wrong. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Artificial Intelligence in Finance: How Agentic AI Is Changing Banking, Trading, and Compliance [Agentic AI](/ai-blog-news/#agentic-ai) # Artificial Intelligence in Finance: How Agentic AI Is Changing Banking, Trading, and Compliance How artificial intelligence in finance is moving from automation to autonomous agents — real use cases, benefits, and where firms go wrong. ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer July 17, 2026 6 min read ![Artificial Intelligence in Finance: How Agentic AI Is Changing Banking, Trading, and Compliance](/app/uploads/2026/07/pexels-gdtography-277628-911738-1.webp) On this page0% 1. [What is agentic AI?](#what-is-agentic-ai) 2. [AI applications in finance](#ai-applications-in-finance) 3. [Benefits of AI in finance](#benefits-of-ai-in-finance) 4. [How can AI be used in finance?](#how-can-ai-be-used-in-finance) 5. [The pitfalls](#the-pitfalls) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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Rule-based automation. Generative AI that drafts and summarizes on command. And now agentic AI: systems that plan, decide, and act with little human oversight. The AI in finance industry is no longer experimenting at the edges. A [2026 Cambridge survey](https://www.jbs.cam.ac.uk/wp-content/uploads/2026/04/ccaf-2026-04-28-global-ai-in-financial-services-report.pdf) of 628 institutions across 151 jurisdictions found 42% are already using or assessing agentic AI, while only 21% have put it into production. That gap is the story. Ask how AI has been used in finance until now and the honest answer is: in the back office. Four of the top five live use cases are internal. Agentic AI is what changes that. ## **What is agentic AI?** [#](#what-is-agentic-ai) Two independent sources, a Moody's analysis and an academic survey, land on the same definition. Five traits: * **Autonomy, reasoning and planning.** The system decides and acts without step-by-step instruction. It also deliberates before acting. A chatbot answers a question. An agent decides what to do next. * **Adaptability.** Learns from feedback and shifts strategy without retraining. * **Collaboration and tool use.** Coordinates with other agents, APIs, and systems to run a workflow end-to-end. * **Long-term goals.** Pursues an objective across a whole process, not a single transaction. [Moody's own numbers](https://www.moodys.com/web/en/us/creditview/blog/agentic-ai-in-financial-services.html) show the effect. Its Research Assistant users consumed 60% more research and cut task time by 30%. Over 90% of interactions shifted to analytical work. The prize is not speed. It is human time moved toward judgments machines cannot make. ## **AI applications in finance** [#](#ai-applications-in-finance) Here artificial intelligence applications in finance stop being theoretical. ### **Governance and risk management** [McKinsey's 2026](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) research found nearly two-thirds of organizations name security and risk (not regulation, not technical limits) as the top barrier to scaling agentic AI. The counterpoint from the same work: firms investing $25 million or more in responsible AI show higher maturity and are far likelier to see EBIT impact. Governance is not a brake, but a precondition. Our experience at Vstorm shows that firms address this with explainable AI models, majority-voting across models so no single blind spot rules an outcome, and traceability that logs an agent's reasoning, not just its answer. ### **Artificial Intelligence in financial markets: multi-agent trading** Agentic AI treats trading as a game between learning agents, not one optimization. In backtests on 100 [Shenzhen Stock Exchange stocks](https://arxiv.org/pdf/2604.21672) from 2018 to 2021, a system pairing multi-agent reinforcement learning with portfolio-insurance constraints returned 7.76% a year, a 2.18 Sharpe ratio, a 6.60% maximum drawdown. This beats a plain reinforcement-learning baseline on every measure. At Vstorm, we build the risk control into how the agent acts, not into a check after the fact, and the numbers improve. ### **Portfolio management** Portfolio management is moving from fixed rebalancing rules to agents that read regime changes and adjust, sometimes across agents with different risk appetites. Position sizing follows: newer frameworks train agents on Sharpe ratio, profit and loss, drawdown, and turnover cost directly, so the system learns a tradable position rather than an elegant one it cannot execute. ### **Compliance** The clearest AI use cases in finance and accounting are in compliance. Regulatory logic now sits inside the workflow. Agents watch transactions in real time, flag suspicious items, escalate the rest, all within guardrails, per [IMF research](https://www.imf.org/-/media/files/publications/imf-notes/2026/english/insea2026004.pdf) on agentic AI in payments. The same research names a coming problem: Know-Your-Agent, an extension of Know-Your-Customer that verifies the entity behind an autonomous agent. Old identity checks assume the presence of a human on the other end. ### **Agentic transformation of institutions** Some firms restructure around "agentic crews," specialized agents handling modeling, validation, and compliance at once, all under human supervision. [Moody's](https://www.moodys.com/web/en/us/creditview/blog/agentic-ai-in-financial-services.html) runs multi-agent copilots that pre-screen credit applications and flag anomalies while keeping the audit trail intact. ### **Customer service with AI agents** Relationship manager churn runs 15% to 35% at many banks, driven by CRM admin, not client work. [McKinsey](https://www.mckinsey.com/industries/financial-services/our-insights/agentic-ai-is-here-is-your-banks-frontline-team-ready) found deal-scoring agents, recommending price and discounts in real time, cut prep time by about 25% and delivered around 10% margin gains in early use. Allianz Partners, using a tool built with Taktile, cut claims processing from days to minutes and kept people in the loop. Agentic does not have to mean unsupervised. ### **Personalized guidance and AI financial management** Here artificial intelligence in financial management turns proactive. McKinsey describes a near-term case: an agent notices, unprompted, that a customer could clear a credit card balance with idle cash in another account, and it acts, though the last click stays with the customer for regulatory reasons. The same capability cuts both ways. If the agent becomes the customer's default interface, the bank beneath it becomes a backend. The automation that deepens personalization can sever the relationship. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## **Benefits of AI in finance** [#](#benefits-of-ai-in-finance) The benefits of AI in finance come down to four things. **Speed and scale:** monitoring, stress testing, and fraud detection at a volume no human team matches. **Cost:** real cuts in processing time without matching headcount. **Better risk-adjusted decisions:** when the risk control lives inside the agent's actions, backtests improve. **Freed judgment:** over 90% of Moody's interactions moved to high-value work. That is the real prize. ## **How can AI be used in finance?** [#](#how-can-ai-be-used-in-finance) The pattern is consistent. Start with a bounded process that already has rules and audit requirements; compliance, claims, credit pre-screening; not open-ended autonomous trading. Build in explainability and oversight from day one. Measure value before scaling. The same Cambridge research found 55% of firms and 63% of regulators struggle to measure AI's value, rising to 76% in the largest institutions. Scaling what you cannot measure is how a pilot becomes permanent. ## **The pitfalls** [#](#the-pitfalls) The upside is real. But so are the failures. Several are specific to agentic AI, not generic AI risk: **Goal misalignment.** Agents optimizing their own reward can, together, produce what no one intended. As the density of learning agents rises, volatility rises with it, liquidity thins, and markets recover from shocks more slowly. No agent was built to destabilize anything. Individual reason, collective fragility. **Data quality.** Agentic systems amplify the data they are given. Data quality, legacy infrastructure, and unclear ownership are the bottleneck behind slow scaling. **Accountability and interpretability.** This is where the numbers bite. Making a system more interpretable typically costs 15% to 30% of its performance. In anti-money-laundering detection, the best systems hit 95% to 99% accuracy, yet fewer than 20% can explain themselves well enough to satisfy regulators. The gap between what works best and what can be audited is the hardest problem here. **Compliance and trust.** Human-in-the-loop assumes a person reviews each decision. That breaks when an agent makes thousands of decisions a second. The SEC Market Access Rule and MiFID II were written for firms running fixed strategies under supervision, not for systems that rewrite their own strategy as they watch. You need a partner who understands compliance is not just an afterthought. Machine learning in financial services is not new. Credit scoring and fraud detection have used it for over a decade. What changed is the move from a model that predicts to a system that acts. An old model: flags a transaction as likely fraud. An agentic system: flags it, chooses a response within its guardrails, executes, and adjusts its own future behavior, no human required to approve each step. That is why at Vstorm we think that the governance above matters more than it would for ordinary machine learning banking. Different risks, greater rewards. Mostly prediction and automation — credit scoring, fraud flagging, rule-based trading, report generation. Agentic AI is the shift to systems that plan and act across multistep workflows, not just produce output for a human to use. The mature ones are internal: compliance monitoring, credit pre-screening, claims processing, portfolio rebalancing. Customer-facing uses — personalized guidance, conversational banking — are earlier but growing fast. Speed and scale in monitoring and risk detection, lower cost in processing-heavy work, better risk-adjusted outcomes when risk controls are built in, and skilled staff freed for judgment over data collection. Start with bounded, auditable processes. Build in explainability and oversight from the start. Measure value before scaling — most institutions admit they cannot yet. Artificial intelligence in finance has cleared the pilot phase in the back office. Now it reaches the hard ground: autonomous decisions in trading, compliance, and the customer's own account. The firms getting value are not the ones with the best models. They are the ones treating governance, explainability, and measurement as part of the build. The distance between the 42% experimenting and the 21% in production is where that gets tested. ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer [Author profile](/author/konrad-budek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Agentic AI for mid-market companies: a structural advantage](/app/uploads/2026/07/THE-GAP-ISNT-TECHNOLOGY.-ITS-IMPLEMENTATION-768x431.webp) ### Agentic AI for mid-market companies: a structural advantage Read article ](/agentic-ai/agentic-ai-for-mid-market-companies-a-structural-advantage/)[ ![The future of Small Language Models in the middle-market](/app/uploads/2026/07/IMG_8222-768x473.jpg) ### The future of Small Language Models in the middle-market Read article ](/agentic-ai/the-future-of-small-language-models-in-the-middle-market/)[ ![Agentic AI ROI: two returns business cases should measure](/app/uploads/2026/04/288C8762-FB8D-43CE-B595-44C96A29B27F_1_105_c-768x512.jpeg) ### Agentic AI ROI: two returns business cases should measure Read article ](/agentic-ai/agentic-ai-roi-two-returns-business-cases-should-measure/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in logistics URL: https://vstorm.co/agentic-ai/agentic-ai-in-logistics Agentic AI for logistics and supply chain operations — orchestration agents for carrier matching, shipment exception handling, and multi-system order routing, built on your existing TMS and WMS. [Home](/)/[Industries](/industries/)/Logistics # Agentic AI in logistics Agents that work across your logistics network. We build agents that match loads to carriers, triage shipment exceptions, and route orders across the systems you already run — escalating to a dispatcher when the call is genuinely theirs to make. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) bill of lading packing list customs entry weight HS code pieces routing agent dispatcher pieces 24 vs 22 load held 01 Read 02 Cross-check 03 Escalate Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most logistics AI automates the clean path and stalls on the exceptions Freight, capacity, and order data live in separate systems — TMS, WMS, carrier EDI feeds, spreadsheets a dispatcher still keeps on the side. Rules engines handle the clean path fine. The moment a carrier cancels, an ETA slips, or a document is missing, the workflow needs a system that can read across all of it, weigh constraints, and decide — or know it should not decide and hand it to a person instead. We map which decisions an agent can own outright and which must route to a dispatcher, before any integration work starts. Sources Agent Outcomes Routing agent Shipment status Carrier capacity Exception alerts TMS / WMS Dispatcher Audit log every routing decision logged and reviewable Use cases ## Where agents earn trust in logistics operations Workflows with clear rules most of the time, and a defined escalation path for the rest. 01 ### Carrier and capacity matching An agent checks live carrier capacity and cost against shipment requirements and books the match, instead of a dispatcher working a spreadsheet by hand. 02 ### Shipment exception triage When an ETA slips or a delivery fails, the agent pulls context from the TMS and carrier feed, proposes a resolution, and escalates only the cases outside its confidence threshold. 03 ### Multi-system order routing Cross-checks inventory, warehouse capacity, and delivery windows across systems before committing an order to a fulfillment path — the coordination pattern behind Mixam's order-completion agent. 04 ### Dock and appointment scheduling Reads inbound and outbound volume against dock availability and books appointments directly, flagging conflicts a scheduler needs to resolve manually. The cost of manual coordination ## Where multi-system operations lose hours today These numbers come from our shipped multi-system orchestration engagements — not industry averages. Every figure below links to the case study behind it. We have not yet shipped inside a logistics-specific company. The mechanics behind these results (real-time constraint checking, multi-step orchestration, human escalation) transfer directly to load matching and shipment routing. Manual setup With agents in production ### Manual multi-step workflow setup 2 hrs Engineers assembled each complex workflow by hand on Synera's platform — two hours of tedious configuration per workflow. [Synera case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) ### With text-to-workflow agents 3 min Synera's agent platform generates complex workflows in under three minutes through multi-step validation — a fluent but illegal graph never reaches the interpreter. [Synera case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 95.4% ### Routing success rate in production Mixam's multi-agent system evaluates capacity and dispatches jobs in real time — the same coordination pattern behind load matching. [Mixam case study](/case-study/ai-agent-for-order-recommendation-and-completion/) Client results ## Proof from production multi-system agent deployments We have not yet shipped a production agent inside a logistics-specific company — here is what the same multi-system orchestration pattern delivers in production elsewhere. [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ### Multilingual AI agent-powered chatbot supporting journalist training A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic. EN + ARone agent answering in English and Arabic from ARIJ's own materials 2languages, English and Arabic, from one knowledge base Read case study ![](/_astro/The-LLM-Book-Vstrom-2.CDz2_UY9.png) ](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [All case studies](/case-studies/) Delivery path ## From workflow audit to a production routing agent TriStorm keeps the exception-handling logic and the escalation rules aligned before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Scope the workflow and systems We audit the target workflow, the systems it touches (TMS, WMS, carrier APIs) and where a human dispatcher currently makes the call. Use cases get ranked by volume and manual-effort saved. * Workflow & systems audit * Data access map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build, test and validate We implement against real shipment and carrier data, with an evaluation suite scored against historical dispatcher decisions before the agent touches a live shipment. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring and support Production rollout with monitoring, audit logging on every routing decision, and a structured handoff so your operations team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own routing and exception-handling workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in logistics, answered All FitUse casesDeliveryOperations Where does agentic AI actually fit into logistics operations? + In workflows that span multiple systems and require a decision, not just a lookup — load-to-carrier matching, exception handling on delayed shipments, dock scheduling, order-to-fulfillment routing. These are multi-step, multi-source tasks. A chatbot or a rules engine handles one step at a time; an agent reasons across the shipment, the carrier network, and the exception, then acts or escalates. How is this different from the TMS or WMS automation we already have? + Your TMS and WMS execute fixed logic against clean data. Agents sit above that layer and handle the cases the fixed logic cannot: an ETA slips, a carrier cancels, a document is missing. The agent reads the exception, checks constraints across systems, and either resolves it or routes it to a dispatcher with the reasoning attached. What logistics workflows have the best return for a first deployment? + High-frequency, well-bounded decisions with a clear escalation path: carrier/capacity matching, shipment exception triage, and multi-system order routing. We start with the workflow that has the most volume and the most manual firefighting attached to it — that is usually where an agent pays for itself fastest. Can an agent actually replace a dispatcher's judgment calls? + Not entirely, and we do not build it to. The agent handles the routine matching and triage (the high-volume decisions that follow a pattern) and escalates the judgment calls a dispatcher should still own. The line between the two is something we define with your operations team before we build anything. Can this integrate with our existing TMS, WMS, and carrier APIs? + Yes, through your existing system APIs and EDI feeds — we do not ask you to replace core infrastructure. The agent orchestrates across systems you already run: reading shipment status, checking carrier capacity, writing back the decision. What is the typical timeline to a working system? + A scoped Proof of Value on one workflow, against real shipment and carrier data, typically lands in around three weeks. Production rollout with monitoring and operator handoff follows the same TriStorm phases as any other engagement. What happens when the agent gets it wrong, or hits a case it has not seen? + It escalates rather than guesses. Confidence thresholds and coverage gaps route the shipment or exception to a dispatcher with the full reasoning trail attached, so nothing silently fails and every decision is reviewable after the fact. Do you have logistics-specific proof, or is this adapted from other industries? + We have not yet shipped a production agent inside a logistics-specific company. What we can point to is the same coordination pattern applied elsewhere: a multi-agent system that evaluates capacity and dispatches jobs in real time for Mixam at a 95.4% routing success rate, and Synera's production agent platform, where generating one validated multi-step workflow went from about two hours to about three minutes. The mechanics (real-time constraint checking, multi-step orchestration, human escalation) transfer directly to load matching and shipment routing. Start with one workflow ## Map one routing or exception workflow worth automating A 30-minute call identifies your system boundaries, escalation rules, and a realistic path to a working agent your dispatch team will trust. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in manufacturing: five use cases for SMBs URL: https://vstorm.co/agentic-ai/agentic-ai-in-manufacturing-five-use-cases-that-deliver-measurable-results-for-mid-market-operators 79% of organisations use generative AI; under 10% scale agents. Five manufacturing use cases that reach production, and what each one requires. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI in manufacturing: five use cases for SMBs [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI in manufacturing: five use cases for SMBs 79% of organisations use generative AI; under 10% scale agents. Five manufacturing use cases that reach production, and what each one requires. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst June 3, 2026 Updated June 18, 2026 13 min read ![Agentic AI in manufacturing: five use cases for SMBs](/app/uploads/2026/06/Group-1777-4.png) On this page0% 1. [What agentic AI does that standard automation cannot](#what-agentic-ai-does-that-standard-automation-cannot) 2. [Use case 1: Predictive maintenance](#use-case-1-predictive-maintenance) 3. [Use case 2: Quality control and visual inspection](#use-case-2-quality-control-and-visual-inspection) 4. [Use case 3: Supply chain and inventory management](#use-case-3-supply-chain-and-inventory-management) 5. [Use case 4: Production scheduling and energy optimisation](#use-case-4-production-scheduling-and-energy-optimisation) 6. [Use case 5: Compliance documentation and regulatory reporting](#use-case-5-compliance-documentation-and-regulatory-reporting) 7. [What the highest-ROI deployments have in common](#what-the-highest-roi-deployments-have-in-common) 8. [What is agentic AI in manufacturing?](#what-is-agentic-ai-in-manufacturing) 9. [Frequently asked questions](#frequently-asked-questions) 10. [Conclusion](#conclusion) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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Yet fewer than 10% are scaling agents in any function. In manufacturing, that gap is not an absence of problems to solve. It is an absence of production-ready implementations. The processes are there. Mid-market manufacturers often already have what agentic AI needs to deliver operational efficiency gains in manufacturing. They have sensor data, ERP records, and quality logs. The market reflects the underlying opportunity. [Mordor Intelligence](https://www.mordorintelligence.com/industry-reports/agentic-artificial-intelligence-in-manufacturing-and-industrial-automation-market) values the agentic AI in manufacturing and industrial automation market at USD 5.5 billion in 2025. It is projected to reach USD 16.79 billion by 2030, growing at a CAGR of 25.01%. The driver of that growth is real-world deployments already in production. Victor Reyes is the Managing Director of Deloitte's Human Capital unit. He summed up the opportunity with a question. "What would you do if you could hire 1,000 more people to run this organisation? Well, that is the kind of impact that you can have with agentic AI." ( [Manufacturing Dive, April 2026](https://www.manufacturingdive.com/news/agentic-ai-potential-rattle-manufacturing-status-quo-deloitte/816325/)) This article covers five agentic AI use cases in manufacturing. It shows evidence-based results for mid-market operators. The use cases are predictive maintenance, quality control, supply chain management, production scheduling, and compliance reporting. For each, we describe the current state, what the agent does, and what the numbers show. ## What agentic AI does that standard automation cannot [#](#what-agentic-ai-does-that-standard-automation-cannot) Traditional automation, including robotic process automation, follows fixed rules applied to a single data source. The automation performs a predetermined action when the right condition is met. That works well for linear, repetitive processes. It fails when conditions change, when decisions need data from multiple systems, or when the process needs judgment. A rule set cannot predict every case. An agentic AI system plans. It retrieves information from many sources at once. It reasons across the combined data. It decides on an action, executes it, and, if conditions change, it replans. This architecture makes AI agent integration in manufacturing systems viable for mid-market processes. These processes include cross-department workflows, multiple data sources, real-time variability, and clear success metrics. [Deloitte's 2025 Smart Manufacturing Survey](https://www.deloitte.com/us/en/insights/industry/manufacturing/2025-smart-manufacturing-survey.html) of 600 executives confirms the trend. It shows that 80% plan to invest 20% or more of their improvement budgets. It also finds that 92% expect smart manufacturing to drive competitiveness over the next three years. The manufacturers pulling ahead are not adding AI as an overlay on existing processes. They are rebuilding workflows around what agents can now execute autonomously. ## Use case 1: Predictive maintenance [#](#use-case-1-predictive-maintenance) ### How it is handled today Most mid-market manufacturers use time-based maintenance schedules. Parts are replaced or equipment is serviced at fixed intervals. This happens regardless of the equipment's actual condition. When a machine breaks without warning, maintenance engineers act quickly. They log a work order in the ERP. They reduce disruption to the production schedule. The time between an anomaly appearing in sensor data and taking corrective action is usually hours or days. ### What the agent does A predictive maintenance agent monitors equipment sensor data continuously: vibration, temperature, pressure, current draw. It detects early signs of failure. It automatically creates work orders. It updates the ERP and inventory with the parts needed. It alerts engineers only when necessary. It reschedules planned maintenance dynamically around live production commitments, so servicing does not create a secondary bottleneck. ### What the evidence shows [McKinsey research on manufacturing analytics](https://www.mckinsey.com/capabilities/operations/our-insights/manufacturing-analytics-unleashes-productivity-and-profitability) shows predictive maintenance often cuts machine downtime by 30–50%. It also increases machine life by 20–40%. [A separate McKinsey commentary on digital reliability transformations](https://www.mckinsey.com/capabilities/operations/our-insights/digitally-enabled-reliability-beyond-predictive-maintenance) found maintenance costs could drop by 18–25%. It also found asset availability could rise by 5–15%. Deloitte's 2025 survey reports that manufacturers embracing AI-driven smart manufacturing see 10–20% higher production output. They also see 7–20% higher staff productivity. ## Use case 2: Quality control and visual inspection [#](#use-case-2-quality-control-and-visual-inspection) ### How it is handled today Quality inspection is conducted by trained human inspectors working in shifts, applying sampling protocols rather than 100% coverage. Inspectors flag defects visually. They log findings in a quality management system and escalate non-conformances manually. Fatigue reduces consistency across shifts. Sampling methods can miss small defect patterns. These patterns often appear only in totals. By then, rework or scrap costs may have grown. ### What the agent does An AI-powered quality control agent deploys computer vision to inspect every unit at production speed. It combines visual findings with sensor data and production settings. It classifies defects by type and severity. It automatically adjusts production settings within defined limits. It builds a continuous improvement loop. It links defect patterns with upstream causes. When the agent finds a root cause, not just a symptom, it shares the finding with production engineers to review. ### What the evidence shows Foxconn deployed its NxVAE AI inspection system across several production lines. It improved reporting accuracy from 95% to 99%. It also reduced operating costs for appearance defect inspection by at least one third. ( [Foxconn press release](https://www.foxconn.com/en-us/press-center/events/csr-events/533)) Industry benchmarks in manufacturing AI research show that AI inspection can cut defect rates by 30–50%. This also reduces scrap and warehousing costs. The consistency advantage is structural, which is especially clear in agentic AI for discrete manufacturing. An AI inspection system performs the same across all shifts. It avoids fatigue-driven variation that affects human inspection at scale. ## Use case 3: Supply chain and inventory management [#](#use-case-3-supply-chain-and-inventory-management) ### How it is handled today Mid-market manufacturers manage supply chain exceptions through a combination of ERP alerts and manual intervention. A procurement exception appears in the system. A planner reviews it and escalates it. The planner schedules a call, negotiates an alternative and updates the purchase order. The lag between identifying an anomaly and implementing a corrective action can run to hours or days. To absorb this lag, inventory buffers are set conservatively. This ties up working capital in stock. The stock exists to cover slow processes, not true demand uncertainty. ### What the agent does A supply chain agent ingests purchase orders, supplier performance data, logistics feeds, inventory levels, and production schedules simultaneously. It detects supply disruptions before they happen. It autonomously identifies alternative sourcing options within pre-approved parameters. It re-routes shipments. It adjusts production sequencing based on material availability. It communicates updated ETAs downstream. The agent escalates to human planners only for decisions that exceed the agent's defined authority. ### What the evidence shows An [ABI Research](https://www.abiresearch.com/blog/artificial-intelligence-ai-in-supply-chain-survey-results) team surveyed 490 supply chain professionals across the US, Mexico, Germany, and Malaysia (October 2025). They found that 76% see autonomous agents as viable for handling reordering and shipment rerouting. A further 64% rate AI capability as either important or very important when evaluating new supply chain technology. [Gartner predicts](https://www.gartner.com/en/newsroom/press-releases/2025-05-21-gartner-predicts-half-of-supply-chain-management-solutions-will-include-agentic-ai-capabilities-by-2030) that by 2030, 50% of cross-functional supply chain management solutions will use intelligent agents. These agents will execute decisions on their own. Kaitlynn Sommers is a Senior Director Analyst at Gartner's Supply Chain practice. She called this shift "a revolution from robotic process automation." She said agents learn from real-time data. They also adapt to changing conditions. They do not just follow predefined scripts. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Use case 4: Production scheduling and energy optimisation [#](#use-case-4-production-scheduling-and-energy-optimisation) ### How it is handled today Operations teams using ERP or MES systems manage production scheduling at the mid-market level. Teams also use spreadsheets to make manual adjustments. When a machine breaks down, a rush order arrives, or a material delivery is late, a planner recalculates schedules. They do this manually across systems, with limited real-time visibility. The rescheduling cycle takes hours, during which the shop floor operates on an outdated plan. Energy consumption is monitored at billing cycle intervals, not in real time. ### What the agent does A production scheduling agent ingests orders, machine availability, maintenance windows, material status, workforce data, and, in energy-intensive environments, live tariff data simultaneously. It produces an optimised schedule and re-optimises automatically when any variable changes. In settings with high energy costs, such as steel, cement, glass, or food processing, the agent shifts flexible loads. It moves them to lower-tariff periods as part of its scheduling logic. The principle of replacing manual, multi-step coordination with agent-driven execution applies beyond the factory floor. [Our work with Synera](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) shows how removing the manual coordination layer from an agentic platform reduced engineering task time from hours to seconds. Production scheduling follows the same pattern. The bottleneck is not the decision itself but the manual work required to reach it. ### What the evidence shows [McKinsey's Manufacturing Lighthouses research](https://www.mckinsey.com/capabilities/operations/our-insights/how-manufacturings-lighthouses-are-capturing-the-full-value-of-ai) (2024) documents Ingrasys increasing forecast accuracy by 27% over three years. The same research reports improvements of 20–60% across critical production KPIs. These include throughput, quality, and delivery, at facilities that scaled AI past the pilot stage. [McKinsey research on production scheduling via digital twins](https://www.mckinsey.com/capabilities/operations/our-insights/digital-twins-the-next-frontier-of-factory-optimization) documents an industrials operator that redesigned its production schedule using AI. The result was a 5–7% monthly cost saving from compressed overtime requirements. [BCG's December 2025 research on agentic deployment](https://www.bcg.com/publications/2025/agents-accelerate-next-wave-of-ai-value-creation) found that AI tools can cut costs by up to 30% and lift productivity by 25%. As reuse of agentic components scales, time to market can improve by 50%. ## Use case 5: Compliance documentation and regulatory reporting [#](#use-case-5-compliance-documentation-and-regulatory-reporting) ### How it is handled today Compliance teams at mid-market manufacturers maintain regulatory documentation manually. This means: * Collecting data from production systems. * Validating the data against requirements. * Preparing structured reports. * And submitting them on fixed schedules. When regulations change, updating internal frameworks is slow and resource-intensive. Audit preparation is typically reactive. For manufacturers operating across multiple jurisdictions, this process compounds with each additional regulatory framework added. ### What the agent does A compliance agent monitors production, environmental, and operational data continuously. It identifies compliance exceptions in real time and generates audit-ready documentation automatically. Each report is time-stamped and structured. When regulatory updates are ingested, the agent adjusts its reporting logic. This reduces the lag between a regulation changing and internal processes reflecting that change. Compliance staff are escalated to only for situations that require novel interpretation or cross-jurisdictional judgment. ### Why this matters now US agencies introduced 59 new AI-related regulations in 2024. This is double the number from 2023, according to [Boomi's regulatory analysis](https://boomi.com/blog/agentic-ai-compliance/). [McKinsey's State of AI 2025](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) found that organisations perform an average of four AI-related risks per day. This compared with two per day in 2022. This means compliance risk is growing faster than mitigation capability. For EU-based mid-market operators, the EU AI Act and incoming CSRD sustainability reporting requirements create concrete, dated obligations well-implemented agentic systems can address directly. ## What the highest-ROI deployments have in common [#](#what-the-highest-roi-deployments-have-in-common) Across the five use cases above, the implementations that reach production and sustain results share four characteristics. They start with processes that are high-frequency, draw from multiple data sources, and have measurable success criteria. These are not one-off analytical tasks. But are operational workflows that run daily and where the cost of slowness or error is visible in the P&L. They treat integration with existing infrastructure, including ERP, MES, and IoT sensors, as a first-class engineering requirement. An agent that cannot read from and write to existing and legacy systems which the plant already depends on is a proof of concept. They include human-in-the-loop checkpoints for high-stakes decisions rather than full autonomy from day one. [McKinsey's research](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) shows that high performers are nearly three times more likely to have fundamentally redesigned workflows around AI. Those that treat AI as an overlay on existing processes fall behind. They are built to be owned. Clients who reach sustained operational value are those whose internal teams understand the system, can maintain it, and extend it without returning to an implementation partner for every change. At Vstorm, our [TriStorm methodology](/tristorm/) structures this journey in three phases: Transformation Consulting identifies the right use cases and builds the business case. Technology Consulting blueprints the integration with the client's specific infrastructure. Agentic AI Engineering builds, deploys, and instruments the production system. The goal is a continuous partnership from roadmap to deployment. With no handoff gap between the strategy and the build. ## What is agentic AI in manufacturing? [#](#what-is-agentic-ai-in-manufacturing) Agentic AI in manufacturing refers to AI systems that plan, act, and adapt autonomously across production workflows. They draw from multiple data sources, including equipment sensors, ERP systems, quality databases, and supply chain feeds. Unlike rule-based automation, which executes predetermined instructions, an agent reasons across live operational data. It makes decisions within defined guardrails and adjusts its behaviour as conditions change. Agentic AI applications in manufacturing span five core domains across both discrete manufacturing and process industries: predictive maintenance, quality control and visual inspection, supply chain and inventory management, production scheduling and energy optimisation, and regulatory compliance. Production deployments are delivering measurable results. These include 30–50% reductions in unplanned machine downtime (McKinsey), 18–25% reductions in maintenance costs (McKinsey), and significant improvements in forecast accuracy and defect detection at manufacturers including Foxconn and Ingrasys. ## Frequently asked questions [#](#frequently-asked-questions) ### What is the difference between agentic AI and traditional automation in manufacturing? Traditional automation, including RPA, follows fixed rules applied to a single data source and executes a predetermined action when a condition is met. An agentic AI system plans across multiple data sources. It reasons about the best course of action, executes autonomously, and adapts when circumstances change. In practice, traditional automation handles predictable, linear processes well. Agentic AI handles the cross-departmental, multi-variable workflows that define most mid-market manufacturing operations. ### Which manufacturing processes are best suited for agentic AI? The processes with the highest ROI are high-frequency, draw from multiple data sources, and have clear success metrics. Predictive maintenance, quality inspection, supply chain exception management, and production scheduling all fit this profile. Compliance reporting is a strong early-mover use case for manufacturers in regulated environments, particularly those facing EU AI Act or CSRD requirements. ### How long does it take to see ROI from agentic AI in manufacturing? This depends on the use case and the state of existing data infrastructure. Predictive maintenance typically shows measurable results within 12–24 months. Quality control and supply chain deployments that start with clean, well-structured data can generate returns faster. The critical factor is not the model but the integration quality with existing systems. ### Can mid-market manufacturers implement agentic AI without a large IT team? Yes, provided the AI implementation partner handles the integration engineering and knowledge transfer is built into the delivery. Mid-market manufacturers are often better positioned than large enterprises. Decision-makers are closer to operations, procurement cycles are shorter, and existing infrastructure is typically sufficient for the highest-value use cases. ### How does agentic AI integrate with existing ERP and MES systems? Integration is handled through APIs and event streams, allowing the agent to observe and recommend before it executes. Once accuracy is established in production, agents can act through controlled write-backs to ERP, MES, or WMS systems with full audit trails. The integration design is one of the three disciplines covered in Vstorm's Technology Consulting track within our [TriStorm methodology](/tristorm/). ### What is the risk of agentic AI making incorrect decisions on the production floor? The risk is managed through graduated autonomy. Agents begin in shadow mode, recommending rather than acting. They earn expanded autonomy as accuracy is validated in production. Human-in-the-loop checkpoints are built in for high-stakes decisions. Observability tooling that monitors every agent step is a production requirement, not an optional feature. Deployments built without this infrastructure are not production systems. ### How does agentic AI support regulatory compliance in manufacturing? A compliance agent continuously monitors operational data against regulatory requirements, generates audit-ready documentation automatically, and adjusts its reporting logic when regulations change. For manufacturers operating under EU CSRD, ISO 14001, or multi-jurisdiction export requirements, this replaces a reactive, labour-intensive process with one that is continuous and audit-ready by default. ### What is the first step for a mid-market manufacturer considering agentic AI? The first step is not to select a technology. It is to identify the operational processes where the cost of slowness or error is highest and the data to support autonomous decision-making already exists. This discovery work maps current-state processes, identifies automation candidates, and models ROI per use case. It is the starting point for every engagement we take on. ## Conclusion [#](#conclusion) The evidence for agentic AI in manufacturing is no longer a projection. It is a documented operational record. Deployments past the pilot stage show 30–50% reductions in unplanned machine downtime, a one-third reduction in defect inspection costs at Foxconn, a 27% improvement in forecast accuracy at Ingrasys, and measurable cost and productivity gains in scheduling and compliance. The execution gap remains significant. Fewer than 10% of organisations are scaling AI agents, despite near-universal awareness of the technology. In mid-market manufacturing, that gap is where the competitive opportunity lives. The infrastructure is already in place. The processes are complex enough to exceed what off-the-shelf tools can handle, and structured enough in their decision logic to be viable targets for agentic automation. The organisations that close the gap between awareness and production deployment over the next 12 to 24 months will hold compounding competitive advantages. The difference between a pilot and a production system is not the model. It is the process architecture, the integration quality, and the discipline to build for the operations team to own what is delivered. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![How Strukto.ai built Mirage's Pydantic AI integration on Vstorm's pydantic-ai-backend](/app/uploads/2026/06/Group-1777-5.png) ### How Strukto.ai built Mirage's Pydantic AI integration on Vstorm's pydantic-ai-backend Read article ](/agentic-ai/how-strukto-ai-built-mirages-pydantic-ai-integration-on-vstorms-pydantic-ai-backend/)[ ![AI compliance in manufacturing: what mid-market operators need to know about ISO 42001 and the EU AI Act](/app/uploads/2026/06/Group-1777-3.png) ### AI compliance in manufacturing: what mid-market operators need to know about ISO 42001 and the EU AI Act Read article ](/agentic-ai/ai-compliance-in-manufacturing-what-mid-market-operators-need-to-know-about-iso-42001-and-the-eu-ai-act/)[ ![Why most agentic AI projects fail before a single line of code is written](/app/uploads/2026/03/95012D22-051B-4AA2-B47D-51FC15D1C707_1_105_c-768x512.jpeg) ### Why most agentic AI projects fail before a single line of code is written Read article ](/agentic-ai/why-most-agentic-ai-projects-fail-before-a-single-line-of-code-is-written/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in media URL: https://vstorm.co/agentic-ai/agentic-ai-in-media Agentic AI for media and publishing — multilingual RAG chatbots, automated source monitoring, and content retrieval agents. [Home](/)/[Industries](/industries/)/Agentic Ai In Media # Agentic AI in media Agents that read and answer across languages and sources. We build agents that monitor unstructured content at scale and answer reader or trainee questions grounded in your own material — in more than one language when the audience needs it. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) source feeds EN AR FR EN ES covered AR EN water rights archive digest 1 AR 2 EN 01 Monitor 02 Match 03 Digest Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Media operations run on more content than any team can read Monitoring thousands of sources, answering reader questions accurately, and supporting training at scale are all retrieval problems: a large body of unstructured content and a stream of specific questions that need a sourced, current answer. Keyword tools and static FAQ pages do not scale with the volume. We start by mapping which questions the agent can answer directly from your content, and where a human editor or trainer should stay in the loop. Sources Agent Outcomes Retrieval &response agent News & sourcefeeds Editorial archive Reader questions Alert / digest Reader answer Audit log every answer traces to a retrieved source Use cases ## Where agents earn trust in media operations High-volume, unstructured-content workflows with a real audience on the other end. 01 ### Automated source monitoring An agent scrapes and reasons over unstructured data from thousands of news sources, surfacing what matters instead of every keyword match. 02 ### Multilingual reader & trainee support A RAG-based chatbot answers questions in multiple languages, grounded in your editorial or training material — not a generic translation layer. 03 ### Editorial knowledge-base retrieval Agents answer from your archive with sourced, auditable citations rather than a generic model's general knowledge. 04 ### Content pipeline automation Coordinates multi-step editorial or distribution workflows across systems, escalating judgment calls to a human editor. The cost of unanswered questions ## What changes when an archive can answer for itself These numbers come from real shipped agentic AI engagements. ARIJ Network's is a direct media deployment — investigative-journalist training, English and Arabic, answered from ARIJ's own knowledge base. The Mixam and Synera figures are not media; they are here because they measure the multi-tool orchestration and multi-step validation an editorial retrieval agent runs on. Every figure below links to the case study behind it. Agents do not decide what gets published or what an editor puts their name to. They retrieve, cross-check and draft what sits before that decision. Every source it used is logged. 95.4% ### Success rate in workflow results Not a media deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) 2 hrs → 3 min ### to generate a validated workflow Not a media deployment — Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) Client results ## Proof from a production media deployment ARIJ Network is direct media proof — a bilingual English/Arabic agent answering investigative journalists from ARIJ's own knowledge base across 22 countries. Mixam and Synera are not media deployments; they are here because they measure the same multi-tool orchestration and multi-step validation an editorial retrieval agent depends on. [ ### Multilingual AI agent-powered chatbot supporting journalist training A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic. EN + ARone agent answering in English and Arabic from ARIJ's own materials 2languages, English and Arabic, from one knowledge base Read case study ![](/_astro/The-LLM-Book-Vstrom-2.CDz2_UY9.png) ](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production agent TriStorm keeps content accuracy and engineering aligned — retrieval quality questions surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Map content and workflow We audit target processes, content sources, and language requirements — ranking automation candidates by reach and impact. * Content & workflow audit * Language requirements review * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We implement against your real editorial content, with an evaluation suite scored for accuracy before any output reaches a reader. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout with monitoring and audit logging, plus a structured handoff so your editorial team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own editorial and archive workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in media, answered All FitUse casesDeliveryOperations Where does agentic AI fit into media and publishing operations? + In workflows that need to monitor, retrieve, or respond across large volumes of unstructured content — source monitoring, multilingual reader support, and training or onboarding content that needs to answer real questions, not just serve static pages. How is this different from a keyword-based news monitoring tool? + A keyword tool matches strings. An agent reads and reasons over the content it retrieves — the same mechanism we used to build a platform scraping unstructured data from thousands of news sources using LLMs, LangChain, and LlamaIndex. Can an agent support readers or trainees in multiple languages? + Yes — we built a RAG-based agentic chatbot in English and Arabic for ARIJ Network, supporting investigative-journalist training at scale across 22 countries. What other media workflows suit an agent? + Automated data scraping and monitoring across thousands of sources, multilingual content support, and retrieval-grounded answers over a large editorial knowledge base. What is the typical timeline to a working system? + A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks, following the same TriStorm phases as any Vstorm engagement. Do we own the system after it is built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in. Start with one workflow ## Map one content workflow worth automating A 30-minute call identifies the content sources, language needs, and a realistic path to a working agent. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in real estate URL: https://vstorm.co/agentic-ai/agentic-ai-in-real-estate Agentic AI for real estate — RAG-powered due diligence agents that extract, classify, and surface critical information from property and legal documents. [Home](/)/[Industries](/industries/)/Agentic Ai In Real Estate # Agentic AI in real estate Due diligence documents, read and cross-referenced automatically. We build agents that extract, classify, and surface critical information from property and legal documents — the same staged retrieval-and-validation pattern that eliminated hallucinations in Schmitt-Thompson Clinical Content's clinical-guideline RAG system. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) diligence agent title record parcel owner area easement property doc easement mismatch title p. 14 doc p. 3 counsel decides 01 Extract 02 Cross-check 03 Flag to counsel Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Due diligence is a document-reading bottleneck, not a judgment bottleneck Reviewing property and legal documents for the details that matter (encumbrances, zoning constraints, title issues) takes time because someone has to read every page. An agent extracts and classifies that content, cross-references it against the rest of the file, and surfaces the specific passages a reviewer needs to make the judgment call. We start by mapping which document types the agent can process directly and where a reviewer's sign-off stays required. Sources Agent Outcomes Due diligence agent Property documents Legal filings Title records Reviewer summary Documentclassification Audit log every extracted fact traces to its source document Use cases ## Where agents earn trust in real estate operations Document-heavy workflows with a clear reviewer sign-off. 01 ### Due diligence document review Extracts, classifies, and surfaces critical information from property and legal documents using specialized OCR and vector search. 02 ### Lease and contract review Reads lease terms and flags clauses that need attention, with reasoning attached for the reviewing attorney or analyst. 03 ### Portfolio document search Answers specific questions across a large set of unstructured filings — sourced to the actual document, not a generalized summary. 04 ### Property document classification Automatically sorts and tags incoming documents by type, routing each to the right workflow without manual triage. What the mechanism delivers ## Document-grounded reasoning, measured in production The first two figures are ours from real estate: Mapline.AI, where due diligence on a property ran for weeks before we built a document agent on advanced RAG, specialized OCR and vector search. The third is ARIJ Network, media, and it is here because it measures the thing every title and lease workflow turns on — retrieval that answers only from the client's own documents. Every figure links to the case study behind it. Agents do not price a property, sign a lease, or clear a title. They read the documents, tie every extracted fact back to the page it came from, and draft what sits before a broker's or attorney's decision — with each step logged for review. Manual due diligence With agents in production ### Per-property due diligence, before the agent weeks Analysts read title documents, surveys and planning records by hand for every property under consideration. [Mapline.AI case study (real estate)](/case-study/advanced-rag-engineering-for-real-estate-due-diligence-ai-agent/) ### With a document agent in production minutes Advanced RAG, specialized OCR and Milvus vector search over the property's own document set — every extracted fact traceable to its page. [Mapline.AI case study (real estate)](/case-study/advanced-rag-engineering-for-real-estate-due-diligence-ai-agent/) $2k–3k ### Saved per due diligence project Not a projection — the published per-project saving from the Mapline.AI engagement. [Mapline.AI case study (real estate)](/case-study/advanced-rag-engineering-for-real-estate-due-diligence-ai-agent/) EN + AR ### one agent answering in English and Arabic from ARIJ's own materials Not a real-estate deployment — A bilingual English/Arabic autonomous agent embedded in ARIJ's Moodle environment, answering only from ARIJ's own knowledge base. [ARIJ Network case study (media)](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) Client results ## Proof from production agentic deployments Our real-estate work is Mapline.AI (due diligence from weeks to minutes, $2k–3k saved per project), and Guesthook, where LLMs generate property descriptions for a vacation-rental marketing agency. The studies below are the ones published end to end with the full mechanism written up: the same grounded retrieval, multi-tool orchestration and multi-step validation. [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ### Multilingual AI agent-powered chatbot supporting journalist training A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic. EN + ARone agent answering in English and Arabic from ARIJ's own materials 2languages, English and Arabic, from one knowledge base Read case study ![](/_astro/The-LLM-Book-Vstrom-2.CDz2_UY9.png) ](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production agent TriStorm keeps document accuracy and engineering aligned — extraction and review questions surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Map document types and workflow We audit target document sets, review requirements, and data access — ranking automation candidates by volume and impact. * Document & workflow audit * Data access map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We implement against real property and legal document shapes, with an evaluation suite scored before any output reaches a reviewer. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout with monitoring and audit logging, plus a structured handoff so your team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own listing and transaction-document workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in real estate, answered All FitUse casesDeliveryOperations Where does agentic AI fit into real estate operations? + Due diligence is the clearest case: reviewing property and legal documents for critical information is a retrieval-and-extraction problem, which is what an agent grounded in your own document set handles well. We have not yet shipped a real-estate-specific deployment; the closest published proof is Schmitt-Thompson Clinical Content's agentic RAG system, validated to 98% accuracy on the open 50-scenario expert benchmark — the same staged retrieval-and-validation mechanism due diligence review needs. How is this different from OCR software we already use? + OCR extracts text. An agent reads the extracted text, classifies it, cross-references it against other documents, and surfaces the specific information a reviewer needs — the reasoning layer on top of extraction, not the extraction itself. Do you have real-estate-specific proof, or is this adapted from other industries? + Real-estate-specific. For Mapline.AI we built a due diligence agent on advanced RAG, specialized OCR and Milvus vector search: analysis that took weeks now runs in minutes, at a published saving of $2k–3k per project. For Guesthook, a vacation-rental marketing agency, we automated property description generation with LLMs. Where we reach outside the industry is for the validation mechanism — Schmitt-Thompson Clinical Content's agentic RAG system hit 98% accuracy on the open 50-scenario expert benchmark, and retrieve-cross-reference-validate is the same discipline a title or lease review needs. What other real estate workflows suit an agent? + Lease and contract review, property document classification, and portfolio-level document search across large sets of unstructured filings. Can this integrate with our existing document management system? + Yes, through your existing APIs and storage — no need to migrate your document archive to a new system. Do we own the system after it is built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in. Start with one workflow ## Map one document-review workflow worth automating A 30-minute call identifies the document types, review requirements, and a realistic path to a working agent. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in smart city URL: https://vstorm.co/agentic-ai/agentic-ai-in-smart-city Agentic AI for smart-city and urban-infrastructure operations — auditable agents for citizen-request routing, permitting review, and infrastructure monitoring. [Home](/)/[Industries](/industries/)/Agentic Ai In Smart City # Agentic AI in smart city Agents for the coordination layer of urban operations. We build agents that route citizen requests, review permitting documents, and monitor infrastructure signals — with the audit trail a public-sector team can actually trust. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Streetlight out, Elm St Roads Lighting Water Permits case 3318 reason attached pole sensor no power 01 Request 02 Check 03 Route Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Urban operations run across systems that do not talk to each other Citizen requests, permitting records, and infrastructure sensor data typically live in separate systems, and resolving any one case means checking more than one of them. An agent reads across those sources, drafts a routing or resolution decision, and escalates the genuine edge cases — the same mechanism that works for any multi-system, document-heavy operation. We map which decisions the agent can complete directly, where a case officer needs to sign off, and how every action gets logged. Sources Agent Outcomes Coordination agent Citizen requests Permittingdocuments Sensor data Case system Officer review Audit log every routing decision traces to its source data Use cases ## Where agents earn trust in urban operations Multi-system workflows with a clear escalation path to a case officer. 01 ### Citizen-request triage and routing Reads incoming requests and routes them to the right department with reasoning attached, instead of a generic ticket queue. 02 ### Permitting document review Cross-checks permit applications against zoning and code requirements, drafting a sourced summary for the reviewing officer. 03 ### Infrastructure sensor monitoring Reads sensor and maintenance data across city infrastructure, flagging issues before they escalate into service failures. 04 ### Compliance record-keeping Maintains sourced, auditable records across departments for regulatory and public-records review. What the mechanism delivers ## Cross-system coordination that leaves a record None of these figures come from a municipality. We have not shipped a production agent inside a city administration or a smart-city program, and nothing below pretends otherwise. They come from Synera (engineering software), Mixam (print on demand) and Schmitt-Thompson (healthcare), and they are here because they measure the three things a municipal workflow actually depends on: orchestration across systems that were never built to talk, real-time evaluate-and-route across a distributed operation, and staged validation where a wrong answer lands on a person. Every figure links to the case study behind it. The agent does not approve a permit, close a citizen case, or dispatch a crew. It reads across the systems, drafts the decision with its sources attached, and hands it to the officer who signs off — and because public-sector accountability means the record matters as much as the outcome, every retrieval, draft and escalation is logged for later review. Manual workflow setup With agents in production ### Manual setup per multi-step workflow 2 hrs Not a smart-city deployment — engineers on Synera's platform assembled each complex workflow by hand, node by node. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) ### With a text-to-workflow agent in production 3 min Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) 95.4% ### Success rate in workflow results A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) 44% → 98% ### raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. [Schmitt-Thompson case study (healthcare)](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Client results ## Proof from production agentic deployments We have not shipped a production agent inside a municipality or a smart-city program. These are the closest available references — the same cross-system orchestration, real-time routing, and staged validation with a full audit trail, shipped in engineering software, print on demand and healthcare. [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production agent TriStorm keeps public-sector compliance and engineering aligned. real inputs adversarial latency VALIDATING go / no-go 1 ### Map systems and workflow We audit target processes, department-system boundaries, and data access — ranking automation candidates by impact and integration risk. * Systems & workflow audit * Data access map * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We implement against real municipal data shapes, with an evaluation suite scored before any output reaches a case officer. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout with monitoring and audit logging, plus a structured handoff so your team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own service-request and permitting workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in smart city, answered All FitUse casesDeliveryOperations Where does agentic AI fit into municipal and urban-infrastructure operations? + In workflows that coordinate across multiple city systems (permitting, citizen requests, infrastructure sensor data) where a decision benefits from cross-referencing more than one source before a case officer signs off. How is this different from the case-management software cities already use? + Case-management software routes a ticket to a queue. An agent reads the request alongside relevant records and systems, drafts a resolution or routing decision with reasoning attached, and escalates genuine edge cases instead of routing everything to a person. What municipal workflows are realistic first projects? + Citizen-request triage and routing across departments, permitting document review, and infrastructure sensor-data monitoring for proactive maintenance flags. Can an agent handle citizen data responsibly? + Yes — agents operate within your existing access controls and audit requirements, with every decision logged and reasoning attached, designed for public-sector review from day one. What is the typical timeline to a working system? + A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks, following the same TriStorm phases as any Vstorm engagement. Do we own the system after it is built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in, with your team trained to operate and extend it. Start with one workflow ## Map one urban-operations workflow worth automating A 30-minute call identifies integration points, data access, and a realistic path to a working agent. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in telecommunications URL: https://vstorm.co/agentic-ai/agentic-ai-in-telecommunication Agentic AI for telecommunications — auditable agents for device activation, call verification and routing, and customer support at scale. [Home](/)/[Industries](/industries/)/Agentic Ai In Telecommunication # Agentic AI in telecommunications Automate activation and routing at carrier scale. We build agents that handle device activation, call verification, and routing at volume — the same high-volume, escalation-aware coordination pattern proven in other production deployments. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) human agent order provision activate verify line live retry 1/3 audit log provision ok verify failed retry 1 ok 01 Provision 02 Retry 03 Line live Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Telecom scale breaks fixed-script automation Device activation, call verification, and routing generate volume that overwhelms manual review, but the exceptions are too varied for a fixed IVR script. An agent reads the actual request, reasons across provisioning and account systems, and completes the task end to end, escalating only genuine exceptions. We start by mapping which requests the agent can resolve directly and which need a human — with every decision logged. Sources Agent Outcomes Activation &routing agent Activationrequests Call transcripts Account data Provisioningsystem Human agent Audit log every routing decision traces to account data Use cases ## Where agents earn trust in telecom operations High-volume workflows with a clear escalation path. 01 ### Device activation Reads activation requests and provisioning data, completing device setup end to end without a person keying in every step. 02 ### Call verification and routing A voice assistant verifies caller intent and routes to the right queue or resolution, handling higher volume without added headcount. 03 ### Network exception coordination Cross-references signals across provisioning and account systems to resolve error cases instead of routing every exception to a technician. 04 ### Customer support with account context Answers billing and service questions grounded in the actual customer's account and provisioning status. What the mechanism delivers ## Evaluate-and-route at volume, measured in production The first two figures are from a telecom deployment: a US fiber operator serving 150,000+ households, where device activation ran through three support agents on the phone for every field installation. We cannot name the operator publicly. The third is Mixam, print on demand, and it is here because it measures the same evaluate-and-route mechanism at a volume no telecom workflow escapes. Every figure links to the case study behind it. Agents do not change a subscriber's plan, credit an account, or touch network configuration on their own. They read the request, check it against the systems of record, draft the resolution, and log every step for the person who signs off. Manual activation support With agents in production ### Daily error analysis and processing, before the agent 330 min Every field installation routed through a support center where three agents manually worked activation across multiple systems — labor cost on every job, and a bottleneck that blocked expansion into other states. [US telecom case study](/case-study/intelligent-automation-with-actionable-ai-agents-for-the-us-telecommunication-company/) ### After the activation agent went live 30 min A 10× efficiency improvement. The agent ingests ticket data, device telemetry and account context, then recommends the next best action in minutes. [US telecom case study](/case-study/intelligent-automation-with-actionable-ai-agents-for-the-us-telecommunication-company/) 98% ### Automation of device activation workflows Routine activation work was nearly eliminated, and call-center agents were redeployed to higher-value work rather than cut. [US telecom case study](/case-study/intelligent-automation-with-actionable-ai-agents-for-the-us-telecommunication-company/) 95.4% ### Success rate in workflow results Not a telecommunications deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) Client results ## Proof from production agentic deployments Our telecom work — 98% automation of device activation for a US fiber operator serving 150,000+ households, and an LLM voice assistant handling call verification and routing — is with clients we cannot name. These are the named studies you can read in full: the same evaluate-and-route, grounded-retrieval and staged-validation mechanisms, shipped in print on demand, media and healthcare. [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ### Multilingual AI agent-powered chatbot supporting journalist training A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic. EN + ARone agent answering in English and Arabic from ARIJ's own materials 2languages, English and Arabic, from one knowledge base Read case study ![](/_astro/The-LLM-Book-Vstrom-2.CDz2_UY9.png) ](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [ ![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) ### Clinical triage that executes the guideline, step by step The model answers the assessment questions. The guideline decides the disposition, the same way every time. 44% → 98%accuracy, raw LLM vs guideline-executing 94%weighted F1 across 591 scenarios Read case study ](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production agent TriStorm keeps scale and engineering aligned — integration and volume questions surfaced before full build commitment. real inputs adversarial latency VALIDATING go / no-go 1 ### Map systems and workflow We audit target processes, provisioning and CRM boundaries, and error patterns — ranking automation candidates by volume and impact. * Systems & workflow audit * Error-pattern review * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We implement against real provisioning and call data shapes, with an evaluation suite scored before production rollout. * Working prototype * Evaluation suite * Escalation rules BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout with monitoring and audit logging, plus a structured handoff so your operations team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own service-assurance and support workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in telecommunications, answered All FitUse casesDeliveryOperations Where does agentic AI fit into telecom operations? + In high-volume, document-and-decision workflows — device activation, call verification and routing, and customer support that needs account context. Device activation is where we have shipped: for a US fiber operator serving 150,000+ households, an agent that ingests ticket data, device telemetry and account context now automates 98% of activation workflows, work that previously routed every field installation through three support agents on the phone. How is this different from the IVR and automation we already run? + An IVR follows a fixed script. An agent reads the actual request or document, reasons across account and provisioning systems, and completes the task (activation, verification, routing), escalating only genuine exceptions. Do you have telecom-specific proof, or is this adapted from other industries? + Yes, though not under a name we can print. We built an activation agent for a US fiber-powered operator serving 150,000+ households across 500+ master-planned communities: 98% automation of device activation, and daily error analysis cut from 330 minutes to 30. We also built an LLM voice assistant automating call verification and routing for a call center. Both clients are anonymized, so the studies you can read end to end are outside telecom — Mixam's three-agent advisor at a 95.4% success rate, and Schmitt-Thompson's guideline-executing triage agent at 98% on the open 50-scenario benchmark. Can an agent actually handle voice-based customer interactions? + Yes, when the interaction is grounded in real account data rather than a generic script. We build this with the same staged retrieval-and-validation approach behind Schmitt-Thompson's clinical-triage agent (checking the request against source data before answering) adapted to call verification and routing. Can this integrate with our existing provisioning and CRM systems? + Yes, through your existing APIs — no rip-and-replace of your provisioning or billing infrastructure. Do we own the system after it is built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in. Start with one workflow ## Map one carrier-scale workflow worth automating A 30-minute call identifies integration points, volume patterns, and a realistic path to a working agent. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI in travel URL: https://vstorm.co/agentic-ai/agentic-ai-in-travel Agentic AI for travel and hospitality — content generation agents that turn listing and property data into publish-ready copy at scale. [Home](/)/[Industries](/industries/)/Agentic Ai In Travel # Agentic AI in travel Listing content that scales with your property catalog. We build agents that turn raw listing and booking data into accurate, publish-ready content and guest support — the same real-data-grounded generation pattern proven in other production deployments. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) listing data description EN DE ES beds 2 view sea pool heated check-in 15:00 parking — not in listing, review published 01 Listing data 02 Grounded draft 03 Publish Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Listing content does not scale with a human writer alone Property descriptions, itinerary summaries and guest-facing copy all need to be accurate to the actual listing, distinct from every other property, and produced at a volume manual writing cannot sustain. An agent reads the real listing data and generates content grounded in it, so each description reflects the property it describes. We start by mapping which content types the agent can generate directly and where a human review step stays in place. Sources Agent Outcomes Content &support agent Listing data Booking history Guest requests Published listing Guest response Audit log every description traces to real listing data Use cases ## Where agents earn trust in travel operations Content and support workflows that need to scale with your catalog. 01 ### Property description generation Turns raw listing data into publish-ready copy at scale, grounded in the actual property's details rather than generic filler. 02 ### Guest support with booking context Answers guest questions grounded in the actual booking and property data, escalating anything outside its scope. 03 ### Itinerary and amenity summarization Summarizes complex itineraries or amenity lists into guest-facing content that stays accurate as details change. 04 ### Multilingual listing content Generates or adapts listing content across languages for international travelers, grounded in the same source data. What the mechanism delivers ## Guided configuration and grounded answers, measured in production Our closest travel-adjacent work (LLM-generated listing content for the vacation-rental marketing agency Guesthook) has no published figure attached, so it is not on this list. What is here comes from Mixam (print on demand), ARIJ Network (media) and Synera (engineering software), and it measures what booking and guest-service workflows depend on: walking a customer through an option space too large to browse, answering only from the operator's own content, and validating a multi-step output instead of generating it in one pass. Every figure links to the case study behind it. Agents do not confirm a booking, issue a refund, or override a rate or availability rule. They assemble the options, ground every answer in your own listing and policy data, and draft what a guest-service or revenue reviewer signs off on — with each step logged. 95.4% ### Success rate for a production multi-agent product advisor Not a travel deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. It is the closest analog we have shipped to itinerary and booking configuration: a customer-facing agent narrowing an option space no one can browse. [Mixam case study (print on demand)](/case-study/ai-agent-for-order-recommendation-and-completion/) 2 hrs → 3 min ### to generate a validated workflow Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. [Synera case study (engineering software)](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) Client results ## Proof from production agentic deployments We have shipped listing-content generation for the vacation-rental marketing agency Guesthook, but not yet an agent inside a hotel group, airline or OTA. These are the closest published references — a customer-facing advisor guiding a person through a billion-plus option space, a bilingual agent answering only from the client's own knowledge base, and a multi-step validation loop replacing a single generation pass. [ ![Mixam](/_astro/mixam-logo.B59_GcxD.png) ### A print-order advisor that lifted conversion to a final sale Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations. ~20% → ~40%conversion to a final sale 96%+web-to-print orders handled end to end Read case study ](/case-study/ai-agent-for-order-recommendation-and-completion/) [ ### Multilingual AI agent-powered chatbot supporting journalist training A bilingual English/Arabic agent supporting ARIJ Network in training investigative journalists, answering from ARIJ's own knowledge base, in English or Arabic. EN + ARone agent answering in English and Arabic from ARIJ's own materials 2languages, English and Arabic, from one knowledge base Read case study ![](/_astro/The-LLM-Book-Vstrom-2.CDz2_UY9.png) ](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [ ![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) ### Engineering workflows built in minutes, validated before they run The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup. 2 hrsto prepare a workflow by hand 3 minto generate one with the agent Read case study ](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [View all case studies](/case-studies/) Delivery path ## From workflow audit to a production agent TriStorm keeps content accuracy and engineering aligned. real inputs adversarial latency VALIDATING go / no-go 1 ### Map content and data sources We audit target content types, listing data quality, and review requirements — ranking automation candidates by volume and impact. * Content & data audit * Data quality review * Prioritised use case outcomes use cases readiness WORKFLOWS current target ALIGNING PLAN 01 02 03 2 ### Build and validate the agent We implement against real listing and booking data, with an evaluation suite scored for accuracy before publishing. * Working prototype * Evaluation suite * Review workflow BLUEPRINT schemas agent graph reliability DESIGNING 3 ### Deploy with monitoring Production rollout with monitoring and audit logging, plus a structured handoff so your team runs the system independently. * Production deployment * Audit trail & monitoring * Operator runbook Not sure where to start? A 30-minute call is usually enough to find your highest-value use case Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own booking and guest-service workflows. Every example is something already running in production. [Book a consultation](/schedule-a-meeting/) Independence ## How we help you stay independent Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement. Technological sovereignty We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe. Small language models Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure. Open source We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source. FAQ ## Agentic AI in travel, answered All FitUse casesDeliveryOperations Where does agentic AI fit into travel and hospitality operations? + Content and listing generation at scale is the clearest case — property descriptions, itinerary summaries, and guest-facing copy that needs to be accurate and distinct across thousands of listings, not templated filler. How is this different from a template-based description generator? + A template fills in blanks. An agent reads the actual property or itinerary data and generates content grounded in those specifics — the same real-data-grounding discipline behind Mixam's three-agent product advisor, which guides customers through more than a billion possible print-order combinations at a 95.4% success rate. Do you have travel-specific proof, or is this adapted from other industries? + The closest is Guesthook, a vacation-rental marketing agency, where we automated property description generation with LLMs — listing content at volume, grounded in the real property data. We have not yet shipped an agent inside a hotel group, airline or OTA. For the coordination half we point to Mixam's three-agent product advisor, guiding customers through more than a billion possible print-order combinations at a 95.4% success rate; grounding output in real customer and catalog data at scale transfers directly to itinerary and guest-service content. What other travel workflows suit an agent? + Guest support grounded in booking and property data, itinerary or amenity summarization, and multilingual content generation for international listings. What is the typical timeline to a working system? + A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks, following the same TriStorm phases as any Vstorm engagement. Do we own the system after it is built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in. Start with one workflow ## Map one content or support workflow worth automating A 30-minute call identifies the content types, data sources, and a realistic path to a working agent. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## What agentic AI is still taken for: three lessons from Stockholm URL: https://vstorm.co/agentic-ai/agentic-ai-misconceptions-three-lessons-from-stockholm From a keynote to Nordic financial institutions: why agentic AI is not a black box, why kill switches miss the point and how accuracy can rise. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/What agentic AI is still taken for: three lessons from Stockholm [Agentic AI](/ai-blog-news/#agentic-ai) # What agentic AI is still taken for: three lessons from Stockholm From a keynote to Nordic financial institutions: why agentic AI is not a black box, why kill switches miss the point and how accuracy can rise. ![Bartosz Adam Gonczarek](https://vstorm.co/app/uploads/2025/02/2024-01-20__Y4A0464_small-1-e1738697918189.jpg) [Bartosz Adam Gonczarek](/author/bartosz-adam-gonczarek/) Chief Transformation Officer and Co-founder of Vstorm October 6, 2026 4 min read ![What agentic AI is still taken for: three lessons from Stockholm](/app/uploads/2026/10/cover-agentic-ai-misconceptions-three-lessons-from-stockholm-v3.png) On this page0% 1. [AI is seen as an abstract model, a black box at work](#ai-is-seen-as-an-abstract-model-a-black-box-at-work) 2. [Talk about kill switches steals the spotlight](#talk-about-kill-switches-steals-the-spotlight) 3. [AI cannot possibly aim at 100% accuracy](#ai-cannot-possibly-aim-at-100-accuracy) 4. [Conclusions](#conclusions) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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The two frameworks behind many production AI agents, [Pydantic AI](https://pydantic.dev/articles/pydantic-ai-v1) and [LangChain](https://www.langchain.com/blog/langchain-langgraph-1dot0), reached version 1 only in autumn 2025. At Vstorm we had been shipping agentic systems to production before that starting line. On stage I gave an engineer's view of what production-grade agentic AI actually is, and in the conversations afterwards I learned how far that view sits from the one decision-makers hold. There is less wishful thinking than a year ago, but the picture of what AI can do is still mostly off. Here is the gap between what agentic AI is and what businesses assume it to be. ![Bartosz Adam Gonczarek speaking on stage at The Agentic Economy Forum in Stockholm, with a slide about practice, research and intellectual sources behind his talk](/app/uploads/2026/10/stockholm-keynote-bartosz-gonczarek.jpg) Bartosz Adam Gonczarek at The Agentic Economy Forum in Stockholm. ## AI is seen as an abstract model, a black box at work [#](#ai-is-seen-as-an-abstract-model-a-black-box-at-work) Three years into a world with capable LLMs, most decision-makers I spoke to are still unsure what goes into agentic AI. They picture a non-deterministic language model in the middle that pulls all the strings. What they miss is that every agentic AI system combines a stochastic core with deterministic scaffolding built around it. I usually open my keynotes with a slide that separates the two: the LLM core and the deterministic shell around it. The scaffolding intercepts, structures and acts on everything that reaches the model and everything the user gets back, and engineers have full control over it. Users are not at the mercy of whatever the model returns, large or small. It is the engineer's job to validate the outcomes and protect against edge cases. ![Bartosz Adam Gonczarek pointing at a slide that shows a user, the model at the core and the harness around it, which handles the prompt going in and the response coming out](/app/uploads/2026/10/stockholm-keynote-slide-what-agentic-ai-is.jpg) The slide that separates the model at the core from the harness engineers build around it. ## Talk about kill switches steals the spotlight [#](#talk-about-kill-switches-steals-the-spotlight) Because it is still unclear what an AI system is, people tend to see these systems as uncontrollable and potentially dangerous. And what we fear and cannot control, we want a way to shut down. From an engineering perspective, this view is deeply flawed. It is as if toddlers who can barely walk were discussing the risks of high-speed skateboarding. Most production-grade systems are not autonomous in 2026. They support people by taking the mundane work off their shoulders. You do not need a kill switch for a system that processes claim documents ahead of a human decision, or for one that suggests how to allocate investment funds. These systems can make mistakes, and it is the engineers' job to guard against them with the scaffolding they build around the LLM. In our projects, what ships to production works in over 90% of cases. If a system removes manual work in 9 situations out of 10, that is a reason to celebrate, not to shut it down, and a good base for improving accuracy before anyone talks about fully autonomous processes. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## AI cannot possibly aim at 100% accuracy [#](#ai-cannot-possibly-aim-at-100-accuracy) Reasonable thinking leads to the wrong conclusion here. If AI is probabilistic at its core, the argument goes, it can never be fully reliable, because that is its nature. On our most ambitious projects at Vstorm we already see that this is not necessarily the case. The view starts to shift once the LLM is treated as one tool in the toolbox rather than the ultimate answer. Every solution we build explores a range of possible situations. If that range is mapped out and deeply understood, a classical deterministic system can work through it with a rulebook: if this happens, do that. Where that understanding is missing, an LLM does the job better, because it can move through an undefined space without a rulebook and spot patterns people may not have seen. What we have found on projects is that the longer an LLM works through such an undefined space, the better the client organisation understands it, by watching and learning from the AI at work. That creates a chance to write or update the rulebook, and for some parts of the job the solution can then fall back to deterministic systems. ![Three cards — the LLM explores undefined space (violet), the client learns by watching it (stone), a rulebook takes over the mapped range (solid teal) — above a qualitative band where the rulebook's share grows as the client learns.](/app/uploads/2026/10/fig-agentic-ai-misconceptions-three-st-sth-rulebook.png) What the LLM explores, a rulebook can later take over. Whether this works for most projects is still an open question, but some projects in the Vstorm portfolio already show the trend. We have found that 100% accuracy can be within reach, not through some superintelligent AI, but through a well-engineered solution that uses both deterministic and non-deterministic components for the problem at hand. ## Conclusions [#](#conclusions) The engineer's perspective differs from conclusions reached by reasoning alone. One thing everyone at the conference agreed on is that working on real projects is how you learn to talk about AI put to work. It is also the hardest perspective to get, because you have to take on the risks of adoption work, and hands-on work with AI does not give you the clean answers that reasoning does. In the end, though, the conclusions earned on real adoption projects are what can save others the time and money they would spend learning the same things themselves. In the keynote I also presented conclusions from projects Vstorm delivered to financial institutions. You can read them in the follow-up post, [Our findings in agentic automation from the Nordics Summit](/agentic-ai/agentic-automation-in-finance-lessons-from-the-nordics-summit/). If you work at a financial institution and want to adopt agentic AI the right way, you can [book a consultation](/schedule-a-meeting/) with Vstorm. ![Bartosz Adam Gonczarek](https://vstorm.co/app/uploads/2025/02/2024-01-20__Y4A0464_small-1-e1738697918189.jpg) [Bartosz Adam Gonczarek](/author/bartosz-adam-gonczarek/) Chief Transformation Officer and Co-founder of Vstorm [Author profile](/author/bartosz-adam-gonczarek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![AI agent evaluation and reliability](/app/uploads/2026/10/cover-ai-agent-evaluation-and-reliability-v3.png) ### AI agent evaluation and reliability Read article ](/agentic-ai/ai-agent-evaluation-and-reliability/)[ ![Our findings in agentic automation from the Nordics Summit](/app/uploads/2026/10/cover-agentic-automation-in-finance-lessons-from-the-nordics-summit-v3.png) ### Our findings in agentic automation from the Nordics Summit Read article ](/agentic-ai/agentic-automation-in-finance-lessons-from-the-nordics-summit/)[ ![Why agentic AI transformation strategy decides who wins](/app/uploads/2026/10/cover-why-agentic-ai-transformation-strategy-decides-who-wins-v3.png) ### Why agentic AI transformation strategy decides who wins Read article ](/agentic-ai/why-agentic-ai-transformation-strategy-decides-who-wins/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI revenue cycle management: from pilot to production for mid-market health systems URL: https://vstorm.co/agentic-ai/agentic-ai-revenue-cycle-management-from-pilot-to-production-for-mid-market-health-systems RCM costs US health systems over $140bn a year, yet only 20% of sub-$1bn systems pilot GenAI for it. The practical path from pilot to production. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI revenue cycle management: from pilot to production for mid-market health systems [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI revenue cycle management: from pilot to production for mid-market health systems RCM costs US health systems over $140bn a year, yet only 20% of sub-$1bn systems pilot GenAI for it. The practical path from pilot to production. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst April 3, 2026 Updated June 26, 2026 10 min read ![Agentic AI revenue cycle management: from pilot to production for mid-market health systems](/app/uploads/2026/04/Group-1777.png) On this page0% 1. [Why RCM still runs on manual labour](#why-rcm-still-runs-on-manual-labour) 2. [What agentic AI revenue cycle management actually means](#what-agentic-ai-revenue-cycle-management-actually-means) 3. [The highest-ROI starting points for mid-market health systems](#the-highest-roi-starting-points-for-mid-market-health-system) 4. [How to structure a pilot that scales](#how-to-structure-a-pilot-that-scales) 5. [The transition from RCM pilot to production](#the-transition-from-rcm-pilot-to-production) 6. [Compliance, data governance, and regulatory considerations](#compliance-data-governance-and-regulatory-considerations) 7. [What a production-grade agentic RCM deployment looks like](#what-a-production-grade-agentic-rcm-deployment-looks-like) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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This guide covers the practical path from pilot to production: where to start, how to design a pilot that scales, what the enterprise transition requires, and how to embed compliance from the start. It draws on verified data from McKinsey, Experian Health, Bain, and HFMA, alongside Vstorm's production experience in healthcare agentic AI._ Agentic AI revenue cycle management is no longer an experimental concept for US health systems. The tools exist, the ROI case is established, and the competitive gap between systems that have moved to production and those still running pilots is beginning to widen. The question for mid-market health systems, those with revenue between $25 million and $500 million, is no longer whether to adopt agentic AI in the revenue cycle, but how to move from a working proof of concept to an enterprise-grade system that operates reliably, scales across workflows, and does not require constant engineering intervention to sustain. This guide covers the practical steps to make that transition. ## Why RCM still runs on manual labour [#](#why-rcm-still-runs-on-manual-labour) Revenue cycle management is, at its foundation, an information processing problem which most health systems still solve with people. Billing teams manually verify patient eligibility through payer portals, chase prior authorisation approvals, correct coding errors after the fact, and follow up on denied claims individually. The result is a process that is expensive, slow, and highly sensitive to staffing levels. The financial exposure is significant. Health systems collectively spend more than $140 billion annually on RCM, with the process typically consuming 3–4% of revenue at scale ( [McKinsey, January 2026](https://www.mckinsey.com/industries/healthcare/our-insights/agentic-ai-and-the-race-to-a-touchless-revenue-cycle), citing Harris Williams, June 2024). Nearly 20% of claims are denied on average, and as many as 60% of those denials are never appealed, each one representing recoverable revenue that is simply abandoned ( [McKinsey, January 2026](https://www.mckinsey.com/industries/healthcare/our-insights/agentic-ai-and-the-race-to-a-touchless-revenue-cycle), citing KFF, January 2025). Fifty-six percent of providers trace the root cause to patient information errors at intake ( [Experian Health, 2025](https://www.experian.com/blogs/healthcare/revenue-cycle-management-and-ai/)). For mid-market health systems, where median operating margins held near 1% throughout 2025 ( [Strata Decision Technology, December 2025](https://www.stratadecision.com/press-release/margins-remain-narrow-us-hospitals-face-intensifying-expense-pressures-and-growing)), there is no financial buffer to absorb this inefficiency. Revenue leakage is a structural problem, not an operational inconvenience. > "The revenue cycle has never been more complex. We have regulatory pressures mounting, the financial squeeze of declining reimbursements, and the constant pressure to do more with less." — Jason Considine, President at Experian Health, describing the environment at the company's High-Performance Summit Automation has been on the agenda for decades. The problem is that most of what health systems have deployed, such as rule-based tools, point solutions, early robotic process automation, was not designed to handle the complexity of modern payer environments. The rules keep changing and these systems do not adapt. ## What **agentic AI revenue cycle management** actually means [#](#what-agentic-ai-revenue-cycle-management-actually-means) The term "agentic AI" has started appearing in vendor materials across healthcare, often applied to systems that are better described as standard automation. But the distinction matters operationally. Traditional robotic process automation executes a defined sequence of steps. It is fast and reliable within its rules, but when a payer changes a portal layout or updates authorisation requirements, it breaks. GenAI adds language understanding, it can read documentation and produce useful outputs, but it does not take action. A billing specialist still needs to take its output and do something with it. Agentic AI revenue cycle management operates differently. An agent makes plans, retrieves context from multiple systems, makes decisions based on that context, and executes follow-up actions across the workflow autonomously and end-to-end. McKinsey describes this as the difference between a tool and a coworker ( [McKinsey, January 2026](https://www.mckinsey.com/industries/healthcare/our-insights/agentic-ai-and-the-race-to-a-touchless-revenue-cycle)). In practice, it means a single agentic workflow can verify eligibility, identify authorisation requirements, retrieve clinical documentation from the EHR, flag coding gaps, submit claims, monitor payer response, and route denials for follow-up without staff managing each step. McKinsey analysis indicates this could reduce cost-to-collect by 30–60% ( [McKinsey, January 2026](https://www.mckinsey.com/industries/healthcare/our-insights/agentic-ai-and-the-race-to-a-touchless-revenue-cycle)). The table below shows where each approach sits across five operational dimensions relevant to RCM. **Dimension** **Traditional RPA / GenAI** **Agentic AI** Scope of automation Single-step or task-level End-to-end workflow across multiple systems Adaptability Breaks when payer rules or portal formats change Re-plans based on new inputs and payer responses EHR & payer integration Limited — typically one system at a time Multi-source: EHR, payer portals, and billing platforms simultaneously Denial handling Routes denials to staff queues for manual review Identifies denial pattern, retrieves documentation, initiates appeal Human oversight Required at every step Human-in-the-loop at exception points only ## The highest-ROI starting points for mid-market health systems [#](#the-highest-roi-starting-points-for-mid-market-health-system) Mid-market health systems are meaningfully behind larger organisations in RCM AI adoption. Among health systems with annual revenue between $500 million and $1 billion, only 20% are actively piloting or implementing GenAI for RCM, compared to 64% of larger health systems ( [HFMA/AKASA survey, April 2025, via Fierce Healthcare](https://www.fiercehealthcare.com/ai-and-machine-learning/adoption-ai-hospital-rcm-surges-even-health-systems-navigate-cost)). Across all providers, only 15% have fully integrated AI into standard RCM operations ( [Experian Health, January 2026](https://www.experian.com/blogs/healthcare/revenue-cycle-management-and-ai/)). The most common entry point is eligibility verification, it is low risk, has a measurable baseline, and is fast to integrate. It is a reasonable start, but it is not where the highest ROI lives. The upstream case is stronger. Prior authorisation and clinical documentation improvement offer the greatest return because they prevent denials before claims are submitted. An anonymous CIO quoted in Bain's 2025 Provider and Payer Healthcare IT Survey put it plainly: "Every denial avoided is thousands of dollars we don't have to chase" ( [Bain, 2025](https://www.bain.com/insights/healthcare-it-investment-ai-moves-from-pilot-to-production/)). For mid-market health systems, we recommend a three-stage entry sequence for AI claims processing automation: 1. **Eligibility and benefits verification** — establishes the data integration baseline and produces measurable results within weeks 2. **Prior authorisation automation** — highest upstream ROI; requires clean eligibility data from stage one to work reliably 3. **Denial prediction before submission** — the stage that produces compound value, catching errors that would otherwise become denial management tasks Medical coding automation is a stage-two priority. It carries higher compliance risk and requires more historical data to train reliably. Deploying it before the integration foundation is established is a common over-reach that delays, rather than accelerates, the path to production. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## How to structure a pilot that scales [#](#how-to-structure-a-pilot-that-scales) Most RCM AI pilots do not fail because the technology underperforms. They fail because they were designed in ways that make scaling structurally impossible. McKinsey identified this as a recurring pattern: health systems purchase pilot solutions without considering how they will extend across the enterprise, which caps impact and produces business cases that cannot justify further investment ( [McKinsey, July 2023](https://www.mckinsey.com/industries/healthcare/our-insights/setting-the-revenue-cycle-up-for-success-in-automation-and-ai)). The reassuring counterpoint: fewer than 5% of providers report AI failing to meet expectations in categories where it has actually been introduced ( [Bain, 2025](https://www.bain.com/insights/healthcare-it-investment-ai-moves-from-pilot-to-production/)). The problem is design, not technology. Three principles separate a pilot that scales from one that stalls. **Scope for integration breadth, not task depth.** Choose a use case that requires connecting your EHR, a payer API, and your billing platform simultaneously. even at small volume. This validates the integration architecture you will need at scale, not just the point solution. **Define non-financial success metrics first.** First-pass resolution rate, staff hours recaptured per week, and error rate reduction are all measurable within the pilot window. Financial ROI is real but lags by one to two billing cycles. Pilots evaluated solely on short-term revenue will disappoint by design. **Assign an operational owner before go-live.** A COO, VP of Operations, or Revenue Cycle Director who is accountable for the pilot outcome, not just the engineering team, is the single most reliable indicator of whether a pilot transitions to production. Without internal ownership, momentum stalls when the external partner rolls off. The practical timing window for a mid-market pilot is eight to twelve weeks. Beyond sixteen weeks without clear milestone metrics, the issue is structural, not technical. ## The transition from **RCM pilot to production** [#](#the-transition-from-rcm-pilot-to-production) The gap between adoption and integration is where mid-market health systems lose momentum. Sixty-three percent of providers use AI in some RCM capacity; only 15% have fully integrated it into standard operations ( [Experian Health, January 2026](https://www.experian.com/blogs/healthcare/revenue-cycle-management-and-ai/)). The RCM pilot to production transition requires three parallel workstreams, not a sequential handoff. **Technical scaling.** A pilot typically automates one workflow. Production requires coordinated agents across front-end (eligibility, prior authorisation), mid-cycle (clinical documentation improvement, coding review), and back-end (denials, payment posting) operations, with a unified observability layer so every agent decision is logged and traceable. This architecture cannot be retrofitted onto a point-solution pilot; it must be planned before engineering begins. **Operational integration.** Billing staff roles shift from full-cycle manual processing to exception handling. This is a change management task with a technology dependency, not the reverse. Staff who understand what the agent does, and when to override it, are the difference between a system that is used and one that is merely tolerated. **Governance formalisation.** Before scaling volume, define who owns agent outputs, how errors are escalated, and what the audit trail looks like for payer or compliance review. These questions become significantly more expensive to answer after a denial dispute or audit. McKinsey projects that leading health systems will move from pilots to production-scale agentic AI deployments across the revenue cycle within the next two to three years ( [McKinsey, January 2026](https://www.mckinsey.com/industries/healthcare/our-insights/agentic-ai-and-the-race-to-a-touchless-revenue-cycle)). Mid-market systems that structure the transition correctly now will not be starting from scratch when that window closes. ## Compliance, data governance, and regulatory considerations [#](#compliance-data-governance-and-regulatory-considerations) Data privacy and security is the most commonly cited barrier to AI adoption in healthcare, raised by 50% of healthcare leaders, alongside accuracy concerns from 41% of providers ( [Experian Health, January 2026](https://www.experian.com/blogs/healthcare/revenue-cycle-management-and-ai/)). For mid-market health systems, these concerns are legitimate and addressable, but they require architectural decisions made before engineering begins, not after. **HIPAA requirements for agentic RCM systems.** Any agentic system that processes patient billing data; such as eligibility records, prior authorisation documentation, claims, explanations of benefits; handles protected health information. HIPAA requirements apply to every system the agent connects to: EHR, payer portals, clearinghouses, and billing platforms. Business Associate Agreements must cover all third parties. Every agent decision must produce an auditable log entry, both for compliance and to maintain billing staff trust during the transition period. For production **agentic AI healthcare billing** systems handling PHI at scale, on-premise or private cloud deployment is the architecturally sound choice. Shared SaaS infrastructure introduces data residency and access control risks that are difficult to audit. **EU AI Act.** Administrative billing automation; claims processing, prior authorisation, coding; does not clearly fall under Annex III high-risk classification under the current EU AI Act framework. High-risk healthcare AI under the Act is defined around clinical safety components in medical devices, public authority eligibility determinations for healthcare benefits, health insurance risk assessment, and emergency triage systems. RCM billing sits outside these categories under current guidance ( [EU AI Act Annex III](https://artificialintelligenceact.eu/annex/3/)). Organisations with EU operations should review the European Commission's classification guidelines, due for publication by February 2026, before production deployment. The practical principle: compliance architecture is cheaper to build in than to retrofit. Treating it as a production requirement from day one removes the most common reason RCM AI implementations stall after a successful pilot. ## What a production-grade agentic RCM deployment looks like [#](#what-a-production-grade-agentic-rcm-deployment-looks-like) We built a production-grade agentic AI system for a healthcare provider that demonstrates the architectural principles that apply equally to RCM deployments: multi-channel integration with live clinical systems, real-time autonomous decision-making, human-in-the-loop escalation for edge cases, and full observability across every agent action, all operating in a regulated environment. The same design patterns that make a patient-facing healthcare agent reliable in production are precisely the ones that make an RCM agent trustworthy at scale. You can review that deployment in our [multi-channel AI agent for healthcare case study](/case-study/multi-channel-ai-agent-in-healthcare/). A production-grade agentic RCM system is not a single tool. It is a coordinated architecture of agents operating across three layers: * **Front-end agents** handle eligibility verification, benefits checking, and prior authorisation, preventing upstream errors before they become downstream denials * **Mid-cycle agents** review clinical documentation, flag coding gaps, and scrub claims before submission where AI claims processing automation generates the most durable ROI * **Back-end agents** manage denial workflows, identify underpayments, and post payments; the highest-volume, most labour-intensive tasks in traditional RCM Each layer feeds the next, and a unified observability layer logs every agent decision with its source data. This is not optional for production, it is the condition under which billing staff, compliance officers, and payers will accept expanded automation. A system that cannot explain its decisions will not scale beyond the pilot. For mid-market health systems weighing the build-versus-partner decision: the specialised agentic engineering capability required to architect and deploy this across live clinical infrastructure is not easily assembled in-house. The model that produces long-term independence, rather than dependency, is a structured implementation partnership with full knowledge transfer to your internal IT team. Our [TriStorm methodology](/tristorm/) is how we take organisations from use case discovery to a deployed, observable agentic system without losing continuity between the strategy and the build. You can read more about our work in healthcare on our [healthcare industry page](/agentic-ai-in-healthcare/). The mid-market window for structured adoption is narrowing. Larger health systems are moving from pilots to enterprise-wide deployments, and the operational and financial distance between early movers and late adopters is beginning to compound. The practical steps covered here — choosing the right use case sequence, designing pilots for scalability, building compliance in from the start, and transitioning through three parallel workstreams — are achievable within a mid-market budget and timeline. The question is not whether to make the move, but how quickly it can be done without repeating the structural mistakes that have kept most providers stuck between 63% adoption and 15% integration. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![The Emperor’s New AI agents](/app/uploads/2026/04/2024-01-20__Y4A0427-scaled.webp) ### The Emperor’s New AI agents Read article ](/agentic-ai/the-emperors-new-ai-agents/)[ ![Print-on-demand quality control: how AI visual inspection and agentic workflows reduce defects and returns](/app/uploads/2026/03/Group-1777-9.png) ### Print-on-demand quality control: how AI visual inspection and agentic workflows reduce defects and returns Read article ](/agentic-ai/print-on-demand-quality-control-how-ai-visual-inspection-and-agentic-workflows-reduce-defects-and-returns/)[ ![Enterprise AI Agent Deployment Consultant Comparison: Boutique vs Enterprise Service 2026](/app/uploads/2026/03/Group-1777-1-1.png) ### Enterprise AI Agent Deployment Consultant Comparison: Boutique vs Enterprise Service 2026 Read article ](/agentic-ai/enterprise-ai-agent-deployment-consultant-comparison-boutique-vs-enterprise-service/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI ROI: two returns business cases should measure URL: https://vstorm.co/agentic-ai/agentic-ai-roi-two-returns-business-cases-should-measure Agentic AI business cases should count two returns: economic savings and the harder-to-measure gains in decision speed, knowledge and agility. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI ROI: two returns business cases should measure [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI ROI: two returns business cases should measure Agentic AI business cases should count two returns: economic savings and the harder-to-measure gains in decision speed, knowledge and agility. ![Antoni Kozelski](https://vstorm.co/app/uploads/2026/09/antoni-kozelski-2026.jpg) [Antoni Kozelski](/author/antoni-kozelski/) CEO & Co-founder July 10, 2026 5 min read ![Agentic AI ROI: two returns business cases should measure](/app/uploads/2026/04/288C8762-FB8D-43CE-B595-44C96A29B27F_1_105_c.jpeg) On this page0% 1. [Most agentic AI business cases measure only one kind of return](#most-agentic-ai-business-cases-measure-only-one-kind-of-retu) 2. [Type 1: economic ROI, the return that fits in a spreadsheet](#type-1-economic-roi-the-return-that-fits-in-a-spreadsheet) 3. [Type 2: non-economic ROI, the return that compounds](#type-2-non-economic-roi-the-return-that-compounds) 4. [Why a partial agentic AI ROI picture leads to underinvestment](#why-a-partial-agentic-ai-roi-picture-leads-to-underinvestmen) 5. [Making both returns visible in the Strategizing phase](#making-both-returns-visible-in-the-strategizing-phase) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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Most companies see only the first. That gap is the difference between a narrow cost-optimisation project and a transformation that compounds, and it is why the agentic AI business case so often understates what the investment is worth. Getting agentic AI ROI right starts with naming both returns before scope is fixed. ## Most agentic AI business cases measure only one kind of return [#](#most-agentic-ai-business-cases-measure-only-one-kind-of-retu) Today most organisations evaluate agentic transformation, and their artificial intelligence investment more broadly, with the same model they apply to any technology purchase: a spreadsheet of hard cost savings. Hours removed, cost per transaction, headcount reallocated. It is the model the finance team trusts, and it is the model the CFO signs off. That model is built to see one kind of value, so it misses the rest. The consequence is visible in the aggregate figures. MIT's State of AI in Business 2025 found that 95% of generative AI pilots delivered no measurable impact on profit and loss, while more than half of budgets went to sales and marketing even though back-office automation produced the highest returns ( [Fortune, on the MIT NANDA report](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/)). Across these AI deployments, the problem is rarely the technology. It is that companies measure the wrong return in the wrong place. ## Type 1: economic ROI, the return that fits in a spreadsheet [#](#type-1-economic-roi-the-return-that-fits-in-a-spreadsheet) Type 1 is the familiar territory: directly quantifiable, and straightforward to defend in a budget review. It is the return that appears when agents take over routine tasks. It shows up as hours saved, cost per transaction reduced, headcount reallocated, and throughput gained. An agent that handles 400 invoice matches a day that previously required 2.5 full-time employees is Type 1. So is an average resolution time that falls from 14 minutes to 90 seconds. We at Vstorm see this return in production. Before we automated device activation for a US telecommunications provider, every field installation depended on a manual, call-centre process: a technician phoned a support centre, where three agents activated the device in real time across multiple systems. The manual workflow carried a high labour cost per installation, limited service to office hours, and blocked expansion into new states. The multi-agent system we built reduces manual effort to a fraction: it achieved 98% automation of device activation and a tenfold efficiency improvement, and it removed the time-of-day limit entirely ( [case study](/case-study/intelligent-automation-with-actionable-ai-agents-for-the-us-telecommunication-company/)). [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Type 2: non-economic ROI, the return that compounds [#](#type-2-non-economic-roi-the-return-that-compounds) Type 2 is where the compounding value lives, and it is harder to put a number on. It shows up as faster decisions, because information flows without bottlenecks and agents improve decision quality; as higher employee satisfaction, because people stop doing repetitive, low-value work; as institutional knowledge captured in agent logic rather than held in one person's head; and as organisational agility, the ability to respond to market shifts in days rather than quarters. These outcomes reach the profit and loss eventually, but the causal chain is indirect and the timeline is longer. The data shows how easily this return is missed. In McKinsey's State of AI in 2025, 64% of organisations said AI was already enabling their innovation, yet only 39% reported enterprise-wide earnings impact ( [McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)). McKinsey also notes that the productivity gains from broad AI tools, such as copilots and AI assistants, tend to be distributed thinly across employees, which makes them hard to see in headline financial results. This is the return we design for alongside the economic one. When we cut engineers' most tedious tasks to seconds for an engineering-automation platform, the measurable time saved was only part of the value; the larger return was freeing skilled engineers for work that machines cannot do ( [case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/)). The multi-channel pre-appointment agent we built in healthcare changed the experience for patients and care teams, a return that a per-transaction figure does not capture ( [case study](/case-study/multi-channel-ai-agent-in-healthcare/)). **Dimension** **Type 1: economic ROI** **Type 2: non-economic ROI** **Nature** Directly quantifiable Real, but hard to quantify **Typical measures** Hours saved, cost per transaction, headcount reallocated, throughput Decision speed, employee satisfaction, captured knowledge, organisational agility **Timeline** Immediate, visible within the year Longer, compounds over time **Where it appears** In the spreadsheet; signed off by finance Reaches profit and loss indirectly **Risk if overlooked** Low, because it is always counted Under-scoping; treating transformation as a cost play ## Why a partial agentic AI ROI picture leads to underinvestment [#](#why-a-partial-agentic-ai-roi-picture-leads-to-underinvestmen) A business case that counts only Type 1 will consistently understate the return, and that has a predictable effect on behaviour. Clients who evaluate agentic AI transformation through economic ROI alone tend to underinvest, scope too narrowly, and deploy agents only where the spreadsheet already justifies the cost. The work becomes a cost-optimisation play rather than a change to how the organisation runs its core business processes. The pattern is measurable. McKinsey reports that 80% of companies set efficiency as their AI objective, but the organisations seeing the most value set goals around growth and innovation instead ( [McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)). Separately, McKinsey found that 94% of companies were not yet seeing significant value from their AI investments, an echo of the old observation that a new technology can appear everywhere except in the productivity figures ( [McKinsey](https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/where-ai-will-create-value-and-where-it-wont)). A return that is never named is a return that never gets funded. ## Making both returns visible in the Strategizing phase [#](#making-both-returns-visible-in-the-strategizing-phase) Our task in the Strategizing phase, the first stage of our TriStorm methodology, is to make both returns visible before scope is set ( [TriStorm](/tristorm/)). We quantify the economic return for each high-impact use case, and we name the non-economic return explicitly, so that decision speed, employee experience, retained knowledge, and agility enter the business case as stated outcomes rather than pleasant side effects. This discipline shapes every engagement. Whether we are deploying AI agents in the complex workflows of finance, customer service, fraud detection, or the supply chain, the economic return is only ever half the picture. When AI powers a business process end to end, the compounding return is often larger than the savings that first justified it. A business case built on economic ROI alone will always under-scope the agentic AI systems worth building. Counting both returns is how a cost project becomes a durable, long-term advantage. That is the work we do before the building begins. ![Antoni Kozelski](https://vstorm.co/app/uploads/2026/09/antoni-kozelski-2026.jpg) [Antoni Kozelski](/author/antoni-kozelski/) CEO & Co-founder [Author profile](/author/antoni-kozelski/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![The future of Small Language Models in the middle-market](/app/uploads/2026/07/IMG_8222-768x473.jpg) ### The future of Small Language Models in the middle-market Read article ](/agentic-ai/the-future-of-small-language-models-in-the-middle-market/)[ ![Buy vs build agentic AI: a single-workflow decision](/app/uploads/2026/03/9A3FD2D7-544E-44EA-BAFE-1B0C8EC14E5B_1_105_c-2-768x392.jpeg) ### Buy vs build agentic AI: a single-workflow decision Read article ](/agentic-ai/buy-vs-build-agentic-ai-a-single-workflow-decision/)[ ![AgenticOS as the destination](/app/uploads/2026/06/2024-01-20__Y4A0401-scaled.webp) ### AgenticOS as the destination Read article ](/agentic-ai/agenticos-as-the-destination/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI services market growth: who is leading URL: https://vstorm.co/agentic-ai/agentic-ai-services-market-growth-who-is-leading The agentic AI market grows at 44.6% CAGR while 11% of organisations run agents in production. Which of the three provider tiers is gaining ground. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI services market growth: who is leading [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI services market growth: who is leading The agentic AI market grows at 44.6% CAGR while 11% of organisations run agents in production. Which of the three provider tiers is gaining ground. ![Antoni Kozelski](https://vstorm.co/app/uploads/2026/09/antoni-kozelski-2026.jpg) [Antoni Kozelski](/author/antoni-kozelski/) CEO & Co-founder June 11, 2026 10 min read ![Agentic AI services market growth: who is leading](/app/uploads/2026/06/Group-1777-2.png) On this page0% 1. [What is driving agentic AI services market growth in 2026](#what-is-driving-agentic-ai-services-market-growth-in-2026) 2. [The three-tier market structure](#the-three-tier-market-structure) 3. [Platform players: embedding agents into enterprise software](#platform-players-embedding-agents-into-enterprise-software) 4. [Global consultancies: scaling agentic AI transformation](#global-consultancies-scaling-agentic-ai-transformation) 5. [Specialist boutiques: where production-grade agentic AI is being built](#specialist-boutiques-where-production-grade-agentic-ai-is-be) 6. [What separates the fastest-growing providers from the rest](#what-separates-the-fastest-growing-providers-from-the-rest) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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The companies gaining durable ground are not always the largest. Three tiers are driving **agentic AI services market growth**: platform players embedding agents into existing enterprise software, global consultancies scaling **agentic AI transformation** at enterprise volume, and specialist boutiques building **production-grade agentic AI** for mid-market organizations. Each tier grows for different reasons and serves different buyers. Understanding which tier matches your operational context is the most practical decision a leader can make in this market today. The **agentic AI services market growth** is running faster than almost any enterprise technology category in recent memory. The market is projected to expand from $7.06 billion in 2025 to $93.20 billion by 2032, at a CAGR of 44.6%. [(MarketsandMarkets)](https://www.marketsandmarkets.com/Market-Reports/agentic-ai-market-208190735.html) Gartner forecasts that 40% of enterprise applications will include task-specific AI agents by end of 2026, up from less than 5% in 2025. [(Gartner, August 2025)](https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025) Yet Deloitte's 2025 Emerging Technology Trends study finds only 11% of organizations are actively using agentic AI in production. [(Deloitte)](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2026/agentic-ai-strategy.html) That gap between market momentum and operational reality defines where the real competition is taking place. The companies gaining durable ground are not simply the ones with the largest portfolios or the most visible brand names. They are the ones with a proven answer to the question most organizations are now asking: how do we move from a controlled pilot to a system running reliably in live operations? This analysis maps three tiers of growth, identifies the leading companies in each, and examines what separates providers growing fastest from those generating activity without production outcomes. ## What is driving **agentic AI services market growth** in 2026 [#](#what-is-driving-agentic-ai-services-market-growth-in-2026) The demand surge has a single structural cause: the first wave of generative **artificial intelligence** did not deliver enterprise-level performance gains. Copilots, chatbots, and document summarizers made individual contributors marginally more productive. They did not move operational performance at the process level, because they were designed to enhance individual tasks rather than automate complex **business processes** across systems and departments. Agentic AI addresses that gap directly. Where generative AI responds to prompts, agentic AI plans, uses tools, coordinates across systems, and executes **agentic workflows** in **real time**, with defined objectives and minimal **human intervention**. This is the capability boards and investors are now demanding. The scale of commitment reflects that demand. More than $9.7 billion has been invested in agentic AI startups since 2023. [(SNS Insider)](https://www.snsinsider.com/reports/agentic-ai-market-7545) Sixty-one percent of CEOs report integrating agents into core operations, **early adoption** levels already surpassing those of the earlier RPA wave. [(Mordor Intelligence)](https://www.mordorintelligence.com/industry-reports/agentic-ai-market) The constraint shaping the market is equally significant. Gartner predicts that over 40% of agentic AI projects will be cancelled by end of 2027, due to escalating costs, unclear business value, or inadequate risk controls. [(Gartner, June 2025)](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027) That cancellation rate is not a statement about the technology. It is a statement about how the technology is being deployed. The primary failure causes are infrastructure gaps (41%), governance and security barriers (38%), and ROI measurement failures (33%). [(Digital Applied)](https://www.digitalapplied.com/blog/agentic-ai-statistics-2026-definitive-collection-150-data-points) Each of these is a solvable engineering and architecture problem. The firms solving them are the firms growing. ## The three-tier market structure [#](#the-three-tier-market-structure) Not all agentic AI service providers are competing for the same buyer. The table below maps three distinct tiers by type, what they build, and who they serve. **Tier** **Type** **What they build** **Who they serve** 1 Platform players Agentic capabilities embedded into existing enterprise software Enterprises already operating on their CRM, ERP, or workflow platform 2 Global consultancies AI transformation strategy plus implementation at scale Enterprises with nine-figure AI budgets and multi-year transformation roadmaps 3 Specialist boutiques Production-grade agentic systems built end-to-end for specific operational contexts Mid-market organizations with complex, domain-specific workflows that do not fit platform templates Three characteristics separate the tiers more than any other factor. Platform players grow because buyers are already inside their data ecosystem. Global consultancies grow because enterprise buyers with large procurement cycles and governance requirements need partners that can operate at their scale. Specialist boutiques grow because mid-market buyers with complex, domain-specific workflows need **AI capabilities** built for their exact operational context, owned outright, and supported by a team that remains accountable for production outcomes. ## Platform players: embedding agents into enterprise software [#](#platform-players-embedding-agents-into-enterprise-software) The platform tier is producing the most visible commercial results in the current market. **Salesforce Agentforce** is the clearest example. Marc Benioff described the product in a Salesforce Investor Day SEC filing as "our fastest-growing organic product ever." [(Salesforce SEC Form 8-K, Investor Day)](https://www.sec.gov/Archives/edgar/data/0001108524/000110852425000168/ex991-investordaypressrele.htm) By early 2026, Agentforce had reached $540 million ARR with 18,500 enterprise customers. [(beam.ai)](https://beam.ai/agentic-insights/ai-agents-in-2026-how-the-us-and-china-are-building-two-very-different-futures) The growth mechanism is direct: Salesforce already holds the CRM data and workflow context its agents need to function. Enabling Agentforce for an existing Salesforce customer requires activation rather than integration from scratch. **Microsoft** is building a different kind of platform advantage through Copilot Studio. Rather than a single agent product, Microsoft is constructing the management infrastructure for **multi-agent systems**: governance, compliance, and integration with the Microsoft 365 ecosystem. The bet is that enterprises running on Microsoft infrastructure will manage their agentic systems the same way they manage everything else. **ServiceNow** is applying agentic capabilities to the IT, HR, and customer operations workflows where it already holds deep process integration. Its positioning is consistent with the platform tier's core growth driver: the agents work because the data and process context was already there. The structural constraint of this tier is equally consistent. Platform agents are optimized for processes that fit their data model. An organization running customer service on Salesforce will find Agentforce immediately applicable. An organization with cross-departmental, domain-specific processes spanning manufacturing execution, custom order management, or healthcare coordination across multiple **data sources** will encounter the ceiling of template-based agents before long. That boundary is where the next two tiers become the relevant choice. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Global consultancies: scaling **agentic AI transformation** [#](#global-consultancies-scaling-agentic-ai-transformation) Accenture and Deloitte represent a tier growing at enterprise scale, driven by the same demand surge but serving buyers with the budget, procurement cycles, and governance requirements that only a firm of their size can accommodate. Accenture's numbers are instructive. Generative and agentic AI revenue tripled year over year to $2.7 billion in FY2025. AI bookings nearly doubled to $5.9 billion across 6,000 projects. The AI workforce grew from 40,000 to 77,000 in two years. [(Accenture FY2025 Annual Report)](https://www.accenture.com/us-en/about/company/integrated-reporting-financial) In Q1 FY2026, advanced AI bookings reached $2.2 billion, nearly doubling year over year again. [(Accenture SEC Form 8-K, Q1 FY2026)](https://www.sec.gov/Archives/edgar/data/0001467373/000146737325000221/q1fy26earnings8-kexhibit.htm) CEO Julie Sweet noted on the Q4 FY2025 earnings call: "One out of every two projects in Gen AI, agentic AI and physical AI now has significant data pull-through." [(CIO Dive)](https://www.ciodive.com/news/accenture-generative-ai-revenue-skills-training-data-modernization/761161/) Deloitte's growth is anchored in the governance layer that enterprise buyers require before they will commit to production. With only one in five companies holding a mature governance model for autonomous AI agents, the demand for structured **agentic AI transformation** consulting (roadmap, governance framework, change management) is high among organizations **adopting agentic AI** that cannot move to production without those controls in place. The documented constraint at this tier is structural rather than a criticism. Global consultancies typically separate strategy and engineering: the team that maps the agentic roadmap is often different from the team that builds the system. For mid-market organizations, the engagement models are calibrated for enterprise budgets. Independent analysis of buyer experiences consistently identifies the roadmap-to-build handoff as the point at which strategic intent and engineering execution diverge. [(JADA Squad)](https://www.jadasquad.com/blog/ai-agent-consulting-companies) That gap is precisely what the next tier is built to close. ## Specialist boutiques: where **production-grade agentic AI** is being built [#](#specialist-boutiques-where-production-grade-agentic-ai-is-be) The fastest-growing segment by proportion in agentic AI services is specialist boutiques: firms whose entire practice is organized around building and deploying **production-grade agentic AI** systems. No legacy consulting business, no platform allegiance, and no separation between the team that scopes the work and the team that delivers it. Their growth is driven by a specific, well-documented market need: mid-market organizations that need agentic AI built to **handle complex**, cross-departmental workflows, integrating with their **existing systems**, owned outright without vendor dependency. [(JADA Squad analysis)](https://www.jadasquad.com/blog/ai-agent-consulting-companies) What distinguishes **production-grade agentic AI** delivery at this tier comes down to four characteristics. First, an open-source stack with no vendor dependency, giving the client full architectural ownership and the freedom to change AI providers without losing their investment. Second, forward-deployed engineers working inside client context rather than against a remote specification, which closes the information gap between what the business needs and what the system actually does. Third, observability built into every deployment: every agent ships with monitoring that traces decisions, enables **human in the loop** review where required, and supports auditing in production. Fourth, knowledge transfer embedded in the build process, so the client's own team can maintain and extend the system after delivery. Vstorm is an example of this tier operating in the EU and UK market. As an agentic AI engineering company, we at Vstorm run [agentic AI consulting services](/agentic-ai-consulting/) and agentic AI engineering in a single team, designed specifically to eliminate the handoff gap between strategic roadmap and deployed production system. As the exclusive EU/UK partner and core contributor to Pydantic AI, the type-safe framework for building agents on top of **large language models** that process **natural language** instructions, which reached approximately 17,000 GitHub stars by mid-2026 [(futureagi.com)](https://futureagi.com/blog/what-is-pydantic-ai-2026/), our engineers contribute directly to the open-source stack the broader industry builds on. The production outcomes we have delivered demonstrate what this model produces in practice. In a deployment for Mixam, a UK-based print-on-demand platform, we delivered a 95.4% workflow success rate, an 11.76% increase in order volume on day one, and a 62.11% quote-to-paid conversion rate. [(Vstorm Mixam case study)](/case-study/ai-agent-for-order-recommendation-and-completion/) These numbers came from a system running in live operations, not a controlled demonstration environment. For organizations evaluating production evidence, the full case study library is available at [vstorm.co/case-study/](/case-studies/). **Neurons Lab** represents the same tier in financial services. The UK and Singapore-based boutique has completed more than 100 client engagements, including production deployments for HSBC, Visa, and AXA, and holds AWS Advanced Tier Partner status with Generative AI and Financial Services competencies. [(neurons-lab.com)](https://neurons-lab.com/articles/neurons-lab-achieves-aws-ai-competency-in-the-agentic-ai-category/) Its focus is exclusive to financial services: regulated workflows including compliance reporting, KYC automation, and fraud detection, where governance and auditability are prerequisites for any production deployment. These two firms do not compete directly. Neurons Lab serves financial institutions operating in tightly regulated environments. Vstorm serves mid-market organizations in manufacturing, healthcare, and print-on-demand. Their shared presence here illustrates a broader point: the specialist boutique tier is growing across multiple verticals, each requiring the kind of domain depth that a generalist provider cannot replicate at the process level. ## What separates the fastest-growing providers from the rest [#](#what-separates-the-fastest-growing-providers-from-the-rest) Three characteristics separate the companies gaining durable ground from those generating activity without production outcomes. **Production evidence, not pilot claims.** Every tier's fastest growers share one characteristic: systems running in live operations with measurable results attached. Eighty-eight percent of AI agents fail to reach production. [(Digital Applied)](https://www.digitalapplied.com/blog/agentic-ai-statistics-2026-definitive-collection-150-data-points) Growth accrues to firms that can point to specific clients, specific metrics, and specific architectures running in the real world. The absence of published production case studies, as opposed to demo highlights or pilot testimonials, is the most reliable signal that a provider has not yet solved the deployment problem. **Continuity from strategy to build.** Salesforce grows because it owns both the platform and the deployment tooling. Accenture grows because it carries strategy through implementation at enterprise scale. Specialist boutiques grow because the same senior team carries the full engagement from process mapping to deployed system. The pattern that does not produce durable growth: firms that hand off between a consulting partner and an engineering partner at the roadmap-to-build boundary. That handoff is where strategic intent most often fractures. **Domain depth in the verticals they serve.** Agentforce is purpose-built for CRM workflows. Neurons Lab focuses exclusively on financial services. Vstorm focuses on mid-market organizations in manufacturing, healthcare, and print-on-demand. The generalist claim, "we can build agents for any industry," is the least differentiated positioning in the current market. Domain depth is what makes the difference between an agent that functions in a controlled demo and one that operates reliably inside an organization's actual processes, data formats, and compliance requirements. For organizations evaluating partners, the operative question is not who is growing fastest in absolute terms. It is which provider's growth model matches the operational problem at hand. Platform players are the right choice where your workflow fits their template. Global consultancies are the right choice where your budget and procurement cycle match their engagement model. Specialist boutiques are the right choice where you need **autonomous systems** built for your specific processes, owned outright, and delivered by a team that has built comparable systems before. The organizations that close the production gap in 2026 will not be the ones that chose the largest provider. They will be the ones that chose the most relevant one. ![Antoni Kozelski](https://vstorm.co/app/uploads/2026/09/antoni-kozelski-2026.jpg) [Antoni Kozelski](/author/antoni-kozelski/) CEO & Co-founder [Author profile](/author/antoni-kozelski/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![4 Lessons from failed AI adoption ideas](/app/uploads/2026/04/2024-01-20__Y4A0426-scaled.webp) ### 4 Lessons from failed AI adoption ideas Read article ](/agentic-ai/4-lessons-from-failed-ai-adoption-ideas/)[ ![Why manufacturing AI stalls before it reaches the shop floor](/app/uploads/2026/06/Group-1777-1.png) ### Why manufacturing AI stalls before it reaches the shop floor Read article ](/agentic-ai/why-manufacturing-ai-stalls-before-it-reaches-the-shop-floor/)[ ![How agentic AI transforms the manual procurement cycle](/app/uploads/2026/06/Group-1777.png) ### How agentic AI transforms the manual procurement cycle Read article ](/agentic-ai/how-agentic-ai-transforms-the-manual-procurement-cycle/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI Transformation Consultancy vs AI modeling shop URL: https://vstorm.co/agentic-ai/agentic-ai-transformation-consultancy-vs-ai-modeling-shop Two vendor types look alike from the outside and answer different questions. How to tell a transformation consultancy from an AI modeling shop. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI Transformation Consultancy vs AI modeling shop [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI Transformation Consultancy vs AI modeling shop Two vendor types look alike from the outside and answer different questions. How to tell a transformation consultancy from an AI modeling shop. ![Antoni Kozelski](https://vstorm.co/app/uploads/2026/09/antoni-kozelski-2026.jpg) [Antoni Kozelski](/author/antoni-kozelski/) CEO & Co-founder June 18, 2026 7 min read ![Agentic AI Transformation Consultancy vs AI modeling shop](/app/uploads/2026/06/Group-1777-1-2.png) On this page0% 1. [Which partner does your problem actually need? Two firms, two different answers](#which-partner-does-your-problem-actually-need-two-firms-two-) 2. [How mid-market companies buy AI help today](#how-mid-market-companies-buy-ai-help-today) 3. [What an AI modeling shop actually does](#what-an-ai-modeling-shop-actually-does) 4. [What an Agentic AI Transformation Consultancy actually does](#what-an-agentic-ai-transformation-consultancy-actually-does) 5. [The shift that separates them: from building models to building systems around models](#the-shift-that-separates-them-from-building-models-to-buildi) 6. [Side-by-side: modeling shop versus transformation consultancy](#side-by-side-modeling-shop-versus-transformation-consultancy) 7. [Which one does your problem need](#which-one-does-your-problem-need) 8. [Where Vstorm fits](#where-vstorm-fits) 9. [Conclusion](#conclusion) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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Two firms, two different answers [#](#which-partner-does-your-problem-actually-need-two-firms-two-) We at Vstorm meet the same pattern in early conversations. A leader has an operational problem, describes it as "an AI project", and starts comparing vendors who all promise to extend its AI capabilities and look broadly similar from the outside. On closer inspection, two of those vendors are answering completely different questions. One firm answers: what model do we need to build? The other answers: what system do we need to build around models that already exist? Both are legitimate. Neither is a substitute for the other. Picking the wrong one is one of the quieter reasons AI initiatives stall, because the engagement is mis-scoped before any code is written. The stakes are rising as budgets move. The agentic AI market is projected to grow from USD 7.06 billion in 2025 to USD 93.20 billion by 2032, a CAGR of 44.6% ( [MarketsandMarkets](https://www.marketsandmarkets.com/Market-Reports/agentic-ai-market-208190735.html)). More money flowing into the category means more firms positioned at its edges, and more buyers who need to tell them apart. ## How mid-market companies buy AI help today [#](#how-mid-market-companies-buy-ai-help-today) Before comparing the two firms, it helps to see how the work is bought today, because most mis-scoping starts here. A mid-market company with a cross-departmental process problem usually reaches for one of three options. The first is an off-the-shelf platform, which works when the process fits the template and stalls when it does not. The second is an in-house build, which depends on a team learning to implement agentic AI for the first time on a live project. The third is hiring a modeling shop, often in the expectation that a "model" will resolve what is really a workflow and integration problem. The third path is where the category confusion bites. The buyer has a process that spans billing, customer service, and analytics, and brings it to a partner whose core competence is training and optimising models. The fit is approximate. We explored this expectation gap in [What do we mean by AI automation, actually?](/agentic-ai/what-do-we-mean-by-ai-automation-actually/), where the recurring issue is buyers describing an outcome while assuming a particular technical route to it. The route, not the outcome, is what separates these two firms. ## What an AI modeling shop actually does [#](#what-an-ai-modeling-shop-actually-does) An AI modeling shop is a model-centric, research-oriented partner. The core unit of work is a model that does not yet exist: a domain-specific classifier, a demand forecaster, a computer-vision defect detector, or a model fine-tuned on proprietary data. The discipline underneath is MLOps, and the risk concentrates in model accuracy and performance. deepsense.ai is a clear and credible example of the category. Its published service surface spans predictive analytics, computer vision, MLOps, edge AI, and model design and optimisation, and a common engagement is team augmentation, working directly alongside a client's data scientists and machine learning engineers ( [deepsense.ai](https://deepsense.ai/)). In its own client reviews, the scope is described as the continuous development and maintenance of machine learning models, including retraining and fine-tuning to meet evolving requirements ( [Clutch](https://clutch.co/profile/deepsenseai)). This is real, valuable, and difficult work, and it is the heart of traditional AI, where the model itself is the deliverable. When a business genuinely needs a new model trained on its own data, this is the right partner. The point of the comparison is not that one firm is weaker, but that the work has a different centre of gravity. ## What an Agentic AI Transformation Consultancy actually does [#](#what-an-agentic-ai-transformation-consultancy-actually-does) An Agentic AI Transformation Consultancy starts from a different premise: the reasoning engine already exists. The work is to orchestrate foundation models into agentic AI systems that plan, call external tools, draw on multiple data sources, hold memory across steps, and keep a human in the loop where a decision needs it, then to integrate them with the CRM, ERP, and legacy infrastructure they have to live inside. The unit of work is the system and the transformation around it, not the model. That means the engagement runs across more than engineering: identifying where agentic AI creates operational leverage, designing the architecture, building and deploying it, and changing the process so the system actually lands. Risk concentrates in orchestration, integration, reliability, and change management rather than in model accuracy. This is the layer our TriStorm methodology is built for, moving from prioritised roadmap to deployed, observable system ( [TriStorm](/tristorm/)). The output is production-grade agentic AI running in operations, owned by the client, rather than a model handed back for someone else to deploy. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## The shift that separates them: from building models to building systems around models [#](#the-shift-that-separates-them-from-building-models-to-buildi) The cleanest way to understand the two firms is the technical shift beneath them. The industry has moved from building models to building systems around models. Traditional ML requires training infrastructure; modern agentic systems require prompt engineering, context management, retrieval pipelines, and tool orchestration. Foundation models are general-purpose systems adapted to many tasks through prompting, unlike traditional ML models that learn one specific task ( [Production AI engineering guide](https://theja.org/aieng101/src/module1/instructional-materials/ml-vs-llm-architectures)). The operational discipline has shifted with it, from MLOps to LLMOps to AgentOps. MLOps suits task-specific models whose objectives and data distributions stay stable; foundation models behave as general-purpose reasoning engines adapted through prompting and in-context learning, which traditional MLOps pipelines were not designed to manage ( [AgentOps research, arXiv](https://arxiv.org/pdf/2601.06456)). This is why an agentic AI implementation is a different engineering problem from training a model. The difficulty is not teaching a model a task. It is making autonomous systems that reason over multi-step tasks behave reliably in production. That, not model accuracy, is what makes agentic AI work in operations. > "The hardest part of an agentic project is rarely the model. It is everything around it: the integrations, the failure modes, and proving the system behaves the same way on Friday afternoon as it did in the demo." Wojciech Achtelik PhD(c), AI Engineer Lead at Vstorm ## Side-by-side: modeling shop versus transformation consultancy [#](#side-by-side-modeling-shop-versus-transformation-consultancy) The table below sets out the practical differences. Neither column is a verdict; each maps to a different kind of problem. **Dimension** **Agentic AI Transformation Consultancy** **AI modeling shop (R&D)** **Core question** What system do we build around existing models? What model do we need to build? **What gets built** Agentic system: planning, tool use, memory, integration A trained or fine-tuned model: classifier, forecaster, vision model **Underlying discipline** AgentOps and LLMOps MLOps and data science **Where the main risk sits** Orchestration, integration, reliability, change management Model accuracy and performance **Typical engagement** End-to-end: roadmap, architecture, build, deployment, handover Often staff or team augmentation alongside an internal data team **Primary deliverable** Deployed, observable system in production A model, often returned for the client to deploy **Best-fit problem** The model exists; the workflow and integration are the hard part The model does not exist yet and must be built on your data ## Which one does your problem need [#](#which-one-does-your-problem-need) The decision reduces to a single test. Does the model you need already exist? If the answer is no, because you need a predictor or detector trained on your own proprietary data, a modeling shop is the right partner, and the risk you are managing is accuracy. If the answer is yes, because foundation models can already reason over your task and the difficulty is the system, the integration, and the process around them, a transformation consultancy is the right partner, and the risk you are managing is reliable production behaviour: systems that hold up in the real world, with human oversight built in. Some steps will still require human judgement, and a feedback loop keeps the system improving after launch. The line does blur honestly, and it is worth stating plainly. Modeling shops increasingly offer agents and retrieval, and agentic systems increasingly call trained ML models as tools inside a larger workflow ( [MLAT framework, arXiv](https://arxiv.org/pdf/2602.14295)). The categories overlap at the edges. What does not change is the centre of gravity: one firm is organised around producing models, the other around producing systems and the transformation they enable. For most mid-market process problems, the bottleneck is the system, not a missing model. ## Where Vstorm fits [#](#where-vstorm-fits) We are an agentic AI engineering company, and we are not a modeling shop. We do not position ourselves as an R&D house training models from scratch; our work is orchestrating, integrating, and deploying agentic AI solutions that run in operations and that the client owns and maintains over the long term. The evidence sits in what we ship. We contribute production patterns to open source: our Full-Stack AI Agent Template has more than 1,730 GitHub stars and over 830,000 downloads, and our [Pydantic DeepAgents](/open-source/pydantic-deep-agents-vs-langchain-deep-agents-which-python-ai-agent-framework-should-you-choose/) framework is drawn directly from client engagements rather than written as demos ( [Vstorm open source](https://oss.vstorm.co/)). We build for observable, debuggable production from the start, which is why we run unified tracing across the full stack to continuously monitor what our systems do once they are live ( [Why we use Logfire in our stack](/agentic-ai/one-dashboard-full-visibility-why-we-use-logfire-in-our-stack/)). And the systems reach production: our journey from a single agent to a hybrid agent-graph architecture with Pydantic AI and Text to SQL is documented as a [case study](/case-study/from-single-agent-to-hybrid-agent-graph-architecture-our-journey-with-pydantic-ai-and-text-to-sql/), alongside a [text-to-workflow platform](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) built for an engineering client. If your problem is a model that does not exist, a modeling shop will serve you well. If your problem is a system that has to work inside your business, that is the work we do. ## Conclusion [#](#conclusion) The two firms are easy to confuse and costly to mix up. A modeling shop answers a model question; a transformation consultancy answers a system question. Run the test before you run a procurement process: if the model does not exist, build it; if it does, build the system around it. Scoping the partner to the real problem is the cheapest decision you will make on the project, and one of the most consequential. ![Antoni Kozelski](https://vstorm.co/app/uploads/2026/09/antoni-kozelski-2026.jpg) [Antoni Kozelski](/author/antoni-kozelski/) CEO & Co-founder [Author profile](/author/antoni-kozelski/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![The real cost of skipping transformation consulting](/app/uploads/2026/03/95012D22-051B-4AA2-B47D-51FC15D1C707_1_105_c-768x512.jpeg) ### The real cost of skipping transformation consulting Read article ](/agentic-ai/the-real-cost-of-skipping-transformation-consulting/)[ ![The mid-market manufacturing AI gap in 2026](/app/uploads/2026/06/Group-1777-2-1.png) ### The mid-market manufacturing AI gap in 2026 Read article ](/agentic-ai/the-mid-market-manufacturing-ai-gap-in-2026/)[ ![Agentic AI for production scheduling in manufacturing](/app/uploads/2026/06/Group-1777-6.png) ### Agentic AI for production scheduling in manufacturing Read article ](/agentic-ai/agentic-ai-for-production-scheduling-in-manufacturing/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic AI vs RPA in healthcare: what is the difference and which should you implement URL: https://vstorm.co/agentic-ai/agentic-ai-vs-rpa-in-healthcare-what-is-the-difference-and-which-should-you-implement RPA handles eligibility checks, claims scrubbing and payment posting. Prior authorisation and denials need something else. Where the line falls. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic AI vs RPA in healthcare: what is the difference and which should you implement [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic AI vs RPA in healthcare: what is the difference and which should you implement RPA handles eligibility checks, claims scrubbing and payment posting. Prior authorisation and denials need something else. Where the line falls. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst April 14, 2026 Updated May 2, 2026 10 min read ![Agentic AI vs RPA in healthcare: what is the difference and which should you implement](/app/uploads/2026/04/Group-1777-3.png) On this page0% 1. [Why healthcare runs on automation and where most organisations are today](#why-healthcare-runs-on-automation-and-where-most-organisatio) 2. [What RPA actually does in a healthcare setting](#what-rpa-actually-does-in-a-healthcare-setting) 3. [Where RPA breaks down](#where-rpa-breaks-down) 4. [What agentic AI does differently](#what-agentic-ai-does-differently) 5. [The compliance question: What HIPAA means for agentic AI in healthcare](#the-compliance-question-what-hipaa-means-for-agentic-ai-in-h) 6. [RPA vs agentic AI: which process fits which technology](#rpa-vs-agentic-ai-which-process-fits-which-technology) 7. [Which should you implement: a decision framework](#which-should-you-implement-a-decision-framework) 8. [What a production-grade implementation looks like](#what-a-production-grade-implementation-looks-like) 9. [Key takeaways](#key-takeaways) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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They are built for different types of work. RPA reliably automates structured, rule-based healthcare processes: eligibility checks, claims scrubbing, payment posting. Agentic AI handles what RPA cannot: prior authorisation workflows, denial management, and any process that requires reasoning across fragmented systems. This article explains how each technology works, where each applies in a healthcare setting, and how to sequence your implementation to avoid the failure patterns that affect 95% of enterprise AI pilots._ Most healthcare operations leaders treating **RPA healthcare automation** and **agentic AI in healthcare** as competing bets are asking the wrong question. The question is not which technology wins. It is which processes each one is built for, and what happens when you deploy one where the other belongs. Getting this distinction right is the difference between a working system and a costly pilot that goes nowhere. ## Why healthcare runs on automation and where most organisations are today [#](#why-healthcare-runs-on-automation-and-where-most-organisatio) The financial pressure driving automation in healthcare is not marginal. US health systems collectively spend more than $140 billion annually on revenue cycle management, with manual processes, fragmented vendor landscapes, and outdated technologies contributing to high costs, delays, and errors. according to [McKinsey's January 2026 analysis](https://www.mckinsey.com/industries/healthcare/our-insights/agentic-ai-and-the-race-to-a-touchless-revenue-cycle), citing Harris Williams market data. The RCM process alone typically costs three to four percent of a health system's revenue at scale. RPA is already deeply embedded in that infrastructure. As far back as 2022, 43% of US health systems were using RPA specifically for claims management, according to Becker's Healthcare research cited by [R1 RCM](https://www.r1rcm.com/articles/benefits-robotic-process-automation-claims-management/). But adoption is now at an inflection point. By 2025, [more than 30% of providers had prioritised AI and automation for seven specific RCM use cases](https://www.mckinsey.com/industries/healthcare/our-insights/agentic-ai-and-the-race-to-a-touchless-revenue-cycle), compared with four to five the year before. At the same time, Experian Health's _State of Claims 2025_ survey of 250 healthcare professionals found that while 67% believe AI can improve the claims process, only 14% are currently using it to reduce denials. Awareness has run ahead of deployment and the gap between them is where most organisations are stuck. ## What RPA actually does in a healthcare setting [#](#what-rpa-actually-does-in-a-healthcare-setting) **RPA healthcare automation** works by scripting a fixed sequence of actions that a bot executes against existing interfaces; logging into payer portals, extracting data fields, populating forms, and moving records between systems. It does not reason. It does not adapt. It follows rules exactly as written, every time. This makes RPA highly effective for processes that share three characteristics: structured data formats, stable system interfaces, and no requirement for contextual judgement. In healthcare, those processes include eligibility verification, standard claims scrubbing before submission, payment posting, appointment reminder dispatch, and basic prior authorisation form submission where payer requirements are fixed and well-defined. The documented results in these areas are significant. One large national healthcare provider reduced payment posting time from eight hours to 45 minutes daily after RPA implementation, according to [Valere Health](https://valere-health.com/resources/workflow-automations/rpa-in-healthcare-what-it-is-and-how-its-being-used/). CareSource, a non-profit serving over two million members, achieved 90% invoice automation and a 50% reduction in manual work after deploying UiPath for claims, utilisation management, and prior authorisation. These are not hypothetical results. When the process fits, RPA delivers. ## Where RPA breaks down [#](#where-rpa-breaks-down) The brittleness is predictable. When a payer changes a portal layout, a form field label, or a file format, the bot stops. It does not recognise the change, does not adapt, and requires a developer to rebuild the affected workflow. In a healthcare environment where payer requirements shift frequently, this maintenance burden accumulates quickly. More fundamentally, RPA cannot reason. It cannot read a clinical note to assess whether a prior authorisation request is medically appropriate. It cannot interpret a denial to identify its root cause. It cannot navigate a non-standard claim or make a judgement call when data is missing. The moment a process requires any of those capabilities, RPA is the wrong tool. Not a limited one, but the wrong one. > "RPA isn't dying – it's evolving. When you need something to work the same way every single time – without exceptions, without interpretations – RPA remains unmatched." – Chris Radich, Public Sector CTO at UiPath The corollary is equally direct: when a process does require exceptions and interpretation, RPA will fail at exactly the moments that matter most ( [CIO.com, June 2025](https://www.cio.com/article/4001371/the-future-of-rpa-ties-to-ai-agents.html)). There is also a structural ceiling emerging from regulation. TEFCA and CMS mandates now require actionable, traceable information flows across fragmented healthcare systems, [capabilities that rule-based automation cannot deliver](https://hitconsultant.net/2026/02/23/agentic-ai-healthcare-workflow-automation-prior-authorization/). Interoperability is no longer optional, and it requires more than bots that copy data between screens. ## What agentic AI does differently [#](#what-agentic-ai-does-differently) **Agentic AI in healthcare** is not a faster version of RPA. It is a different architecture. At its core is a reasoning engine, typically a large language model, connected to tools, memory, and an orchestration layer. Instead of executing a fixed script, an agentic system pursues a defined outcome by planning a sequence of actions, adapting when it encounters unexpected inputs, and maintaining context across multiple steps and data sources. Prior authorisation illustrates the difference most clearly. Today, this process is handled manually: staff navigate multiple payer portals, interpret payer-specific documentation requirements, gather clinical records from the EHR, submit the request, and then monitor its status, often across weeks, with follow-up calls and resubmissions when documentation is rejected. According to the [AMA's 2024 Prior Authorisation Physician Survey](https://www.ajmc.com/view/ama-survey-highlights-growing-burden-of-prior-authorization-on-physicians-patients) (conducted December 2024, released February 2025), physicians complete an average of 39 prior authorisations per week, spending approximately 13 hours on the process. 93% report it delays patient care. 31% say requests are often or always denied, many due to documentation gaps that a well-designed agent would have caught before submission. An agentic system handles this end to end: it reads the clinical note, identifies the payer's criteria, surfaces any missing documentation, submits through the portal, monitors status, and, on denial, pulls the relevant records, builds the appeal, and routes it for human review. No staff member clicking through six screens. No queue waiting for someone to notice a rejection. We built a system of this kind for a US healthcare provider serving more than 100,000 members across multiple states. By deploying a multi-channel, pre-appointment AI agent, each doctor now saves more than five hours per week, while patient engagement increased over 20% through personalised, accessible communication. The full case study is available at [vstorm.co](/case-study/multi-channel-ai-agent-in-healthcare/). The broader data is consistent with this pattern. One California healthcare network deployed AI-powered claims review and achieved a 22% decrease in prior authorisation denials by commercial payers and an 18% decrease in denials for services not covered, without adding RCM staff, and saving an estimated 30 to 35 hours per week in back-end appeals work, according to the [AHA and Ailevate, 2025](https://www.aha.org/system/files/media/file/2025/10/Trailblazers_Ailevate_ClaimsDenials.pdf). [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## The compliance question: What HIPAA means for agentic AI in healthcare [#](#the-compliance-question-what-hipaa-means-for-agentic-ai-in-h) Both RPA and agentic AI touch protected health information, and HIPAA applies to both. The compliance architecture, however, is more complex for agentic systems and the regulatory bar is rising. On January 6, 2025, HHS Office for Civil Rights proposed the first major update to the HIPAA Security Rule in 20 years, removing the distinction between "required" and "addressable" safeguards and mandating full encryption of all electronic PHI in storage and transit. Breach notification timelines would shorten from 60 days to 30 days, and continuous monitoring would become a formal requirement. As of publication, this update remains a proposed rule ( [HIPAA Journal](https://www.hipaajournal.com/when-ai-technology-and-hipaa-collide/)). For agentic AI specifically, the compliance requirements go further. A peer-reviewed framework published by researchers at Mississippi State University in April 2025 identifies three non-negotiable controls for HIPAA-compliant agentic systems: attribute-based access control (ABAC) for granular PHI governance, a hybrid PHI sanitisation pipeline to prevent data leakage, and immutable audit trails for compliance verification. Additionally, any third-party API infrastructure that processes PHI, including the LLM provider, requires a valid Business Associate Agreement ( [Neupane et al., arXiv, April 2025](https://arxiv.org/abs/2504.17669)). The vendor complexity problem is real. A 2025 report by Ponemon Institute and Imprivata found that nearly half of surveyed health IT leaders experienced a data breach or cyberattack involving third-party network access in the prior year. As one healthcare security expert put it in [TechTarget](https://www.techtarget.com/healthtechsecurity/feature/Key-HIPAA-compliance-considerations-for-agentic-AI-tools): with agentic AI, one of the most complex compliance challenges is the sheer number of different business associates that may be involved, PHI continues to be PHI as it flows through each system unless properly de-identified at every stage. Practically, this means on-premise deployment or rigorously governed cloud architecture with full data lineage visibility is not optional in a healthcare context. It is a foundational design requirement, not an afterthought. For organisations we work with through our [healthcare agentic AI practice](/agentic-ai-in-healthcare/), governance and compliance are scoped before engineering begins, not bolted on after. ## RPA vs agentic AI: which process fits which technology [#](#rpa-vs-agentic-ai-which-process-fits-which-technology) The table below maps the most common healthcare administrative processes against RPA fit and agentic AI fit. The logic is process-driven, not technology-driven: start from the nature of the work, not from a vendor's capabilities. **Process** **How it is handled today (without automation)** **RPA fit** **Agentic AI fit** **Why** Eligibility verification Staff manually log into payer portals to check coverage before each appointment Strong Optional enhancement Structured, rule-based, stable interface – RPA's natural domain Claims scrubbing Billing staff review each claim for coding errors before submission Strong AI improves accuracy on edge cases Repetitive and rule-governed; AI adds value on complex or ambiguous claims Prior authorisation Staff navigate payer portals, gather clinical documentation, submit and track – 39 per physician per week (AMA 2024) Form submission only Strong Requires reasoning, multi-system navigation, exception handling, and status tracking Denial management Billing team manually reviews denial codes, drafts appeals, resubmits claims Templated letters only Strong Root cause analysis and adaptive appeal drafting require contextual judgement Clinical documentation Physicians dictate or manually enter notes during and after patient encounters Not applicable Strong Unstructured, context-dependent – outside RPA's capability entirely Patient communication (pre-appointment) Nurses or admin staff call patients to collect updates and confirm appointments Automated reminders only Strong Personalised dialogue and adaptive follow-up require reasoning, not scripted triggers Payment posting Billing staff manually extract remittance data and reconcile against outstanding claims Strong Optional Structured, high-volume, stable – RPA reduces this to near-zero manual effort ## Which should you implement: a decision framework [#](#which-should-you-implement-a-decision-framework) This is not a binary choice. The practical question for most healthcare operations teams is one of sequencing: where does each technology apply, and in what order should you build? Start with RPA where processes are fully standardised, interfaces are stable, data formats are predictable, and no contextual judgement is required. Eligibility verification and payment posting are the clearest examples. These processes deliver strong ROI quickly, and they build the automation maturity; clean data, integrated systems, staff buy-in; that agentic AI depends on later. Move to agentic AI where processes involve unstructured data, multi-system coordination, frequent exceptions, or decisions that require context. Prior authorisation and denial management are the highest-value entry points for most healthcare organisations. McKinsey's January 2026 analysis recommends starting with back-end RCM; accounts receivable follow-up, underpayment management, denial resolution; as the lowest-risk zone for initial agentic AI deployment, because the work is rules-governed in structure but variable in content. The hybrid model, RPA executing the deterministic core, agentic AI handling exceptions and cross-system orchestration, is where most mature healthcare organisations are heading. As Blue Prism's 2026 analysis puts it: [the sweet spot is hybrid automation, let AI handle the unpredictable parts and keep RPA for the reliable core processes](https://www.blueprism.com/resources/blog/future-ai-agents-trends/). What determines whether this works is readiness, not ambition. Agentic AI requires clean data infrastructure, integration access, governance frameworks, and a clear definition of success before the first agent goes live. Organisations that skip this assessment face a well-documented failure pattern. According to the MIT Media Lab's Project NANDA report, _"The GenAI Divide: State of AI in Business 2025"_ (July 2025), 95% of enterprise AI pilots deliver no measurable P&L impact, based on analysis of 300 deployments across industries. The biggest ROI, the report finds, consistently comes from back-office automation where AI is given the right integration and context to do its job ( MIT NANDA, July 2025). ## What a production-grade implementation looks like [#](#what-a-production-grade-implementation-looks-like) The gap between a working agentic system and a stalled pilot is not the quality of the underlying model. It is the quality of the implementation decision that precedes it. Organisations that move furthest with **healthcare workflow automation** share a consistent pattern: they start narrow; one high-volume, high-pain process; and build human oversight into the workflow from day one. They instrument the system so every agent decision is traceable and auditable, which matters both for HIPAA compliance and for the ongoing tuning that makes agentic systems improve over time. And they structure the engagement so their internal team gains the capability to maintain and extend the system, rather than creating a dependency. This is the logic behind our [TriStorm methodology](/tristorm/): Transformation Consulting identifies where agentic AI creates the highest operational leverage; Technology Consulting translates that into an architecture blueprint; Agentic AI Engineering builds and deploys the production system with observability built in from the start. No handoff gaps. No discovery that the use case was not viable after three months of development. In healthcare, that process almost always starts with prior authorisation or denial management, the highest-volume, highest-pain points in most revenue cycle environments, and the processes where the case for agentic AI is clearest and fastest to prove. ## Key takeaways [#](#key-takeaways) RPA and agentic AI solve different problems. RPA belongs on structured, stable, high-volume processes where the rules never change. **Agentic AI in healthcare** belongs on processes that require reasoning, multi-system coordination, and the ability to handle exceptions, such as prior authorisation, denial management, and clinical documentation are the clearest entry points. The organisations seeing the strongest results are not choosing between the two: they are using RPA to automate the deterministic core and agentic AI to handle everything the bots cannot. The sequencing matters, the governance matters, and the choice of where to start matters more than the technology itself. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![How to integrate AI agents with EHR systems: Epic, Cerner, and custom integrations](/app/uploads/2026/04/Group-1777-5.png) ### How to integrate AI agents with EHR systems: Epic, Cerner, and custom integrations Read article ](/agentic-ai/how-to-integrate-ai-agents-with-ehr-systems-epic-cerner-and-custom-integrations/)[ ![The Emperor’s New AI agents](/app/uploads/2026/04/2024-01-20__Y4A0427-scaled.webp) ### The Emperor’s New AI agents Read article ](/agentic-ai/the-emperors-new-ai-agents/)[ ![Agentic AI revenue cycle management: from pilot to production for mid-market health systems](/app/uploads/2026/04/Group-1777.png) ### Agentic AI revenue cycle management: from pilot to production for mid-market health systems Read article ](/agentic-ai/agentic-ai-revenue-cycle-management-from-pilot-to-production-for-mid-market-health-systems/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Our findings in agentic automation from the Nordics Summit URL: https://vstorm.co/agentic-ai/agentic-automation-in-finance-lessons-from-the-nordics-summit Three lessons from Vstorm projects for financial institutions: earlier tech decides AI success, early adopters had it worst, structure beats authorship. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Our findings in agentic automation from the Nordics Summit [Agentic AI](/ai-blog-news/#agentic-ai) # Our findings in agentic automation from the Nordics Summit Three lessons from Vstorm projects for financial institutions: earlier tech decides AI success, early adopters had it worst, structure beats authorship. ![Bartosz Adam Gonczarek](https://vstorm.co/app/uploads/2025/02/2024-01-20__Y4A0464_small-1-e1738697918189.jpg) [Bartosz Adam Gonczarek](/author/bartosz-adam-gonczarek/) Chief Transformation Officer and Co-founder of Vstorm October 6, 2026 5 min read ![Our findings in agentic automation from the Nordics Summit](/app/uploads/2026/10/cover-agentic-automation-in-finance-lessons-from-the-nordics-summit-v3.png) On this page0% 1. [Lesson 1: The success of agentic AI depends on the maturity of earlier technology](#lesson-1-the-success-of-agentic-ai-depends-on-the-maturity-o) 2. [Lesson 2: Early adopters had it worst](#lesson-2-early-adopters-had-it-worst) 3. [Lesson 3: Structure matters far more than who wrote the code](#lesson-3-structure-matters-far-more-than-who-wrote-the-code) 4. [The model is the smallest part of the lesson](#the-model-is-the-smallest-part-of-the-lesson) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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My aim was simple: to share what we know from actual project experience, not visions or forecasts. Participants appreciated that approach, and after the session several told me this was exactly what they had been missing at similar events. If that is so, the three lessons I shared from the stage are worth going through here. ## Lesson 1: The success of agentic AI depends on the maturity of earlier technology [#](#lesson-1-the-success-of-agentic-ai-depends-on-the-maturity-o) In the race for the newest and the latest, it is easy to forget the obvious: new technologies come in layers, laid on top of earlier ones. Those layers matter a great deal in a game as data-heavy as AI adoption. If earlier systems cannot expose coherent data models to AI, or the integration options are limited, there is no point expecting miracles from the agentic AI sitting on top. What the saying "agentic AI is the cherry on top" really means is that the success of LLM adoption depends on: 1. semantically structured, high-quality data sources, which depend on 2. open systems with robust, machine-accessible APIs, which depend on 3. an experienced team that got all of this in order in time, often before AI ![Slide: Lesson one, the success of agentic AI depends on the maturity of the earlier components. Agentic AI sits on top of three layers: semantically structured, quality data sources; open-ended systems with robust, machine-accessible APIs; and an experienced team that knows its strengths and weaknesses](/app/uploads/2026/10/nordics-summit-lesson-one-maturity-of-earlier-components.png) Lesson one: agentic AI is the cherry on top of three earlier layers. That is what Vstorm project reality shows. The most successful projects are those where the data model was cleaned, corrected and stabilised years before LLMs took the spotlight. AI can only be executed well where back-end systems are ready to integrate new components. There, the AI part starts as an isolated island of functionality before it grows into something more comprehensive, such as Vstorm's AgenticOS. Most of all, it works when the team on the client's side knows its systems and builds for the future. The success of an agentic project is often the fruit of their work from years before. ## Lesson 2: Early adopters had it worst [#](#lesson-2-early-adopters-had-it-worst) Everyone knows the AI space moves fast, but what does that mean for adoption projects? It means the tools and materials change while you build. If AI adoption were a race, those who started first would be worst off, because the engines kept improving and the runners-up got better equipment to catch up and take the lead. That is exactly what happens. ![Slide: Lesson two, early adopters had the worst. Then, 2024 to 2025: GPT-4-class models weaker at planning and tool use, orchestration frameworks in beta, Pydantic AI not yet released, no typed outputs or evaluation tooling. Now, 2026: models that plan across steps, mature tool calls, stable frameworks with versioned APIs and observability, typed outputs and evaluation built in, known patterns](/app/uploads/2026/10/nordics-summit-lesson-two-early-adopters.png) Lesson two: what builders had in 2024–2025 compared with 2026. In Stockholm I shared the story of a multi-agent system a bank deployed in 2025 to support its clients financially. It was built with LangChain and with models that were far weaker at planning, context and tool use, so the architecture had to be spread across several workers orchestrated together. The same engineering team told me that if they started the project in 2026, they would use a single agent and a simpler architecture. Why? Because everything has matured since: not only the models, but also the components for evaluating and observing their work. Many people are surprised that systems shipped a year ago ran on orchestration frameworks that were still in beta. [Pydantic AI](https://pydantic.dev/articles/pydantic-ai-v1) (the framework Vstorm uses most often) reached version 1 in September 2025, and [LangChain](https://www.langchain.com/blog/langchain-langgraph-1dot0) in October 2025. ![Timeline: Pydantic AI was in beta from December 2024 for nine months and became stable with v1 in September 2025; LangChain spent fifteen months in beta, then had the 0.1.x stable line and reached 1.0 in October 2025](/app/uploads/2026/10/nordics-summit-agentic-ai-framework-maturity-timeline.png) How long the frameworks took to stabilise, as shown in the keynote in September 2026. So every system shipped before then was built on a beta version of these frameworks, and any system running on a stable version of LangChain or Pydantic AI is about a year old at most. That is not much time to gather experience with agentic AI and see the return on the investment. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Lesson 3: Structure matters far more than who wrote the code [#](#lesson-3-structure-matters-far-more-than-who-wrote-the-code) One way AI is reshaping the market is its ability to write code. Vibe coding works well enough that many people wonder where its limits are and whether code written by people is better or worse than code written by a machine. The lesson we take from Vstorm projects is that the real question is not who writes the code, a person or a machine. It is what structure the code is written from. Code written by engineers comes from their understanding, their mental model, of what the solution should become. There is a caveat: even a great understanding does not guarantee great code. That depends on how good they are at the job. With machine-generated code, the result depends on how well the intent is defined. The code will reflect how clear the description, rules and guidelines were. So the structure you start with, before any code is written, matters a great deal. It can take the form of written specifications or of mental models locked in someone's head. It pays to capture it well, both to make it clearer for people and to improve the code a machine produces. ![Slide: Lesson three, structures matter far more than who did what. Humans' mental models and a well-written prescription, rule or guideline both feed into structures, meaning the problem space and the means to traverse it, which lead to the solution](/app/uploads/2026/10/nordics-summit-lesson-three-structures.png) Lesson three: code comes from structures, whether they live in someone's head or in a written guideline. ## The model is the smallest part of the lesson [#](#the-model-is-the-smallest-part-of-the-lesson) All three lessons are about what surrounds the model: the systems built before it, the tooling that matured around it and the clarity of what we ask it to build. That works in favour of organisations that have not started yet. The preparation is ordinary engineering work, and the tools available today are more stable than those the first adopters had. ![Three tinted checklist cards, each tied to a keynote lesson: data through machine-accessible APIs (teal), an architecture for today's models (blue), intent written down (ochre).](/app/uploads/2026/10/fig-agentic-automation-in-finance-st-nrd-checks.png) Three checks before any build starts. From the projects we have delivered, we recommend three checks before any build starts: 1. Confirm that the systems the agent will rely on expose coherent data through machine-accessible APIs. 2. Choose the architecture for the models and frameworks available today, not for those available when the idea first came up. 3. Write down the intent, rules and guidelines clearly enough for either an engineer or a model to build from. These lessons come from the same keynote as the companion post, [What agentic AI is still taken for: three lessons from Stockholm](/agentic-ai/agentic-ai-misconceptions-three-lessons-from-stockholm/), which covers what decision-makers still assume about agentic AI. If you work at a financial institution and want to check your systems against these three points, you can [book a consultation](/schedule-a-meeting/) with Vstorm. ![Bartosz Adam Gonczarek](https://vstorm.co/app/uploads/2025/02/2024-01-20__Y4A0464_small-1-e1738697918189.jpg) [Bartosz Adam Gonczarek](/author/bartosz-adam-gonczarek/) Chief Transformation Officer and Co-founder of Vstorm [Author profile](/author/bartosz-adam-gonczarek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![What agentic AI is still taken for: three lessons from Stockholm](/app/uploads/2026/10/cover-agentic-ai-misconceptions-three-lessons-from-stockholm-v3.png) ### What agentic AI is still taken for: three lessons from Stockholm Read article ](/agentic-ai/agentic-ai-misconceptions-three-lessons-from-stockholm/)[ ![Why agentic AI transformation strategy decides who wins](/app/uploads/2026/10/cover-why-agentic-ai-transformation-strategy-decides-who-wins-v3.png) ### Why agentic AI transformation strategy decides who wins Read article ](/agentic-ai/why-agentic-ai-transformation-strategy-decides-who-wins/)[ ![Agentic AI doctor notes generators: ambient scribes beyond transcription](/app/uploads/2026/10/cover-agentic-ai-doctor-notes-generators-v3.png) ### Agentic AI doctor notes generators: ambient scribes beyond transcription Read article ](/agentic-ai/agentic-ai-doctor-notes-generators/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic process automation services URL: https://vstorm.co/agentic-ai/agentic-process-automation-services Agentic process automation services — a scoped engagement from workflow audit through production deployment, ending in full code ownership and an operator runbook. [Home](/)/[Industries](/industries/)/Agentic Process Automation Services # Agentic process automation services An engagement that ends in a handoff, not a retainer. A scoped path from workflow audit to a production agent your team owns and operates — every phase ends in a tangible deliverable, not a status update. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Your team owns both lanes Agent Your team Intake Check Draft Approve Execute 1. 01Agent works 2. 02Team approves 3. 03Handoff Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most automation engagements end in a retainer, not an outcome. A lot of process-automation services are structured to keep you dependent on the vendor — proprietary platforms, ongoing licensing, engineers only the vendor understands. We structure engagements the opposite way: every phase produces something you can run without us, and the engagement ends when you can. We scope the engagement around your process, not a pre-packaged offering that half-fits. Sources Agent Outcomes TriStorm engagement Target process Existing systems Team capacity Production agent Operator runbook Full codeownership every phase ends in something tangible What you get ## Deliverables at each phase of the engagement Every phase produces something concrete — not a status update. 01 ### Workflow and data audit A documented map of the target process, its data sources, and where an agent can act versus where it must escalate. 02 ### Working prototype and evaluation suite A functioning agent validated against your real data, with a test suite scoring decision accuracy before production commitment. 03 ### Production deployment and monitoring The agent live in your environment with observability and alerting — not a prototype left to degrade unmonitored. 04 ### Operator runbook and handoff Documentation and training so your team monitors, maintains, and extends the system independently. Delivery path ## The TriStorm engagement structure Every phase gates the next — no full commitment before the value is proven. 01 ### Strategize Workflow audit, feasibility assessment, and an ROI model — a prioritised implementation plan before any build starts. Workflow & data auditFeasibility assessmentPrioritised plan 02 ### Build A working agent on your real data, with an evaluation suite and guardrails — evidence for a build decision. Working prototypeEvaluation suiteProduction path 03 ### Transform Production rollout with monitoring and governance, ending in full ownership transfer to your team. Production deploymentMonitoring & alertingOwnership transfer Client results ## Proof from production engagements [View all case studies](/case-studies/) [![Schmitt-Thompson Clinical Content](/logos/clients/schmitt-thompson.svg) Healthcare **44% → 98%**raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark Guideline-executing medical triage: the LLM answers triage assessment questions; disposition follows the guideline deterministically — 98% on the open 50-scenario benchmark, on par with a 94.4% nurse-panel correct-disposition rate. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) [![ARIJ Network](/logos/clients/arij.png) Media **EN + AR**one agent answering in English and Arabic from ARIJ's own materials A bilingual English/Arabic autonomous agent embedded in ARIJ's Moodle environment, answering only from ARIJ's own knowledge base. Read case study](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) FAQ ## Agentic process automation services, answered All FitUse casesDeliveryOperations What exactly do I get from an agentic process automation engagement, deliverable by deliverable? + A workflow and data audit, a working prototype validated against your real data, a production deployment with monitoring, and an operator runbook that lets your team run the system without us. Every TriStorm phase ends in something tangible, not a status update. Do you offer a fixed-scope engagement, or is everything custom? + Every engagement starts with the same Proof of Value structure — one process, real data, a working agent, evaluated before any production commitment. Scope beyond that follows what the Proof of Value actually finds, not a pre-sold package. Can you take over a process automation project a previous vendor left unfinished? + Yes. We audit existing integration gaps, reasoning failures, and observability blind spots, and resolve them with your target infrastructure in mind — this does not require starting over. What does the evaluation suite you build actually check? + Decision accuracy against known-correct outcomes, policy compliance where rules apply, and regression testing so a later change doesn't silently break something that worked. Every agent action is logged with reasoning for review. How involved does our team need to be during the engagement? + As involved as your process knowledge requires — we need someone who understands the workflow, but we do not require dedicated engineering time from your side. We handle the build; your team validates the outcomes and receives the handoff. What happens after the engagement ends? + You own the code, the evaluation harness, and the runbook. There is no retainer requirement — support and expansion are available if you want them, not a condition of ownership. Start with a scoped engagement ## Get a Proof of Value before any full commitment. A 30-minute call scopes the first phase — real deliverables, evaluated before you commit further. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Agentic process automation URL: https://vstorm.co/agentic-ai/agentic-process-automation Agentic process automation — agents that read unstructured requests, reason across systems, and complete a task end to end, handling the exceptions traditional RPA cannot. [Home](/)/[Industries](/industries/)/Agentic Process Automation # Agentic process automation Automation that handles the exception, not just the happy path. We build agents that read unstructured requests, reason across your systems, and complete a task end to end — the layer above the automation you already run. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Your process exception Intake Match record Update system Manual queue waits on a person Agent reads, reasons, completes 01 happy path 02 exception 03 agent lane Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## RPA automates the happy path. Everything else lands on a person. Rule-based automation replays a fixed sequence and breaks the moment an input varies — a document in a different format, an email with an unusual request, a case that needs judgment. Agentic automation reads the actual content, reasons across the relevant systems, and handles that variation directly. We map which parts of a process an agent can complete directly, and where a person should stay the final decision-maker. Sources Agent Outcomes Process agent Unstructuredrequests Source documents System state Completed task Human review Audit log every completed task traces to its source input Use cases ## Where agentic automation earns its place Processes with unstructured inputs and a real judgment call. 01 ### Document review and extraction Reads unstructured documents, extracts what matters, and cross-references it against other systems — the same mechanism that compressed weeks of due diligence into minutes. 02 ### Multi-step approval workflows Coordinates approvals across systems and stakeholders, escalating only the cases that genuinely need a decision-maker. 03 ### Cross-system reconciliation Cross-checks records across systems that don't talk to each other, flagging discrepancies with reasoning attached instead of a blanket report. 04 ### Exception handling for existing automation Picks up the cases your RPA or workflow tools already route to a person, and resolves the ones that follow a documentable pattern. Delivery path ## From process audit to a production agent TriStorm keeps process accuracy and engineering aligned. 01 ### Map the process and exceptions We audit the target process, its exception patterns, and system boundaries — ranking automation candidates by volume and complexity. Process & exception auditSystem integration mapPrioritised use case 02 ### Build and validate the agent We implement against real process data, with an evaluation suite scored before any output reaches production. Working prototypeEvaluation suiteEscalation rules 03 ### Deploy with monitoring Production rollout with monitoring and audit logging, plus a structured handoff so your team runs the system independently. Production deploymentAudit trail & monitoringOperator runbook Client results ## Proof from production agentic automation [View all case studies](/case-studies/) [![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) Engineering software **2 hrs → 3 min**to generate a validated workflow Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [![Mixam](/_astro/mixam-logo.B59_GcxD.png) Print on demand **95.4%**Success rate in workflow results A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) FAQ ## Agentic process automation, answered All FitUse casesDeliveryOperations What is agentic process automation, in plain terms? + Software that reads a task, reasons across the systems and documents relevant to it, and completes the task end to end — deciding within guardrails and escalating when it should not decide alone. It is the layer above traditional automation, not a replacement for every automation tool you already run. How is agentic process automation different from RPA? + RPA replays a fixed sequence of clicks and breaks when the input varies. Agentic automation reads the actual content of a request — an email, a document, an exception — and reasons about what to do, adapting to variation instead of failing on it. What kind of process is a good candidate for agentic automation? + Processes with unstructured inputs and a judgment call — document review, multi-step approvals, cross-system reconciliation — not simple, fully-structured data transfers that a scheduled script already handles well. Does agentic automation replace our existing RPA or workflow tools? + Usually not — it sits alongside them, handling the exceptions and judgment calls those tools route to a person. We integrate with what you already run rather than replacing it wholesale. What is the typical timeline to a working system? + A scoped Proof of Value — one process, real data, a working agent — typically lands in 3-6 weeks, following the same TriStorm phases as any Vstorm engagement. Do we own the system after it's built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in. Start with one process ## Map one process worth automating end to end. A 30-minute call identifies the exceptions your current automation can't handle, and a realistic path to a working agent. [Book a discovery call](/schedule-a-meeting/)[Agentic process automation services](/agentic-ai/agentic-process-automation-services/) --- ## Agentic tokenization: how AI agents will pay URL: https://vstorm.co/agentic-ai/agentic-tokenization-how-ai-agents-will-pay Agents can browse and click but cannot prove a cardholder is present. How tokenization, SCA, AP2 and UCP answer that, and what it means for mid-market. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Agentic tokenization: how AI agents will pay [Agentic AI](/ai-blog-news/#agentic-ai) # Agentic tokenization: how AI agents will pay Agents can browse and click but cannot prove a cardholder is present. How tokenization, SCA, AP2 and UCP answer that, and what it means for mid-market. ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer September 22, 2026 21 min read ![Agentic tokenization: how AI agents will pay](/app/uploads/2026/10/cover-agentic-tokenization-how-ai-agents-will-pay-v3.png) On this page0% 1. [Agentic commerce: between tokens and tokens](#agentic-commerce-between-tokens-and-tokens) 2. [Why payment companies introduced tokenization](#why-payment-companies-introduced-tokenization) 3. [What tokenization means in AI payments](#what-tokenization-means-in-ai-payments) 4. [One layer up: how the agent finds what to buy](#one-layer-up-how-the-agent-finds-what-to-buy) 5. [The consent confirmation](#the-consent-confirmation) 6. [The other half of trust: proving who the agent is](#the-other-half-of-trust-proving-who-the-agent-is) 7. [Challenges with tokenization](#challenges-with-tokenization) 8. [Summary](#summary) 9. [Sources](#sources) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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What used to be a demo is now a scenario ordinary users describe out loud: find a good four-day window at the seaside, book a hotel there, and buy me something to read while I am away. For a human assistant that is browsing, a checkout and a few clicks. An agent can do the browsing part. It can drive a pointer and fill in a form. What it cannot do is satisfy the one requirement the entire payment system was built around: proving that the cardholder is here. ## Agentic commerce: between tokens and tokens [#](#agentic-commerce-between-tokens-and-tokens) Agentic commerce looks simple from the outside, and agents really can operate the human-facing interfaces that already exist. The complication is that once you remove the human, three different things called tokens start doing the work at once: in the model, in the payment, and increasingly in the money itself. These are the operational definitions used below. ![Three tinted cards for three meanings of token: a unit of text in the model (about four characters), a network token standing in for the card number in the payment, and a stablecoin pegged to a real currency as money.](/app/uploads/2026/10/fig-agentic-tokenization-how-ai-ex-tok-meanings.png) Three different things called tokens, all used at once by agent payments. ### Tokens in LLM operations For language models, a token is the basic unit of text the model processes. A short word is roughly one token; longer or uncommon words cost more. Another rule of thumb puts one token at about four characters. Tokens are used for two things: * Pricing: LLM services bill per token, and the more capable the model, the higher the rate. * Context: the context window is measured in tokens, not words. ### Tokens in payments In payment processing, a token is a string that stands in for sensitive card data so the real number never travels. Tokens are generated by checkout tools, digital wallets and payment networks, then passed through the network to authorize the payment. The ones that matter here are network tokens, issued by the card networks themselves: Visa through the Visa Token Service, Mastercard through the Mastercard Digital Enablement Service. If there is a breach or a man-in-the-middle attack, the attacker gets the token, not the card. ### Tokens as money There is a third meaning, and it turns out to matter for agents. A stablecoin is a digital token pegged to a real currency at a fixed rate. Where the first two are a unit of text and a stand-in for a card, this one is the money being moved. It becomes relevant later, for the class of payments that card rails cannot economically carry. ## Why payment companies introduced tokenization [#](#why-payment-companies-introduced-tokenization) A payment token behaves like a gift card issued by the network: valid in one shop, at one moment. If the data leaks, only the token is exposed, and no matter how many tokens leak, the money stays where it is. This is not new. EMVCo published its payment tokenization specification in 2014, and Visa and Mastercard launched their token services the same year as the infrastructure underneath Apple Pay. The goal was to remove the card number from the transaction entirely. That solved one problem: card data exposure. It left a second one open, namely whether the person initiating the payment was the account holder at all, rather than someone moving the user's money to a fake shop. That second problem was answered by regulators rather than by the networks, through [**Strong Customer Authentication**](https://www.ecb.europa.eu/press/pr/date/2025/html/ecb.pr251215~e133d9d683.en.html) under PSD2, applicable from 2019 and fully enforced for cards across most of the EEA by the end of 2020. SCA requires payment companies to check that the specific user was present in the payment flow. Two answers, two different problems, arriving from two directions. Together they produced a very tight system. ### The SCA basics SCA requires at least two of three elements before a payment is confirmed: * Something only the user has: a chip card, a registered device or a hardware token. * Something only the user knows: a password or a PIN. * Something only the user is: a fingerprint, an iris scan or face recognition. Under SCA, a stolen card number is not enough. The attacker still needs the fingerprint or the password, which makes theft considerably harder. The effect shows up in where fraud concentrates: [joint ECB and European Banking Authority reporting from December 2025](https://www.ecb.europa.eu/press/pr/date/2025/html/ecb.pr251215~e133d9d683.en.html) found card fraud rates up to 17 times higher when the payee sat outside the EEA, where SCA is not legally required and often not applied. The same report is more careful about credit transfers, where higher fraud rates were observed on SCA-authenticated transactions, because SCA tends to be applied to the higher-risk, higher-value ones in the first place. What works here becomes the obstacle in agentic payments. Regulators and payment companies spent years making sure the user is the one who pays. An agent is not the user, and still wants to pay on the user's behalf. ## What tokenization means in AI payments [#](#what-tokenization-means-in-ai-payments) To do what a modern assistant is expected to do, pick the window and book the hotel, an agent has to pay. With SCA in place and the user sitting at the keyboard, that is straightforward. For an agent, several streams have to be combined. ### Agents paying other machines A user checking the weather opens a website. A system doing the same thing calls an API, and an API usually wants paying. The problem is the amount: these payments rarely exceed a dollar, and the processing fee exceeds the value of the transaction. The economics are worth stating plainly. A card authorization carries a fixed cost of roughly 20 to 30 cents before any percentage fee, which makes it uneconomic for an inference call that costs thirty. Below about a dollar, a different rail is needed. Three tools are in use: * Stablecoin tokens: the payment is issued in a stablecoin pegged to a real currency, settled on-chain in seconds at a fraction of a cent. * The x402 protocol: HTTP status code 402 Payment Required was reserved in the 1990s alongside the rest of the standard and then went essentially unused for three decades. In 2025 Coinbase built a protocol on top of it: the server answers 402 with a set of payment requirements, the agent pays in USDC, a facilitator verifies and settles, and the resource comes back. In July 2026 the protocol moved under the Linux Foundation, with Visa, Mastercard and American Express all among the members of its foundation. * Stripe Machine Payments and Mastercard Agent Pay for Machines: the established payment companies now ship their own machine-to-machine tooling. Both can use x402 and neither is limited to it. Mastercard launched Agent Pay for Machines in June 2026 specifically for high-frequency, low-latency, low-value payments, settling across everything from cards to stablecoins. In the holiday example, this layer is what buys the weather data and anything else the system needs to pick the window. ### Agents paying on behalf of the user Machine-to-machine payments are the easy half. They happen between companies and need no user attention at all. Buying something for the user needs more control and more formality, because the amount is open-ended. A hotel runs from a handful of dollars for a bad motel to thousands for an apartment. The book can be a paperback or a collector's edition. Whatever the item, the agent has to pay in a more human way, and there are three ways to do it: * Virtual cards: the agent gets a card scoped to one transaction. This is the closest thing to the gift card above, and the point where the analogy gains a spending limit. The drawback is volume, since an agent settling twenty invoices a day generates twenty card requests. * Agentic tokens: issued by the card networks, for example Mastercard Agentic Tokens or the tokenized credentials inside Visa Intelligent Commerce. These extend the same MDES and Visa Token Service infrastructure with two additions, an agent identifier and a session scope, which removes the need to issue a card at all. The trade-off is dependence: the credential is now issued and governed by the network, and so is the policy that binds it. * Shared payment tokens: the primitive behind ChatGPT's checkout, defined jointly by OpenAI and Stripe in the Agentic Commerce Protocol. The token is scoped to one merchant and one cart total. ACP is an open standard rather than a two-company arrangement, and PayPal adopted it in October 2025 as a second compliant provider. A merchant on another processor can join through the Shared Payment Token API or the Delegated Payments Spec. Crucially, nothing else moves: the merchant remains the merchant of record, accepts or rejects the order, charges the payment method, remits tax, and handles fulfilment and returns as before. The pattern resembles a manager approving a budget and letting the team spend it. It applies inside a business too. An agent renewing tooling subscriptions issues one credential per vendor, each scoped to a single merchant identifier, with a monthly budget and an expiry at the end of the month. So the centre of gravity has moved. The system used to exist to prove a human was present when their money was spent. They are not. It now exists to prove that consent was given beforehand. ## One layer up: how the agent finds what to buy [#](#one-layer-up-how-the-agent-finds-what-to-buy) Everything above describes how the money moves. None of it explains where the hotel and the book came from. An agent cannot pay for a room it cannot find, at a price it cannot read, with a cancellation policy it cannot parse. That is a separate layer with its own standard. Google's Universal Commerce Protocol, released in January 2026, is an open, vendor-agnostic standard co-developed with Shopify, Etsy, Wayfair, Target and Walmart, and endorsed at launch by more than twenty partners including Adyen, American Express, Best Buy, Mastercard, Stripe, Visa and Zalando. It defines a common language between consumer surfaces, AI Mode in Search, Gemini and others, and merchant backends, covering catalog search, cart building and checkout. Businesses integrate through APIs, through Agent2Agent or through the Model Context Protocol, and the protocol is designed to work with AP2 for the payment step. It is worth being precise about the boundary. UCP does not process or store raw payment credentials; payments stay with regulated providers using tokenization and secure delegation. It does not displace the storefront either, and the merchant remains the merchant of record, with both native and embedded checkout variants available. UCP sits above the payment stack rather than inside it. The consumer-facing expression arrived at Google I/O in May 2026 as Universal Cart: one persistent basket that follows the shopper across Search, Gemini, YouTube and Gmail, checking out through Google Pay or through the retailer's own flow, with brands including Nike, Sephora, Target, Walmart and Wayfair. Checkout is rolling out in the United States first, then Canada and Australia, and later the United Kingdom. For a business, the practical consequence of this layer has nothing to do with payments. Data consistency becomes a hard requirement for appearing in agentic shopping at all. A human shopper forgives a stale price or a missing variant and clicks through anyway. An agent comparing three hotels on structured data does not. ## The consent confirmation [#](#the-consent-confirmation) An agent being able to pay says nothing about whether it was allowed to. Managing consent for the use of someone else's money created a new layer of control. AP2, the Agent Payments Protocol, is the anchor. Google introduced it in September 2025 as an open standard for how agents pay on behalf of users, and it works through mandates: cryptographically signed digital contracts proving the user genuinely intended to buy a given product or service. The mandates come in three types, each answering a different question. The Intent Mandate is created when the user gives the first instruction, find me a good four-day window at the seaside, book a hotel and buy me something to read. The Cart Mandate is signed at final approval and locks the specific items and the specific price, so what the user pays for is exactly what the user saw. The Payment Mandate carries the transaction itself. Technically, these mandates are built on W3C Verifiable Credentials, an open standard for tamper-evident digital statements that anyone can check without calling back to the issuer. That matters for the merchant: the hotel does not need to trust the agent, and does not need to call Google to ask whether the agent is telling the truth. It verifies a signature. In practice, consent is granted as a policy rather than as a click. The user enrolls a card, names the agent and sets the rules: a maximum per transaction, a monthly cap, allowed merchant categories, an expiry window, and optionally a step-up rule requiring a fingerprint or a push confirmation above a certain amount. The agent operates freely inside those rules, and anything outside them fails at the network level. The authorization is revocable in real time from the issuer's app; the next attempt simply does not authorize. Applied to our example, the split is clean. The weather data is bought inside the policy with no human involvement. The hotel, at several hundred dollars, triggers a Cart Mandate and a confirmation. The paperback, at fifteen dollars, sits below the step-up threshold and goes through on the policy alone. ![One intent mandate splits into three payments along an amount axis: weather data on the machine rail and a paperback on card rails inside the policy, and the hotel above a red step-up line needing a Cart Mandate and confirmation; the policy rules are listed below.](/app/uploads/2026/10/fig-agentic-tokenization-how-ai-ex-tok-holiday.png) One request, three payments, one step-up. Two developments in 2026 pushed the layer further. In April, Google released AP2 v0.2 with support for what it calls Human Not Present payments, where agents buy autonomously against pre-authorized instructions, such as taking a limited-run ticket the moment it goes on sale. At the same time Google donated AP2 to the FIDO Alliance, the body behind passkeys, and Mastercard donated a complementary standard called Verifiable Intent, which creates a tamper-proof log of user-authorized agent actions. Consent for agents now sits on the same standards track as passwordless login, and the protocol is no longer governed by a single vendor. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## The other half of trust: proving who the agent is [#](#the-other-half-of-trust-proving-who-the-agent-is) A mandate proves the user agreed. It says nothing about who is holding the credential. That is the merchant's question, and it needs a different answer. The scale of the problem is measurable. AI-driven traffic to United States retail websites [rose by more than 4,700% between 2023 and 2024](https://www.forbes.com/sites/joanverdon/2025/08/21/gen-ai-driven-traffic-to-us-ecommerce-sites-up-4700-adobe-reports/), and [doubled year on year again by June 2026](https://www.digitalcommerce360.com/2026/06/17/adobe-ai-referred-traffic-to-retail-sites-doubles-in-a-year/). Shoppers like it: in the same Adobe research, [85% of those who had used generative AI to shop said it improved their experience](https://business.adobe.com/blog/generative-ai-powered-shopping-rises-with-traffic-to-retail-sites). Akamai's 2025 fraud reporting, cited by Visa, put the growth of AI-powered bot traffic at 300%, with the commerce sector alone absorbing more than 25 billion AI bot requests across a two-month period. The merchant is left with a dual failure mode: bot detection that blocks legitimate agentic transactions, and genuine scrapers and scalpers passing themselves off as shopping assistants. Visa named three specific merchant problems when it released the Trusted Agent Protocol in October 2025, developed with Cloudflare and published in the Visa Developer Center and on GitHub: bot detection that blocks legitimate agent traffic, support for agent-driven guest and logged-in checkout, and visibility into the consumer standing behind the agent. TAP is an open framework built on existing web infrastructure, which lets approved agents pass that information to the merchant rather than making the merchant guess. Mastercard approaches the same problem from the credential side. Each Agentic Token is issued uniquely to a single agent. One agent holds one token, it cannot impersonate another agent, and the token cannot be reused across agent instances. That produces accountability at the level of the agent itself, which is what a dispute investigation later needs. By mid-2026 the layer had grown its own tooling. Visa added Agent Score, which lets merchants assess whether AI agents can actually navigate, understand and complete tasks on their site, and an Agentic Directory listing agents and merchants Visa has verified as legitimate participants. The framing is worth noticing: a directory of approved agents is a gate, and gates tend to acquire owners. ## Challenges with tokenization [#](#challenges-with-tokenization) The infrastructure above exists, is documented and runs in production. Everything around it is unsettled. ### Responsibility and accountability The hardest problem in agentic payments is not technical. It is who pays when the agent gets it wrong. Suppose the agent books the hotel for the wrong weekend, or books a non-refundable room when the user wanted flexibility, or buys the two-hundred-dollar collector's edition because the description matched better. Nothing was hacked. The token was valid, the mandate was signed, the policy was honored. The outcome is still not what the user wanted. None of the existing frameworks answers this. AP2 provides the audit trail, a record of what was authorized and when, but it does not allocate liability between the agent platform, the merchant, the card issuer and the consumer. The card networks preserve the consumer's right to a chargeback and encode agent identity so a disputed transaction can be attributed. That is attribution, not allocation. Merchant and consumer surveys consistently place trust and liability above technology as the main barrier to adoption, which is a polite way of saying the industry is waiting for someone else to accept the risk first. There is a sharper version of the same problem on the machine-to-machine side. Card rails come with a dispute and reversal mechanism built up over decades. Stablecoin transfers do not. An agent that has paid for eight hundred API calls in USDC has no chargeback to file, because there is nothing to reverse. The two rails our example uses disagree at the foundations about whether a payment can be undone. ### Security Agents introduce attack surfaces that did not exist when a human sat at the checkout. The most obvious is prompt injection. An agent reading a hotel page is reading text, and text can carry instructions. A page can contain hidden content telling the agent the room is available at a different rate, that the cancellation policy is favorable, or that it should proceed to a different booking endpoint. The agent holds a valid token and a valid mandate for a hotel booking, and has simply been talked into using them somewhere else. The credential works exactly as designed, which is why the payment industry's existing controls do not detect it. The second is agent impersonation, described above. As a security problem rather than a commercial one it reads differently: the identity layer is young, the directories of approved agents are new, and until they mature, a merchant's only real defense against a hostile agent is the bot detection that also blocks the legitimate ones. Third, and least discussed, is the behavior of the merchant side of the conversation. If agents can be talked into things, they can be manipulated at scale, pushed toward unwanted offers, nudged into overpayment or drawn into purchases the user never contemplated. Guardrails against dark patterns aimed at agents, and against collusion between agents, remain an open research area rather than a product feature. ### Compliance Europe is the hardest place to do this, and the reason is the mechanism described at the top of this article. SCA works because a human proves presence. Agentic payments work because a human proved consent earlier. Reconciling the two is a live legislative question. The European Parliament and Council reached a provisional political agreement on the PSD3 and PSR texts on 27 November 2025, moving SCA toward a more risk-based approach and extending liability for online fraud. What is still unresolved is where an agent sits in the delegation chain, whether SCA can be delegated to an agent platform, and who carries the liability at each hop until the new rules take effect. Payments regulation is also not the only regime in play. An agent that pays for data in USDC, runs on a third-party model and executes in someone else's cloud falls under the AI Act, MiCA and DORA at the same time, and no single lens governs it. The AI Act's transparency obligations went into application from August 2026, with the AI Digital Omnibus [deferring the Annex III high-risk obligations to 2 December 2027 and Annex I to 2 August 2028](https://www.gibsondunn.com/eu-ai-act-omnibus-agreement-postponed-high-risk-deadlines-and-other-key-changes/), which puts a hard date on the problem. Add PCI-DSS, data residency and financial-crime monitoring, and compliance stops being a footnote. It becomes the reason a regulated business cannot deploy this yet, even where the technology works. ## Summary [#](#summary) Agentic payments are best understood as a shift in what the payment system is trying to prove. For a decade the industry built an increasingly good answer to one question: is the cardholder here? Tokenization removed the card number from the transaction, SCA proved a human was on the other end, and the combination cut fraud sharply. Agents invalidate the premise. Nobody is here. The question becomes whether the owner of the money agreed to this in advance, within limits that can be verified afterwards. Answering it takes four distinct layers, built by four different sets of companies. Identity establishes which agent is asking. Consent, through AP2 and its mandates, establishes what was authorized. Credentials, meaning virtual cards, agentic tokens and shared payment tokens, carry the payment without exposing the card. Settlement moves the money, over card rails for purchases and over stablecoins for the fractions of a cent that cards cannot economically carry. Above all four sits discovery, where UCP defines how the agent finds anything worth buying in the first place. A single request like the one in our example touches every one of them. That layering explains where the commercial fight is actually happening, and it is not where the demos suggest. The visible competition is over the shopping experience, over whose chat window the purchase happens in. The durable position is one layer down. Visa and Mastercard are not racing to build a better storefront; they are racing to make their token infrastructure the recognition layer every agent transaction has to pass through, because whoever authenticates the agents ends up taxing the flow. A verified-agent directory, an agent score and a network-issued credential are not features. They are a toll booth being installed while everyone watches the checkout button. The gap between this architecture and its use is worth keeping in mind. The most prominent consumer deployment of agentic checkout, Instant Checkout in ChatGPT, launched at the end of September 2025 and was [withdrawn in March 2026](https://openai.com/index/powering-product-discovery-in-chatgpt/), with OpenAI stating that the initial version had not offered the flexibility the company aspired to and refocusing on product discovery while letting merchants use their own checkout. It never got large: [The Information counted a dozen live Shopify merchants, and Shopify told Forrester the number was closer to 30 and climbing](https://www.forrester.com/blogs/what-it-means-that-the-leader-in-agentic-commerce-just-pulled-back/). Forrester's own ConsumerVoices survey from March 2026 found that among online adults in the United States, United Kingdom and Canada who regularly use answer engines, completing a purchase inside the answer engine was the least-adopted use case of all. The machine-to-machine side has the opposite problem: volume that may not mean what it looks like. In the thirty days to mid-July 2026, x402 handled [about 75 million transactions moving about $24 million](https://www.coindesk.com/tech/2026/07/15/visa-mastercard-and-ripple-join-the-standard-letting-ai-agents-pay-in-stablecoins), an average of roughly 32 cents. How much of that is demand rather than plumbing is contested. An Artemis analysis published with Visa in July 2026 classified 39% of transactions and 89% of dollar volume as wash, test or internal transfers rather than someone paying for something, which drops the average genuine payment to fourteen cents. ![Two split bars: 61% of x402 transactions and 11% of dollar volume were someone paying for something, with 39% and 89% wash, test or internal transfers; the average payment across all traffic was about 32 cents.](/app/uploads/2026/10/fig-agentic-tokenization-how-ai-ex-tok-x402.png) Most x402 dollar volume was not someone buying something. The pattern that has settled is narrower and more useful than the original vision: discovery moves into AI, the transaction stays with the merchant. For a business, that reframes the question. It is not whether to adopt agentic checkout. It is whether product data, pricing, availability and policies are accurate and machine-readable enough for an agent to recommend, and eventually to buy. The plumbing is being built by Visa, Mastercard, Google, Stripe and Coinbase. Being findable and buyable through it is the part nobody can do on a company's behalf. ### Where this leaves a mid-market business That last point is an engineering brief in disguise. If being findable and buyable is the part nobody can do on your behalf, it is worth being precise about what that part is, because it is not a payments project and it does not wait for the standards to settle. We at Vstorm read it as two connected problems, neither of them a payments problem. The first is making commercial data agent-ready. Product information, prices, stock levels and returns or cancellation policies have to be consistent, current and machine-readable across every system that holds them, then exposed through retrieval an agent can query without inventing a price or missing a variant. That is [RAG engineering](https://vstorm.co/rag-development-service/) and system integration against the [e-commerce](https://vstorm.co/ai-in-ecommerce-and-retail/) or [print-on-demand](https://vstorm.co/agentic-ai-in-print-on-demand/) stack a business already runs. The second is the buying side. The same protocols that let an agent transact for a consumer let a business run its own agents inside signed, revocable policy limits: one credential per vendor, a monthly cap, an expiry, a step-up above a threshold. Renewing tooling subscriptions, reconciling supplier invoices, buying the data a pricing model depends on. The control question is no longer presence. It is consent recorded in advance and auditable afterwards, which is a system-design decision before it is a payments one. Both sit inside how we already work. Strategizing to find where agent-readiness creates real operational leverage rather than chasing a demo, Building to design the retrieval and integration architecture, and Transformation to ship it as a production system the client owns outright, on open-source foundations, with no lock-in to a single network or model. Our [TriStorm methodology](https://vstorm.co/tristorm/) carries that from roadmap to deployed system under one team. The infrastructure in this article belongs to the big money traders. Being readable and buyable through it stays with you, and that is the part we build. ## Sources [#](#sources) Card networks * Mastercard, Mastercard unveils Agent Pay, 29 April 2025 * Mastercard, First live agentic transaction in Hong Kong, 27 March 2026 * Mastercard, Agent Pay for Machines, 10 June 2026 * Visa, Find and Buy with AI: Visa Unveils New Era of Commerce (Visa Intelligent Commerce), 30 April 2025 * Visa, Visa Introduces Trusted Agent Protocol, 14 October 2025 * Visa, Akamai and Visa Collaborate to Build Trust in Agentic Commerce (bot-traffic figures), 17 December 2025 * Visa, Intelligent Commerce Connect, 8 April 2026 Consent and standards * Google Cloud, Announcing the Agent Payments Protocol (AP2), 16 September 2025 * Google, Google donates Agent Payments Protocol to FIDO Alliance (AP2 v0.2, Human Not Present, Verifiable Intent), 28 April 2026 * FIDO Alliance, Google donates Agent Payments Protocol to FIDO Alliance, 28 April 2026 Discovery * Google Developers Blog, Under the Hood: Universal Commerce Protocol (UCP), 11 January 2026 * Google, Google Shopping introduces Universal Cart, Google I/O, May 2026 * Protocol specification, SDKs and reference implementations, ucp.dev Credentials and checkout * OpenAI, Buy it in ChatGPT: Instant Checkout and the Agentic Commerce Protocol, 29 September 2025 * OpenAI, Powering Product Discovery in ChatGPT (contains the Instant Checkout withdrawal), 24 March 2026 * Stripe, Stripe and OpenAI launch Instant Checkout, 29 September 2025 * Stripe, Agentic Commerce Suite, 11 December 2025 * PayPal, OpenAI and PayPal Team Up to Power Instant Checkout, 28 October 2025 * Forrester, What It Means That The Leader In “Agentic Commerce” Just Pulled Back (Instant Checkout merchant count; ConsumerVoices survey, March 2026), 7 March 2026 Machine-to-machine * Coinbase, Coinbase and Cloudflare will launch the x402 Foundation, September 2025 * Linux Foundation, Operational launch of the x402 Foundation, 14 July 2026 * Stripe, Introducing the Machine Payments Protocol, March 2026 * CoinDesk, Visa, Mastercard and Ripple join the standard letting AI agents pay in stablecoins (75m payments, $24m over 30 days), 15 July 2026 * Artemis with Visa, Agentic Payments from the Ground Up (wash, test and internal transfer filtering), July 2026 Traffic and adoption research * Adobe, Generative AI-Powered Shopping Rises with Traffic to Retail Sites (4,700% traffic growth; 85% improved experience), August 2025 * Digital Commerce 360, Adobe: AI-referred traffic to retail sites doubles in a year, 17 June 2026 ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer [Author profile](/author/konrad-budek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Prompt injection is not the vulnerability. Your agent’s tools are.](/app/uploads/2026/10/cover-ai-agent-security-prompt-injection-v3.png) ### Prompt injection is not the vulnerability. Your agent’s tools are. Read article ](/agentic-ai/ai-agent-security-prompt-injection/)[ ![Single agent vs multi-agent systems: when one agent is not enough](/app/uploads/2026/10/cover-single-agent-vs-multi-agent-systems-v3.png) ### Single agent vs multi-agent systems: when one agent is not enough Read article ](/agentic-ai/single-agent-vs-multi-agent-systems/)[ ![AI agents can act: can you still control them?](/app/uploads/2026/10/cover-agentic-ai-control-plane-v3.png) ### AI agents can act: can you still control them? Read article ](/agentic-ai/agentic-ai-control-plane/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AgenticOS as the destination URL: https://vstorm.co/agentic-ai/agenticos-as-the-destination An agentic OS runs all of a company's agents in one place. Why mid-market firms should own theirs, and how our open-source AgenticOS does it. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/AgenticOS as the destination [Agentic AI](/ai-blog-news/#agentic-ai) # AgenticOS as the destination An agentic OS runs all of a company's agents in one place. Why mid-market firms should own theirs, and how our open-source AgenticOS does it. ![Bartosz Adam Gonczarek](https://vstorm.co/app/uploads/2025/02/2024-01-20__Y4A0464_small-1-e1738697918189.jpg) [Bartosz Adam Gonczarek](/author/bartosz-adam-gonczarek/) Chief Transformation Officer and Co-founder of Vstorm June 26, 2026 Updated September 28, 2026 7 min read ![AgenticOS as the destination](/app/uploads/2026/06/2024-01-20__Y4A0401-scaled.webp) On this page0% 1. [Small Giants](#small-giants) 2. [The Trends](#the-trends) 3. [The alignment that matters](#the-alignment-that-matters) 4. [The end-goal of AI adoption for SMBs](#the-end-goal-of-ai-adoption-for-smbs) 5. [What an agentic OS looks like in software](#what-an-agentic-os-looks-like-in-software) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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Mid-market companies risk conflating AI adoption with AI transformation. The distinction matters: adopting off-the-shelf tools creates operational dependency; true transformation builds sovereign agentic systems that the company owns end-to-end. Three macro trends are reshaping how this is done — the commoditisation of coding, the rising value of specialist engineering, and an abundance of open-source building blocks. Vstorm guides mid-market companies through all three, using the TriStorm methodology to move from concept to deployed, lock-in-free agentic systems. The result, as seen with Mixam and Synera, is transformation that compounds rather than constrains. Agentic AI for mid-market companies is no longer a horizon concept: it is a present-day competitive decision. Unlike traditional AI tools that automate isolated tasks, modern agentic AI systems coordinate multi-step, complex workflows across enterprise systems and multiple data sources, making the question not whether to adopt, but how to do so without creating long-term dependency on a single provider. ## Small Giants [#](#small-giants) Ambitious Middle-market companies have one thing in common: trying to excel at what they do. The success in that makes them into what we call “Small Giants” — not forcing scale, but in the quality of their offering. And a key aspect for them is how technology is deployed in their service offering and business operations. But the software business is also a business. That fact alone creates the risk of getting the cost-value balance wrong. While technology providers want to squeeze a profit, business owners are at work trying to extract as much value as possible at the lowest cost. The middle-market decision-makers have always had to be clever about this — leveraging tech for the benefit of the company while keeping the cost of it at bay. The same holds true with the current wave of artificial intelligence adoption with Large Language Models in the service of business. And while it started recently, there are already important lessons to learn from early adopters’ choices and how they navigate the trends. ## The Trends [#](#the-trends) Market adoption of agentic AI is shaped by three important trends that we see have the biggest impact on project decisions: ### Trend 1 — The commodization of coding The differentiation between the power user and the programmer is slowly fading into obscurity. The coding used to be little more than black magic that, in the days of Claude Code, Cursor and Copilots, is becoming obsolete. Knowing how to produce software used to be costly, which in essence spurred the market towards standardized software. Costly-coded once and used by millions made it affordable but… rigid. Bearing that burden, however contributes to the second trend, that is: ### Trend 2 — A growing need for stellar engineering When virtually anyone can code, the result is a vast quantity of solutions with questionable design and execution. Quality, however, is still a function of good engineering. And in that regard, the engineering craft ranks high on the priority list. This is where the concept of a forward-deployed engineer takes root. To illustrate the point, in a world of DIY sheds, a great building remains the result of rigid engineering and a breadth of architectural vision. It is in this layer that multi-agent systems capable of reasoning, coordinating, and executing across interconnected processes separate themselves from the proof of concept stage. ### Trend 3 — An abundance of raw computing materials Innovation in the technology domain made it possible to enjoy a plethora of competing technological building blocks that can be leveraged by the middle market. The list includes various LLM model types or AI models, harness frameworks, and AI stacks all the way to AI development platforms. But just as in the real world, they come with usage terms, obligations, and restrictions. At Vstorm, we see those three trends being leveraged by leading companies, which we internally call “the tinkerers.” They benefit from the trend of _commodizing coding_ by tinkering with AI tools until they can’t progress any further without _specialized engineering_. When that happens, they call on us to improve their concepts or guide them across a measurable threshold in their systems that they wouldn’t have been able to cross alone. All of that is made possible thanks to the _abundance of raw computing building blocks;_ the systems, models, or frameworks; they freely choose from. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## The alignment that matters [#](#the-alignment-that-matters) We at Vstorm stay true to our customers’ needs: _excellence and sovereignty in how technology is used in the service of end-customers and their organizations._ Vstorm, by its origin and mission, does not need to mask any competing goals to just pretend to work in favor of our customers. By lacking investors and operating independently, we can and do contribute to their goals directly. While the goal for each middle-market company is to embrace AI (enabling them to automate complex processes and analyze data at scale) as part of their key systems, we help them do that in keeping with the three macro trends: * It is desirable for SMB’s to acquire the necessary skills to maintain, upgrade, and build parts of their solutions, effectively leveraging the “commoditization of coding.” * It is possible to augment their team with external AI talent we provide and to be led to a desired business goal that can only be attained with the help of generative AI and agentic systems. Vstorm fills the gaps, offering excellence in engineering and end-to-end AI transformation. * It is necessary to build internal autonomy from out-of-the-box solutions and system providers, thus achieving agentic AI without vendor lock-in and reducing token and subscription costs. This is doable given the abundance of raw computing technologies available. To make it possible, we learned at Vstorm how to guide customers beyond the technological frontier to achieve their goals and become small giants in their fields. The outcome is our proprietary agentic AI transformation roadmap methodology, which we call Tristorm. We also accelerate the building of customers’ agentic systems with the help of predefined building blocks that we ship in the form of open-source components. With these methods and assets, our customers can build their systems faster while avoiding lock-in, which is all too often the case with proprietary components. Together, this guidance and acceleration lead to the agentic transformation of several of our customers. The AI we helped them build allowed them to scale (like Synera), helped them remake their business for a successful acquisition (like Mixam), or pivot into new ventures (like in the case of Rendreal). ## The end-goal of AI adoption for SMBs [#](#the-end-goal-of-ai-adoption-for-smbs) The technology environments at the end of transformation can be thought of as the agentic bloodstream of the company. Built ground up, domain after domain, agentic AI becomes a crucial part of customer-facing and internal processes. The trick is to arrive at that state without building dependency on frontier models or software providers. This leads to sovereign systems, with their code and prompts owned by the company, which controls both the data and how it is processed. The entire system, end-to-end. This is critical in the long run. How good is the advantage a company can obtain over its competition if the advantage can be replicated by its competition, which is a risk if prompts and proprietary data spill across company walls? Unlike enterprise AI deployments built on closed, proprietary platforms, each company’s system should remain entirely their own: a genuine extension of what makes them distinct. Each company has its own technology-based bloodstream, and adding agents to it simply cannot increase its vulnerability, thus risking the demise of the organization. Transformation done right is that which builds on the best the company has to offer and tops it off with agentic AI. The successful transformation stories of our projects show that it can be done, if the three macro trends are properly leveraged and the transformation partner fully aligns its goal with the company it works for. These are the foundations of a comprehensive AgenticOS. One leads to engineering and consulting — a Vstorm team embedded in your operations, building the systems your company will own. The other leads to a different kind of partnership — we take equity and lead the transformation from the inside, as your interim executive and engineering team. Choose the path that fits. ## What an agentic OS looks like in software [#](#what-an-agentic-os-looks-like-in-software) An agentic OS is the layer that runs all of a company's agents in one place, with shared tools, context, permissions and budgets. We publish ours as [open source](https://github.com/vstorm-co/agenticos): [AgenticOS](/agenticos/), under the Apache-2.0 licence and built on Pydantic AI. It starts from four questions that a company with agents in five places usually cannot answer: what do we run, what did it cost, what did it touch, and who approved it. AgenticOS keeps one record for every agent. Teams build agents in the browser from skills, context files and MCP tools, run them on a schedule or a trigger, and set a budget that stops a run before the model call. It runs on infrastructure the company controls, on its own Docker and Postgres. Models, parsing, embeddings and tracing can still use external services, depending on configuration, so the team decides what leaves its network. That is the sovereignty argument above, in code you can read before the first call. ![Bartosz Adam Gonczarek](https://vstorm.co/app/uploads/2025/02/2024-01-20__Y4A0464_small-1-e1738697918189.jpg) [Bartosz Adam Gonczarek](/author/bartosz-adam-gonczarek/) Chief Transformation Officer and Co-founder of Vstorm [Author profile](/author/bartosz-adam-gonczarek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Buy vs build agentic AI: a single-workflow decision](/app/uploads/2026/03/9A3FD2D7-544E-44EA-BAFE-1B0C8EC14E5B_1_105_c-2-768x392.jpeg) ### Buy vs build agentic AI: a single-workflow decision Read article ](/agentic-ai/buy-vs-build-agentic-ai-a-single-workflow-decision/)[ ![From RPA to agentic AI: rebuilding the revenue cycle](/app/uploads/2026/06/Group-1777-2-2.png) ### From RPA to agentic AI: rebuilding the revenue cycle Read article ](/agentic-ai/from-rpa-to-agentic-ai-rebuilding-the-revenue-cycle/)[ ![Agentic AI for predictive maintenance](/app/uploads/2026/06/Group-1777-9.png) ### Agentic AI for predictive maintenance Read article ](/agentic-ai/agentic-ai-for-predictive-maintenance/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AI agent evaluation and reliability URL: https://vstorm.co/agentic-ai/ai-agent-evaluation-and-reliability Why a single-run score overstates an AI agent's reliability, what to measure instead (pass^k, traces, judges) and how to set a target you can defend. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/AI agent evaluation and reliability [Agentic AI](/ai-blog-news/#agentic-ai) # AI agent evaluation and reliability Why a single-run score overstates an AI agent's reliability, what to measure instead (pass^k, traces, judges) and how to set a target you can defend. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst October 9, 2026 19 min read ![AI agent evaluation and reliability](/app/uploads/2026/10/cover-ai-agent-evaluation-and-reliability-v3.png) On this page0% 1. [Why AI agent evaluation is hard](#why-ai-agent-evaluation-is-hard) 2. [What to measure and in what order](#what-to-measure-and-in-what-order) 3. [Make failures attributable](#make-failures-attributable) 4. [Setting a target you can defend](#setting-a-target-you-can-defend) 5. [What public AI agent benchmarks can and cannot tell you](#what-public-ai-agent-benchmarks-can-and-cannot-tell-you) 6. [Building an evaluation set from your own traffic](#building-an-evaluation-set-from-your-own-traffic) 7. [Using LLM judges without inheriting their errors](#using-llm-judges-without-inheriting-their-errors) 8. [Engineering for reliability](#engineering-for-reliability) 9. [Turning evaluation into evidence](#turning-evaluation-into-evidence) 10. [Where to start](#where-to-start) 11. [Frequently asked questions](#frequently-asked-questions) 12. [Conclusion](#conclusion) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. 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On τ-bench airline, a GPT-4o agent succeeded 42% of the time on one attempt and 20% across four. * Split tasks into state-verifiable and judge-dependent before choosing metrics, and use an LLM judge only where no end-state check exists, validated against human labels. * Set the target against the process the agent replaces, and count the cost of detecting and fixing failures, not only the failure itself. * Regulation (EU) 2026/1744 moved Annex III high-risk obligations to 2 December 2027; Article 50 transparency duties, general-purpose AI obligations and the Article 5 prohibitions were not deferred. Before a company lets an AI agent loose on its customers or its books, someone has to say how often it will get the job right. The usual answer is a score from one test run. That shows what the agent can do, but not whether it does it every time. Take τ-bench, a public benchmark from Sierra, which sells customer-service agents. In its airline section, an agent has to change or cancel bookings for a simulated customer while following the airline's policy, and a script then checks whether the booking database ended up as it should. Given one attempt at each task, an agent built on OpenAI's GPT-4o succeeded 42% of the time, the equivalent of 21 of the 50 tasks. Given four attempts at the same task, its chance of succeeding on all four was 20%, the equivalent of 10 of the 50. These are averages across repeated runs of every task, not counts from a single test. The first number measures capability, whether the agent can do a task at all, and is known as pass@1. The second measures reliability, whether it does the task every time, and is known as pass^4, or pass^k for k runs. A test that runs each task once produces only the first number, and it is easy to mistake it for the second. Both come from the [leaderboard in the original τ-bench repository](https://github.com/sierra-research/tau-bench), which has since been replaced by τ³-bench. ![Two rows of 50 task squares for a GPT-4o agent on τ-bench airline: 21 filled for pass@1 (42%), 10 filled for pass^4 (20%), with the 11-task gap outlined.](/app/uploads/2026/10/fig-ai-agent-evaluation-and-e3.png) One attempt passes 42%; four in a row pass 20%. Run 1 Run 2 Run 3 Run 4 One-run test says Four-run test says Task A pass pass pass pass pass pass Task B pass fail pass fail pass or fail, 50/50 fail _An illustration, not τ-bench data: a task the agent solves only some of the time inflates the first number and disappears from the second._ This article assumes you know what an AI agent is. If not, start with our [guide to agentic AI](https://vstorm.co/agentic-ai/what-is-agentic-ai-a-simple-guide-for-small-and-medium-businesses/). ## Why AI agent evaluation is hard [#](#why-ai-agent-evaluation-is-hard) ### Errors multiply, they do not add ![Curve of 0.95 to the power of n falling to 36% at twenty steps.](/app/uploads/2026/10/fig-ai-agent-evaluation-and-e1.png) At 95% per step, twenty independent steps succeed 36% of the time. Take an agent that completes each step correctly 95% of the time. Over twenty steps, if the steps were independent, end-to-end success would be 0.95²⁰, or about 36%. A per-step number that sounds excellent produces an end-to-end number that does not. The independence assumption is doing the work here and it does not hold: agent steps are correlated, because a bad retrieval at step three poisons everything downstream. Treat this calculation as an illustration under independence, not a bound or forecast. What it establishes is directional. The longer the trajectory, the wider the gap between step accuracy and task success. ### The agent changes the world An agent that books a flight has booked a flight. Re-running it is not free, and a confident closing message saying the booking succeeded is not evidence that it did, which is why the more careful benchmarks compare the end state of a database against an annotated goal state rather than scoring text, the approach [τ-bench](https://arxiv.org/abs/2406.12045) takes. The same asymmetry applies to your own harness: anything that writes to a real system needs a sandbox with resettable state. ### Same input, different trajectory Two runs of the same task take different paths: sampling temperature, tool latency, retrieval order, a transient API error that retries on one run and not the other. None of it is visible in a single-run score. All of it is visible in pass^k. ### The environment drifts under you Tools change, APIs deprecate fields, policy documents get edited, and the model is updated by a provider on a schedule you do not control. An agent that passed its evaluation in March can fail the identical evaluation in June without a line of your code changing. ## What to measure and in what order [#](#what-to-measure-and-in-what-order) ![Layer 0 splits tasks into state-verifiable (teal, e.g. match an invoice to its purchase order) and judge-dependent (violet, e.g. draft the email to the supplier). Both feed layer 1, outcome, checked by an assertion or a judge; layer 2, trajectory, also counts towards reliability; layer 3, service level, sits outside it.](/app/uploads/2026/10/fig-ai-agent-evaluation-and-e2.png) Decide whether a task can be verified before you choose how to measure it. ### Layer 0: Is this task verifiable at all? Every metric list assumes something it rarely states: that the task has a checkable ground-truth end state. Many agent tasks do not. Split your task inventory in two before choosing any metric. * **State-verifiable** tasks end in a condition you can assert against: a record updated, a ticket closed and routed to the right queue, a transaction posted with the right amount, a file written with the right contents. For these, correctness is a comparison, and it is cheap, objective and repeatable. * **Judge-dependent** tasks do not: a summary, a piece of advice, a drafted reply, a research brief. There is no end state to compare, so correctness has to be assessed by something: a human, a rubric, or a model. Everything below follows from which side a task falls on. Suppose the agent handles supplier-invoice exceptions: "match this invoice to the purchase order and flag the discrepancy" is state-verifiable, while "draft the email explaining the discrepancy to the supplier" is not. Evaluating the whole workflow requires both methods. ### Layer 1: Outcome **Task success** against an end-state check, not a final-message check: a harness that scores the agent's own account of what it did is measuring fluency, not work. * **pass@k and pass^k** measure success in at least one attempt and in every attempt, respectively. Report both, and ask vendors for the second. If a vendor cannot produce it, run k trials yourself on a small subset during evaluation. Pick k from how often the same task recurs in production. The τ-bench paper reports k up to eight. * **Cost per successful task** includes human rework. If an agent fails a third of the time, its low cost per call leaves out the expense of having a person redo those tasks. ### Layer 2: Trajectory Trajectory metrics help explain why a task succeeded or failed. Track tool-call correctness, step count, loop detection, and recovery after a failed call. A twenty-step turn and a three-step turn both return one answer, so a loop is invisible in a totals dashboard. You see it only if cost and steps are attributed per step. ### Layer 3: Service-level Track latency, escalation rate, human override rate, and time to resolution. These matter commercially, but keep them outside the reliability definition above. An agent can be fast, cheap and available while being inconsistent, and conflating the two is how teams report green dashboards for a system users have stopped trusting. ### What this costs, and where to economise Running pass^k costs k times a single run, labelling trajectories costs human time, and validating a judge costs more human time. None of it is free, and an engineering lead will say so within ten minutes. Three economies keep it affordable. Run pass^k on a stratified subset: you need enough cases per stratum to see the decay, not the whole corpus. Sample trajectory labelling rather than doing it exhaustively. And run a smaller tier in continuous integration (CI) than at the release gate: CI catches regressions fast, the release gate is thorough. What the spend buys is worth stating, because it is easy to present this as pure cost. The programme's value is the difference between shipping at a reliability you measured and shipping at one you assumed, priced with the same four questions later in this article. If the cost of an undetected failure is low, that difference is small and you should build less of this than the article describes. If it is high, the evaluation is cheaper than one incident. ### Offline and online answer different questions Offline evaluation is reproducible, belongs in CI, and proves you did not regress on tasks you already knew about, but it cannot anticipate inputs you have not seen. Online evaluation, sampling live traffic and watching escalation and override rates, catches what the offline set does not contain, but it is not reproducible and cannot gate a release. A programme with only one of them has a predictable blind spot. ## Make failures attributable [#](#make-failures-attributable) None of the trajectory metrics above are computable without traces rather than logs, with a span per step. Each span carries the tool name, the arguments, the outcome, latency, token cost, and an identifier that lets a failed production run be pulled out and turned into a test case later. The test of adequate instrumentation: when a run fails, can you say which call failed, what it was given and what it cost, without re-running anything? Instrument to a standard rather than to a vendor, and read the standard before building on it. [OpenTelemetry's GenAI semantic conventions](https://github.com/open-telemetry/semantic-conventions-genai) now live in their own repository, separate from the core conventions, with no tagged release and an unresolved schema URL in the README. Use them, keep a mapping layer between their attribute names and your dashboards, and budget for a rename. One boundary worth stating plainly: telemetry records that 1,200 tokens came back in 850 milliseconds, not whether they were right. Evaluation sits above telemetry, not inside it, and buying an observability tool does not get you an evaluation programme. ## Setting a target you can defend [#](#setting-a-target-you-can-defend) There is a genre of statistic about agent projects failing before they reach production, and you have seen a version of it. The published estimates vary by tens of percentage points, because they count different populations against incompatible definitions of "production." Some mean any live deployment, some mean deployment at scale, some mean deployment with full security sign-off. Several are vendor-published with no stated methodology, which is why none of them is quoted here. The spread is the point. A range that wide is not a measurement, and no figure inside it can tell you whether your agent is good enough, because none of them is measuring your task, your traffic or your cost of failure. ### Measure against the incumbent, not against zero A success rate is uninterpretable on its own. A rate of 70% is excellent if the process it replaces achieves 40%, and unacceptable if it achieves 95%. So establish the baseline first, on the same task set, before the agent exists. For the invoice handler: what proportion of exceptions does the current team resolve correctly, and how often does an error reach the supplier? Once the agent is running, that measurement is much harder to take honestly, because the comparison is no longer neutral. ### Value when right, cost when wrong Four questions. They are a framing, not a formula: 1. What does a success earn? 2. What does a failure cost? 3. What does _detecting and remediating_ a failure cost? 4. Is that cost a single number, or a long-tailed distribution? The third is the one most often skipped, and in our experience it is usually the one that decides the answer. A 90%-reliable agent drafting marketing copy is useful: the failures cost an edit and they are obvious. A 90%-reliable agent submitting regulated filings is a different proposition, not mainly because the stakes are higher but because the failures are not obvious. The asymmetry that matters is between detected and undetected failure, which is why the detection term is not optional. ### When the honest answer is "do not build an agent" High stakes plus imperfect reliability points to an assistant with an approval step rather than an autonomous agent. That is a legitimate outcome of this analysis and it should be reachable without anyone losing face. The related move is to narrow the task until reliability is acceptable. Narrowing works, and it has a natural stopping point: keep narrowing only as long as the task still requires non-determinism. Once a task can be specified completely, it is a workflow, and a workflow built as a workflow is more reliable, cheaper and easier to audit than the same thing built as an agent. ## What public AI agent benchmarks can and cannot tell you [#](#what-public-ai-agent-benchmarks-can-and-cannot-tell-you) Public benchmarks are useful for comparing models and useless for predicting how your agent will behave. Four questions are worth asking of any of them. How is success verified: end state, text match, or a model judge? What is the task horizon, minutes or hours? Short-horizon results do not extrapolate to long-running work. Does it report a reliability metric at all, or only single-attempt scores? And is the task set current, with scores comparable across its versions? The fourth is the one people skip, and the τ-bench family shows why. The original repository now directs users to τ³-bench as the current version, and its [maintained repository](https://github.com/sierra-research/tau2-bench) lists more than 75 task fixes: incorrect expected actions removed, ambiguous instructions clarified, impossible constraints corrected. It also states that, on its banking\_knowledge domain, results produced before and after its v1.0.1 grading update are not comparable, and that affected leaderboard submissions were re-graded. That is a benchmark doing its job properly, and it is also a warning: a score measures a specific task set at a specific version, and both move. Quote the version or do not quote the score, including for the figures at the top of this article, which is why they are labelled. Apply the same four questions to SWE-bench, GAIA, WebArena or whatever appears next. The questions travel, but a table of scores does not. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Building an evaluation set from your own traffic [#](#building-an-evaluation-set-from-your-own-traffic) ### Source cases from production, not from imagination Hand-written test cases encode what the team already thinks the agent should handle. Production traffic encodes what users actually send, which is different and duller. Sample across the real distribution, including the boring majority. An evaluation set made only of interesting edge cases will overstate difficulty and miss the regressions that matter. For the invoice handler, that means most of the set is ordinary quantity and price mismatches, not the three exotic cases everyone remembers. ### Every incident becomes a test case When something fails in production, the trace becomes a case in the set. This loop is what stops the same bug shipping twice, and it is the first thing to fall away when a team is busy. ### How many cases, and how to know the number actually moved Enough cases that the smallest improvement you care about sits outside your noise floor. You do not need statistics to find that floor: run the same evaluation twice against the same agent with nothing changed, and the spread between those two runs is it. Anything smaller is not a result. That measurement takes an afternoon and it prevents a common and expensive failure: over-reading small movements. The difference between 70% and 75% on 40 cases, two cases, is noise, and teams that treat it as progress tune themselves backwards. ### Checking the evaluation set against production Check the evaluation set on a fixed schedule, with a named owner: * Does the case mix still match the traffic distribution, or has usage moved? * Do its scores move in the same direction as production outcomes, or have the two decoupled? Decoupling means the set has stopped measuring the thing you care about. * When production fails a class of case the set does not contain, that is a coverage gap. The fix is a new case class, not a tweak to an existing case. The first time a supplier sends a credit note instead of an invoice, that is a new class, not a difficult example of an old one. ### Freeze it, version it, hold a slice back A frozen, versioned set is what makes a regression gate meaningful. Without it, a score change could be a change in the agent or a change in the ruler. A held-out slice stops the set quietly becoming a target to tune against. ### Label the trajectory, not only the outcome Per-step labels, covering right tool, right arguments and correct recovery, feed evaluations, fine-tuning and reward models. Outcome-only labels still support evaluation, but they train weaker reward models ([Lightman et al.](https://arxiv.org/abs/2305.20050) found per-step supervision significantly outperforms outcome supervision on maths reasoning), and they cannot tell you why anything failed. ## Using LLM judges without inheriting their errors [#](#using-llm-judges-without-inheriting-their-errors) ### Use a judge only where Layer 0 says you must Reaching for a judge because it is easy to wire up is an avoidable mistake. The invoice handler needs a judge for the supplier email and none for the purchase-order match. ### Validate the judge before trusting its scores Score a human-labelled slice and report chance-corrected agreement, not raw agreement. Raw agreement flatters a judge on any task with an unbalanced label distribution: one that always answers "pass" agrees with humans most of the time on a set where most things pass. Judge validation is itself an evaluation, with its own labelled set, owner and cadence. Budget for it, or do not use a judge. ### Known failure modes Test your judge for each of these on your own data, because their size varies by judge, by task and by model version, and a published figure for one judge does not transfer to another: * **Position bias:** does the verdict change when you swap the order of the two responses? * **Verbosity bias:** does the longer answer win when it should not? * **Self-preference:** does the judge favour output from its own model family? * **Domain calibration:** does it agree with a specialist on specialist content? * **Temperature sensitivity:** does the same input scored twice produce the same verdict? Each is a test you can run in an afternoon, and running them beats knowing what the literature reports, because what you need is the number for your judge on your data. ### Judge drift is a silent regression Pin the judge model version. When it changes, re-baseline before comparing anything to history. A judge update re-scores your past as well as your present, and a quality "improvement" that coincides with a judge upgrade is not evidence of anything. ## Engineering for reliability [#](#engineering-for-reliability) ### Levers that raise pass^k Narrow the task. Make tools hard to misuse: constrain the signature so invalid combinations are unrepresentable, with enums instead of free text, required fields, and server-side validation that rejects rather than guesses. Make them idempotent, so a retried posting cannot pay the same invoice twice. Constrain the action space to what the task needs. Put deterministic scaffolding around the non-deterministic step, so ordering, validation and formatting are code rather than prompt. Gate retries on a state check rather than retrying blind. Define explicit fallbacks. Put human checkpoints at the irreversible steps, not uniformly across the workflow. Most of these narrow what the agent is allowed to do. Check whether the task still requires non-determinism before deciding to keep it as an agent. ### More agents is not more reliability Multi-agent architectures are often proposed as a reliability improvement, and the evidence is not encouraging. [Cemri et al.](https://arxiv.org/abs/2503.13657) (NeurIPS 2025) report that performance gains from multi-agent systems on popular benchmarks are often minimal, and build a taxonomy of fourteen failure modes from more than 1,600 annotated traces across seven frameworks, in three categories: specification and system design, inter-agent misalignment, and task verification. They conclude that the failures are not merely a matter of the underlying model. Our practical reading, which is an inference rather than a finding of the study: each additional agent adds coordination surface, and coordination is where many of the failures were found. Add agents to decompose a genuinely separable problem, not to improve reliability. ### Regression gates and release discipline Run evaluations in CI against the frozen set. Use canary and shadow runs on live traffic before a full rollout. Agree rollback triggers before launch, with named thresholds, rather than negotiating them during an incident. And plan for the dependency you do not control: when the provider updates the model, your system changes without a deploy. The frozen set and the pinned judge version exist so that you can tell what moved. ## Turning evaluation into evidence [#](#turning-evaluation-into-evidence) ### The evidence file Evaluation output is only useful later if someone can reconstruct it. Keep, as one referenceable artefact: * the evaluation set version and where its cases came from * run logs with trace IDs * the judge model and version, if one was used * the human-review sample and its agreement statistics * the incident-to-case trail * who signed off, and against which target The test: if you cannot reconstruct why you believed the agent was ready to ship, you do not have evidence. You have a memory. That holds in any jurisdiction, and is worth doing whether or not anyone asks. ### What the EU AI Act requires as of October 2026 For EU-facing systems the timeline changed in 2026 and a great deal of published guidance has not caught up. [Regulation (EU) 2026/1744](https://eur-lex.europa.eu/eli/reg/2026/1744/oj/eng), the Digital Omnibus on AI, is dated 8 July 2026 and entered into force on 27 July 2026. Reading its effect on the AI Act timeline, the [Cloud Security Alliance's analysis](https://labs.cloudsecurityalliance.org/research/csa-research-note-eu-ai-act-high-risk-deadline-omnibus-20260/) sets out the changes: high-risk obligations for standalone Annex III systems move from 2 August 2026 to 2 December 2027, and for AI embedded in products already covered by EU product-safety law under Annex I to 2 August 2028, while Article 50 transparency duties (apart from a short grace period, to 2 December 2026, for marking content from generative systems already on the market), the general-purpose AI provider obligations and the Article 5 prohibitions were not deferred. If your compliance calendar still says 2 August 2026 for high-risk obligations, it is wrong. The deferral bought calendar, not scope: the evidence an assessment will ask for, should your system turn out to be in scope, is the same evidence, and it is produced by the programme described above. ## Where to start [#](#where-to-start) Three stages, defined by what you have at the end of each rather than by how long they take. The three run in order, but they overlap: if your agent is already live, some of Stage 2 has to happen before Stage 1 can finish. **Stage 1: Decide what "good" means.** Inventory the tasks and split them by verifiability. Get the incumbent baseline from whoever owns the process today, measured on the same tasks. If an agent is already in production, you need a second baseline, its current performance, and that one is harder, because nobody instrumented for it. Work through the four value-and-cost questions with someone who carries the consequences of a failure, not only with the engineering team. You are done when you have a target number you would defend to a CFO, with the baselines beside it and the detection-cost question answered. **Stage 2: Build the instrument.** Pull the first cases from production traces rather than writing them. Instrument per step before you start scoring, because retrofitting traces onto an existing harness costs more than building them in. Measure your noise floor before you measure anything else. You are done when the set is drawn from real traffic and sized against that floor, traced end to end, running in CI against a frozen version, with a judge validated only where Layer 0 requires one. **Stage 3: Close the loop.** Route every production incident into the set as a case. On the review cadence, check three things: whether the case mix still matches traffic, whether its scores still move with production outcomes, and whether anything in the evidence file has gone stale. You are done when that review has happened twice without anyone chasing it. These map onto the three phases of the [TriStorm approach](https://vstorm.co/tristorm/) (Strategize, Build, Transform), but the sequence stands on its own. ## Frequently asked questions [#](#frequently-asked-questions) ### What is the difference between pass@k and pass^k? pass@k counts a task as solved if at least one of k attempts succeeds, and pass^k only if all k succeed. pass@k measures capability and is the optimistic number common on coding leaderboards. pass^k measures reliability and is the number that predicts production behaviour. The [τ-bench paper](https://arxiv.org/abs/2406.12045) introduced it for that reason. ### How many test cases do I need to evaluate an AI agent? There is no universal number. Run the same evaluation twice against an unchanged agent: the spread between those runs is your noise floor, and your set needs to be large enough that the smallest improvement you care about sits outside it. ### Can an LLM reliably evaluate another AI agent? For tasks with no checkable end state, a model judge is often the only scalable option, but it has to be validated against human labels using chance-corrected agreement before its scores mean anything, and re-validated whenever the judge version changes. Where an end-state check is available, use that instead. ### Does the EU AI Act still apply to AI agents in 2026? Yes. Regulation (EU) 2026/1744 deferred high-risk obligations for Annex III systems to 2 December 2027 and for Annex I systems to 2 August 2028, but Article 50 transparency duties (apart from a short grace period, to 2 December 2026, for marking content from generative systems already on the market), general-purpose AI obligations and the Article 5 prohibitions were not deferred and remain in force. ## Conclusion [#](#conclusion) Until a team runs each case more than once and sets the result against its own baseline, it does not know which of the two numbers describes its agent. If you want a second pair of eyes on an agent evaluation programme, or on whether a workflow should be an agent at all, [book a conversation](https://vstorm.co/schedule-a-meeting/). ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![An engineering voice at PRINTING United Expo in Las Vegas](/app/uploads/2026/10/cover-an-engineering-voice-at-printing-united-expo.jpg) ### An engineering voice at PRINTING United Expo in Las Vegas Read article ](/agentic-ai/an-engineering-voice-at-printing-united-expo/)[ ![What agentic AI is still taken for: three lessons from Stockholm](/app/uploads/2026/10/cover-agentic-ai-misconceptions-three-lessons-from-stockholm-v3.png) ### What agentic AI is still taken for: three lessons from Stockholm Read article ](/agentic-ai/agentic-ai-misconceptions-three-lessons-from-stockholm/)[ ![Our findings in agentic automation from the Nordics Summit](/app/uploads/2026/10/cover-agentic-automation-in-finance-lessons-from-the-nordics-summit-v3.png) ### Our findings in agentic automation from the Nordics Summit Read article ](/agentic-ai/agentic-automation-in-finance-lessons-from-the-nordics-summit/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Prompt injection is not the vulnerability. Your agent’s tools are. URL: https://vstorm.co/agentic-ai/ai-agent-security-prompt-injection Prompt injection will get through. AI agent security depends on what the agent can do when it does: break the lethal trifecta and enforce limits in code. [Home](/)/[Blog](/ai-blog-news/)/[Engineering](/ai-blog-news/#engineering)/Prompt injection is not the vulnerability. Your agent’s tools are. [Engineering](/ai-blog-news/#engineering) # Prompt injection is not the vulnerability. Your agent’s tools are. Prompt injection will get through. AI agent security depends on what the agent can do when it does: break the lethal trifecta and enforce limits in code. ![Wojciech Achtelik](/app/uploads/2025/09/1741016754159-Photoroom.webp) [Wojciech Achtelik](/author/wojciech-achtelik/) PhD(c), AI Tech Lead September 25, 2026 6 min read ![Prompt injection is not the vulnerability. Your agent’s tools are.](/app/uploads/2026/10/cover-ai-agent-security-prompt-injection-v3.png) On this page0% 1. [The problem](#the-problem) 2. [An agent worth attacking](#an-agent-worth-attacking) 3. [Assume it already happened](#assume-it-already-happened) 4. [Constraints, and what they cost](#constraints-and-what-they-cost) 5. [The same agent, rebuilt](#the-same-agent-rebuilt) 6. [The boring but effective reality](#the-boring-but-effective-reality) 7. [Sources](#sources) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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The better question is: **what can the model actually do when an injection inevitably succeeds?** ## The problem [#](#the-problem) LLMs follow instructions. This is what makes them so useful: we feed them instructions in human language and they do our bidding. The problem is that they do not just follow _our_ instructions. They will happily follow any instructions that make it to the model. “Summarize this document” invites arbitrary text into the model’s context, and the model has no reliable way to distinguish between your trusted instructions and untrusted data. Everything eventually gets glued together into a single sequence of tokens. Because of this, trying to detect and filter malicious prompts is a losing game. The defender has to be right every time, but the attacker only needs to get lucky once. A 99%-effective classifier might give you useful telemetry, but it is useless as a security boundary if a single bypassed string gives an attacker access to your database. We have seen this exact failure pattern ship in [Slack AI](https://promptarmor.substack.com/p/slack-ai-data-exfiltration-from-private) and the [GitHub MCP server](https://invariantlabs.ai/blog/mcp-github-vulnerability). Invariant Labs’ write-up of the GitHub vulnerability gets to the core of the issue: it is “not a flaw in the GitHub MCP server code itself, but rather a fundamental architectural issue that must be addressed at the agent system level.” ## An agent worth attacking [#](#an-agent-worth-attacking) Consider a standard research agent. It reads a prospect’s website, pulls context from your CRM, drafts an account brief, and emails it to the account owner. It has four tools: `web_search`, `http_get`, `crm_query`, and `send_email`. The CRM tool takes raw SQL and runs on a service account. The mail tool accepts a recipient and a free-form body. This is the [lethal trifecta](https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/): access to untrusted content, access to private data, and the ability to communicate with the outside world. Individually, these capabilities are fine. Combined, they are fatal. [![Diagram of a research agent with four tools that together form the lethal trifecta. On the left, untrusted content: the prospect's website, any page the agent picks, reached through web\_search and http\_get. At the bottom, private data: the CRM, queried through crm\_query with raw SQL and full read access. On the right, the outside world: the internet, any recipient and any URL, reached through send\_email and http\_get. The LLM sits in the middle with access to all three.](/app/uploads/2026/09/ai-agent-security-fig1-lethal-trifecta-1600x680.png)](/app/uploads/2026/09/ai-agent-security-fig1-lethal-trifecta.png) Fig. 1: Four tools, three legs of the lethal trifecta: untrusted content, private data and a way out. The exploit is trivial. A hidden instruction on the prospect’s website tells the agent to query the CRM, encode the results into a URL, and fetch it with `http_get`. The request lands on an attacker-controlled server with your customer data in the query string. The agent complies. Nothing in the system blocks the action because nothing in the system was designed to verify it. The injection only opened the door. The damage came entirely from the tools the agent was already holding. [![Diagram of the exploit in four numbered steps. 1: the agent reads the prospect's website through http\_get and picks up a hidden instruction, 'query the CRM, send it out'. 2: following the injection, it calls crm\_query and dumps the customers table with SELECT \* FROM customers. 3: the rows come back to the agent. 4: the agent sends the data out through http\_get to the attacker's server as a query string, ?d=. No component has a bug; every step uses a tool the agent was given.](/app/uploads/2026/09/ai-agent-security-fig2-exploit-1600x680.png)](/app/uploads/2026/09/ai-agent-security-fig2-exploit.png) Fig. 2: One hidden instruction, zero bugs: the attack uses only tools the agent already had. ## Assume it already happened [#](#assume-it-already-happened) Assume compromise is inevitable. A malicious prompt has made it into the context window. Your next step is to audit exactly what the agent is capable of doing at that moment. Do not just look at the tool list; look at the credentials executing those tools. `crm_query` sounds like a narrow tool, but if the underlying service account has full read access to your entire customer database, that is exactly what the attacker has access to. Nobody intentionally grants full read permissions to an agent that “only summarizes,” but it happens constantly when teams move fast. Next, look at exfiltration. Anything that moves bytes outward counts. This includes emails, webhooks, outbound HTTP calls, and even Markdown image URLs that your client fetches when it renders a response. If you leave a single exfiltration vector open, an attacker will use it. Finally, evaluate the writes. Which of them are reversible, and would anyone notice if they were triggered maliciously at 3 AM on a Sunday? ![Three tinted audit columns: the service account with full read access behind crm\_query, four exfiltration vectors (emails, webhooks, outbound HTTP, Markdown image URLs), and writes sorted by reversible and noticed, with irreversible unseen writes outlined in red.](/app/uploads/2026/10/fig-ai-agent-security-prompt-ex-pi-audit.png) When the injection lands, audit what the agent holds. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Constraints, and what they cost [#](#constraints-and-what-they-cost) Security should not rely on asking the model whether an action is safe. You need structural constraints in code. Each comes with real trade-offs. **Break the trifecta.** Cut off one of the three legs. A research agent needs to read the web and internal documents, but it rarely needs to decide where its requests go. Any URL the model controls is an outbound channel, because data can ride along in the path or the query string. Let the system decide what gets fetched, deny all other egress, and entire classes of exfiltration attacks disappear. The cost is capability: the agent legitimately cannot do certain things anymore. **Make the tool signature the policy.** Never ship `run_sql()`. Ship `get_orders_for_customer(customer_id)`. Hardcoded parameters are a boundary a model cannot talk its way around. The trade-off is tool bloat. You have to write more tools, and your engineering team becomes the bottleneck for every new feature. **Separate proposing from executing.** Route the model to `draft_email()`, not `send_email()`. But be careful here: human approval is often just security theater. If a human reviews 60 proposals a day, they become a rubber stamp within a week. Approvals only work if they are rare and the UI clearly shows what the agent is trying to do and why. **Authorize deterministically.** The model proposes; a deterministic system decides. If the agent wants to update a customer record, a non-LLM layer has to verify whether that specific agent, acting for that specific user, actually has permission. Karol Piekarski describes that layer, the agentic AI control plane, in [AI agents can act: can you still control them?](https://vstorm.co/agentic-ai/agentic-ai-control-plane/) ![Four constraint cards, each showing what it replaces (struck through), what to ship instead (for example get\_orders\_for\_customer(customer\_id) instead of run\_sql()) and what it costs; the fourth cost is left blank.](/app/uploads/2026/10/fig-ai-agent-security-prompt-ex-pi-costs.png) Structural constraints, and what they cost. ## The same agent, rebuilt [#](#the-same-agent-rebuilt) Every one of these fixes is standard software architecture. `crm_query` becomes a strict API endpoint for specific fields instead of a raw SQL pipe. The prospect’s website still gets read, but the model no longer chooses what to fetch: the URL comes from the CRM record, and the system retrieves the page before the model ever runs. `send_email` and `http_get` are deleted entirely. The sandbox denies outbound network traffic by default, meaning a compromised model trying to ping an external server is blocked at the network level. Now run the exact same attack on this rebuilt architecture. The model can read the brief fields for its assigned account, but it cannot dump the CRM. It cannot send an email or make an HTTP request because those tools and network paths do not exist. It can write hostile text into a draft, but drafts are rendered and sent as plain text, so nothing in them can trigger a request when someone opens them. What is left is text, which is exactly why a human reviews the draft before it goes anywhere. The attacker cannot steal the customer database. The model may still follow the injection; the data simply has no path out of the system. That is the difference between a mitigation and a boundary. A mitigation asks the model to behave. A boundary does not care whether it does. [![Diagram of the same research agent, rebuilt. The prospect's website is still untrusted content, but the system fetches it before the model runs. crm\_query now returns brief fields for one account only. send\_email and http\_get are removed and outbound network traffic is denied, so the agent has no path to the internet. Its only output is draft\_email, a plain-text draft that a human reviews first.](/app/uploads/2026/09/ai-agent-security-fig3-rebuilt-1600x680.png)](/app/uploads/2026/09/ai-agent-security-fig3-rebuilt.png) Fig. 3: Break the trifecta: remove the way out, and a successful injection has nowhere to send the data. ## The boring but effective reality [#](#the-boring-but-effective-reality) Least privilege, capability security, sandboxing, and scoped credentials are among the strongest ideas in security, and they are decades old. They were developed for exactly this scenario: managing a component you cannot fully trust inside a system that has to keep working anyway. ![A dashed mitigation card that asks the model to behave set against a solid teal boundary card that does not care, each followed by a short flow: one ends in an actual incident, the other in a rejected draft.](/app/uploads/2026/10/fig-ai-agent-security-prompt-ex-pi-boundary.png) Build boundaries the model cannot argue with. It is tempting to treat LLM agents as a wholly new category that requires novel defenses, such as [guardrails](https://vstorm.co/glossary/guardrails/) that use models to monitor models. But that just makes your security recursive and fragile. The older answer is boring, but it works: enforce your boundaries around capabilities and data flows in code, completely outside the model’s reach. The model will eventually be tricked. Your architecture is what determines whether that failure results in a rejected draft or an actual incident. ## Sources [#](#sources) Incidents and concepts cited in the text * PromptArmor, [Slack AI data exfiltration from private channels via indirect prompt injection](https://promptarmor.substack.com/p/slack-ai-data-exfiltration-from-private) (the Slack AI case), August 20, 2024 * Invariant Labs, [GitHub MCP Exploited: Accessing private repositories via MCP](https://invariantlabs.ai/blog/mcp-github-vulnerability) (the GitHub MCP case and the architectural-issue quote), May 26, 2025 * Simon Willison, [The lethal trifecta for AI agents: private data, untrusted content, and external communication](https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/) (the three capabilities), June 16, 2025 ![Wojciech Achtelik](/app/uploads/2025/09/1741016754159-Photoroom.webp) [Wojciech Achtelik](/author/wojciech-achtelik/) PhD(c), AI Tech Lead [Author profile](/author/wojciech-achtelik/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Agentic AI doctor notes generators: ambient scribes beyond transcription](/app/uploads/2026/10/cover-agentic-ai-doctor-notes-generators-v3.png) ### Agentic AI doctor notes generators: ambient scribes beyond transcription Read article ](/agentic-ai/agentic-ai-doctor-notes-generators/)[ ![Agentic tokenization: how AI agents will pay](/app/uploads/2026/10/cover-agentic-tokenization-how-ai-agents-will-pay-v3.png) ### Agentic tokenization: how AI agents will pay Read article ](/agentic-ai/agentic-tokenization-how-ai-agents-will-pay/)[ ![Single agent vs multi-agent systems: when one agent is not enough](/app/uploads/2026/10/cover-single-agent-vs-multi-agent-systems-v3.png) ### Single agent vs multi-agent systems: when one agent is not enough Read article ](/agentic-ai/single-agent-vs-multi-agent-systems/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AI agents for patient scheduling: what works, what does not, and what to avoid URL: https://vstorm.co/agentic-ai/ai-agents-for-patient-scheduling-what-works-what-does-not-and-what-to-avoid Which parts of patient scheduling AI agents handle reliably, which ones they do not, and the failure modes worth designing out before launch. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/AI agents for patient scheduling: what works, what does not, and what to avoid [Agentic AI](/ai-blog-news/#agentic-ai) # AI agents for patient scheduling: what works, what does not, and what to avoid Which parts of patient scheduling AI agents handle reliably, which ones they do not, and the failure modes worth designing out before launch. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst May 5, 2026 Updated June 17, 2026 1 min read ![AI agents for patient scheduling: what works, what does not, and what to avoid](/app/uploads/2026/05/Group-1777-7.png) On this page0% 1. [How patient scheduling works today and why it breaks](#how-patient-scheduling-works-today-and-why-it-breaks) 2. [What AI agents for patient scheduling can actually do](#what-ai-agents-for-patient-scheduling-can-actually-do) 3. [What does not work](#what-does-not-work) 4. [What to avoid](#what-to-avoid) 5. [What a production-grade implementation involves](#what-a-production-grade-implementation-involves) 6. [Closing observations](#closing-observations) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## What does not work [#](#what-does-not-work) ### Chatbot versus production-grade AI agent **Dimension** **Generic scheduling chatbot** **Production-grade AI scheduling agent** **Core pattern** Fixed decision tree: scripted prompts and expected responses Iterative: plan, retrieve patient context, reason, act, adapt **Handling exceptions** Fails or escalates to staff when the patient response deviates from script Interprets intent, reformulates, and resolves rescheduling requests, insurance edge cases, and referral queries autonomously **EHR integration** Typically read-only or no direct EHR connection; confirmed bookings require manual entry Bidirectional: reads availability and writes confirmed appointments back to the EHR via HL7 FHIR **Patient context awareness** None: same questions asked of every patient regardless of history Draws on full patient record to ask history-relevant questions and surface relevant alerts for the clinical team **Memory across sessions** Stateless: no context carried between interactions Maintains and updates patient history after each interaction, building a richer data foundation over time **Compliance architecture** Variable: HIPAA compliance depends on vendor; often retrofitted or left to the operator BAA in place before deployment; AES-256 encryption, audit logging, and role-based access controls designed in from the outset **When it fails** Staff are pulled in to resolve failures, often producing more manual work than the original process Observable failure points with audit trail; exceptions flagged for human review with full context attached ### One-way patient scheduling EHR integration ### Automating a process that has not been mapped ### Treating staff adoption as an afterthought ## What to avoid [#](#what-to-avoid) ### Choosing a platform before mapping the workflow ### Skipping HIPAA compliance architecture ### Ignoring patient demographic when designing the channel ### Treating scheduling as an isolated workflow ## What a production-grade implementation involves [#](#what-a-production-grade-implementation-involves) ## Closing observations [#](#closing-observations) ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![What is HIPAA-compliant AI?](/app/uploads/2026/05/Group-1777-9.png) ### What is HIPAA-compliant AI? Read article ](/agentic-ai/what-is-hipaa-compliant-ai/)[ ![AI process automation in print on demand: five use cases that deliver measurable results for SMBs](/app/uploads/2026/04/Group-1777-6-1.png) ### AI process automation in print on demand: five use cases that deliver measurable results for SMBs Read article ](/agentic-ai/ai-process-automation-in-print-on-demand-five-use-cases-that-deliver-measurable-results-for-smbs/)[ ![What is patient intake automation?](/app/uploads/2026/04/Group-1777-7.png) ### What is patient intake automation? Read article ](/agentic-ai/what-is-patient-intake-automation/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AI automation agency URL: https://vstorm.co/agentic-ai/ai-automation-agency Vstorm is an agentic AI automation agency — production-grade agents delivered by engineers, with full code ownership and no vendor lock-in. [Home](/)/[Industries](/industries/)/Ai Automation Agency # AI automation agency An agency staffed by engineers, not project managers. Agents that take the manual work out of one workflow, tested on your data before the full build, with the code and the decision logic handed to your team. [Book a free consultation](/schedule-a-meeting/) [See client results](#proof) requirement Model model A model B your agent Pydantic AI caught Eval suite your repo no lock-in 01 Evaluated 02 Model swap 03 Yours Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Most automation agencies sell a demo. Few can explain the guardrails. A convincing demo tells you nothing about whether an agency can get a system through evaluation, integration, and a compliance review — or whether you'll own the result when the engagement ends. Those are engineering questions, and most agencies answer them with a sales deck instead of a technical one. We answer the engineering questions first: what does the eval suite check, who owns the code, what happens when the model changes. Sources Agent Outcomes Engineeringevaluation Businessrequirement Existing systems Risk tolerance Production agent Your team, trained Full codeownership every claim backed by a shippable system What sets us apart ## Why companies choose Vstorm as their automation partner Engineering depth, not a reseller layer over generic tooling. 01 ### First agentic AI engineering company in the Agentic AI Foundation A member of the foundation that works on standards for production agentic AI. 02 ### Embedded, not outsourced Our engineers work inside your existing team, transferring capability instead of creating a permanent dependency. 03 ### Full code ownership, always No proprietary runtime, no recurring platform fees — you control what we build, from day one of the handoff. 04 ### Official Pydantic implementation partner We work on the frameworks our agents run on, so breaking changes and new patterns reach us before they reach your codebase. Delivery path ## How an engagement actually runs The same three steps in every engagement, whatever the industry. 01 ### Map the workflow We map how the workflow runs today with your experts and agree the success threshold before anything is built. Workflow & data mapSuccess thresholdBuild plan 02 ### Proof of Value A working agent on your data and edge cases, with an evaluation suite. It ends in a go or no-go decision backed by measured accuracy and running costs. Working agentEvaluation suiteGo / no-go decision 03 ### Build and hand over Production rollout with monitoring and guardrails, ending with the code, the prompts and the runbooks in your team's hands. Production deploymentMonitoring & alertingOwnership transfer Client results ## Proof across industries [View all case studies](/case-studies/) [![ARIJ Network](/logos/clients/arij.png) Media & Journalism, Multilingual RAG **EN + AR**one agent answering in English and Arabic from ARIJ's own materials A bilingual English/Arabic autonomous agent embedded in ARIJ's Moodle environment, answering only from ARIJ's own knowledge base. Read case study](/case-study/multilingual-ai-agent-powered-chatbot-supporting-journalist-training/) [![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) Engineering software **2 hrs → 3 min**to generate a validated workflow Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) FAQ ## Choosing an AI automation agency, answered All FitUse casesOperations What should we actually look for when evaluating an AI automation agency? + Real production deployments you can verify, an engineering team that can explain the evaluation and guardrail approach (not just the demo), and a clear answer to who owns the code when the engagement ends. How is Vstorm different from a generic automation agency? + We are engineers first: the people who scope the workflow are the people who build and evaluate the agents, not a project-management layer over outsourced development. We work on the frameworks our agents run on, as an official Pydantic implementation partner and a member of the Agentic AI Foundation. Do you work across industries, or specialize in one? + Across industries — the underlying engineering (orchestration, evaluation, guardrails) is consistent even as the workflow specifics change. We've shipped in healthcare, fintech, manufacturing, media, and more. Can you take over automation work another agency started? + Yes. We audit existing integration gaps, reasoning failures, and observability blind spots, and resolve them with your target infrastructure in mind — no need to start over. Do we own the code and systems you build? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in, and no recurring platform fees. What is the typical timeline to a working system? + The first milestone is a Proof of Value: one workflow, your real data and a working agent, measured against a success threshold agreed before the build. How long it takes depends on the workflow and the systems it touches, and we estimate it once the workflow is mapped. Start with a conversation ## Talk to the engineers, not an account manager. Book a free consultation. We will map one real workflow worth automating — no pitch, no deck. [Book a free consultation](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AI business automation URL: https://vstorm.co/agentic-ai/ai-business-automation AI business automation — agents scoped to one real business process at a time, ranked by data readiness and impact. [Home](/)/[Industries](/industries/)/Ai Business Automation # AI business automation Start with one process that actually costs you. We rank automation candidates by data readiness and business impact, then build one working agent before talking about the rest of the roadmap. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) trigger rule action Form Email Schedule new lead invoice in weekly report proven Update CRM Team review Audit log Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## A company-wide automation roadmap is a strategy document, not a result. Ambitious, company-wide AI initiatives tend to stall because they try to automate everything at once instead of proving value on one process. The companies that actually ship start narrow: one workflow, real data, a working agent — then expand from evidence, not from a slide. We rank candidates by data readiness, integration complexity, and business impact before recommending where to start. Sources Agent Outcomes Businessprocess agent Operations data Finance workflows Customer requests Completed task Team review Audit log one process proven before the next is scoped Where to start ## Business functions with the clearest automation ROI Document-heavy, multi-system work with a real decision inside it. 01 ### Operations Workflow generation, exception handling, and cross-system reconciliation — the highest-volume automation candidates in most companies. 02 ### Finance Claims and invoice verification, multi-step approvals, and reconciliation across systems that don't share data natively. 03 ### Customer-facing teams Support and onboarding grounded in real account data, handling volume that a fixed script cannot serve accurately. 04 ### Compliance & documentation Document review and record-keeping that needs a sourced, auditable trail — not a black-box automation. Delivery path ## From one process to a production agent TriStorm scopes the first win before scaling the roadmap. 01 ### Strategize We rank automation candidates across your business by data readiness, integration complexity, and impact — then pick one to prove first. Cross-functional workflow auditImpact rankingPrioritised use case 02 ### Build A working agent on your real data, with an evaluation suite — evidence for a build decision, not a slide deck. Working prototypeEvaluation suiteProduction path 03 ### Transform Production rollout with monitoring and governance, ending in ownership transfer — then the next process is scoped from evidence. Production deploymentMonitoring & alertingOwnership transfer Client results ## Proof across business functions [View all case studies](/case-studies/) [![Mixam](/_astro/mixam-logo.B59_GcxD.png) Operations, Print on demand **95.4%**Success rate in workflow results A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) [![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) Operations, Engineering software **2 hrs → 3 min**to generate a validated workflow Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) FAQ ## AI business automation, answered All FitUse casesDeliveryOperations Where should a company start with AI business automation? + With one process that already costs real time or money — not a company-wide rollout. We rank candidates by data readiness and business impact during the Strategize phase, before any build starts. Which business functions see the most from agentic automation? + Operations, finance, and customer-facing teams typically have the highest volume of document-heavy, multi-system, judgment-requiring work — the profile agentic automation handles best. Can automation span more than one department, or does it need to stay siloed? + It can span departments when the workflow itself does — a finance approval that touches operations data, for instance. We map the actual workflow, not the org chart. What has this delivered for companies like ours? + A three-agent product advisor that lifted workflow success to 95.4% for a print-on-demand platform, and a workflow-generation agent that cut engineers' setup time from 3 hours to 3 minutes for an engineering-software platform — each scoped to one real business process. How do you decide what to automate first? + We rank candidates by data readiness, integration complexity, and business impact during the Strategize phase — not by what sounds impressive, by what will actually ship. Do we own the system after it's built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in. Start with one process ## Find the one process worth automating first. A 30-minute call ranks your automation candidates by data readiness and impact — no company-wide commitment required. [Book a discovery call](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AI compliance in manufacturing: what mid-market operators need to know about ISO 42001 and the EU AI Act URL: https://vstorm.co/agentic-ai/ai-compliance-in-manufacturing-what-mid-market-operators-need-to-know-about-iso-42001-and-the-eu-ai-act The EU AI Act carries €35M penalties; ISO 42001 is voluntary until procurement asks. What mid-market manufacturers need in place, and by when. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/AI compliance in manufacturing: what mid-market operators need to know about ISO 42001 and the EU AI Act [Agentic AI](/ai-blog-news/#agentic-ai) # AI compliance in manufacturing: what mid-market operators need to know about ISO 42001 and the EU AI Act The EU AI Act carries €35M penalties; ISO 42001 is voluntary until procurement asks. What mid-market manufacturers need in place, and by when. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst June 2, 2026 Updated June 27, 2026 9 min read ![AI compliance in manufacturing: what mid-market operators need to know about ISO 42001 and the EU AI Act](/app/uploads/2026/06/Group-1777-3.png) On this page0% 1. [What mid-market manufacturers need to know: a direct answer](#what-mid-market-manufacturers-need-to-know-a-direct-answer) 2. [Most mid-market manufacturers are already running regulated AI](#most-mid-market-manufacturers-are-already-running-regulated-) 3. [The EU AI Act timeline and what the May 2026 Omnibus changes](#the-eu-ai-act-timeline-and-what-the-may-2026-omnibus-changes) 4. [What ISO 42001 requires and why it matters](#what-iso-42001-requires-and-why-it-matters) 5. [How ISO 42001 and the EU AI Act work together and where they diverge](#how-iso-42001-and-the-eu-ai-act-work-together-and-where-they) 6. [A practical compliance pathway for mid-market manufacturing operators](#a-practical-compliance-pathway-for-mid-market-manufacturing-) 7. [The added compliance surface of agentic AI in manufacturing](#the-added-compliance-surface-of-agentic-ai-in-manufacturing) 8. [Frequently asked questions](#frequently-asked-questions) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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The EU AI Act carries legal force with penalties reaching €35M. ISO 42001 is a voluntary governance standard, but enterprise procurement is making it a practical requirement. A provisional political agreement in May 2026 defers the main high-risk compliance deadlines, though formal adoption is still pending. For mid-market manufacturers, the question is not which framework to follow, but how to treat them as a single, coordinated programme, and to start that work now rather than in 2027. ## What mid-market manufacturers need to know: a direct answer [#](#what-mid-market-manufacturers-need-to-know-a-direct-answer) **AI compliance in manufacturing** now rests on two instruments: the EU AI Act, which is binding law, and ISO 42001, which is voluntary but increasingly required by enterprise buyers. Mid-market manufacturers are primarily deployers of AI and face substantial obligations under the Act wherever their AI systems monitor workers or affect employment decisions. A provisional political agreement in May 2026 defers standalone high-risk AI obligations to December 2, 2027, but formal adoption in the Official Journal is still pending. The most effective first step is a complete AI inventory: every system in use, classified by risk tier. ## Most mid-market manufacturers are already running regulated AI [#](#most-mid-market-manufacturers-are-already-running-regulated-) Today, most manufacturers manage AI deployments without a central inventory. Visual quality inspection tools, workforce scheduling platforms, predictive maintenance software, and production optimisation systems are typically procured by individual operational teams, with no formal risk classification or governance structure. This is the starting condition for the majority of mid-market operators approaching AI compliance in manufacturing for the first time. The EU AI Act distinguishes between providers (organisations that build and place AI systems on the market) and deployers (organisations that put AI into operational use). Most manufacturers are deployers. Deployer obligations are still substantial: human oversight requirements, logging, incident reporting, and instructions for use all apply to high-risk systems. Manufacturers who sell equipment with embedded AI to EU buyers are also providers. Both roles can apply to the same organisation, and they carry different compliance tracks. Two manufacturing AI use cases fall squarely within the Act's Annex III high-risk category: worker monitoring systems, and AI used for task allocation, performance evaluation, or any decision affecting employment status. The Act also expressly prohibits AI designed to infer the emotional state of workers in the workplace, with no exceptions. By contrast, equipment-focused predictive maintenance and product quality inspection, where no personal data is processed and no individual rights are affected, typically fall outside high-risk classification. ( [Source: EU AI Act Annex III](https://artificialintelligenceact.eu/annex/3/)) Classification is use-case-specific, not technology-specific. The same underlying model can be high-risk in one deployment and minimal-risk in another, depending on what data it processes and whose decisions it influences. ## The EU AI Act timeline and what the May 2026 Omnibus changes [#](#the-eu-ai-act-timeline-and-what-the-may-2026-omnibus-changes) The EU AI Act follows a phased structure. Prohibited AI practices and AI literacy obligations entered force in February 2025. General-purpose AI model obligations have applied since August 2025. The main high-risk obligations were originally scheduled for August 2026. On May 7, 2026, the European Parliament and the Council reached a provisional political agreement on the Digital Omnibus on AI. Standalone high-risk Annex III obligations are provisionally deferred to December 2, 2027. For AI embedded in regulated products under Annex I, including machinery covered by existing EU product safety legislation, the provisional deadline is August 2, 2028. Formal adoption and publication in the Official Journal are expected before August 2026; until that publication, the original August 2, 2026 deadline remains binding law. ( [Source: Gibson Dunn, May 2026](https://www.gibsondunn.com/eu-ai-act-omnibus-agreement-postponed-high-risk-deadlines-and-other-key-changes/)) Two things the Omnibus does not change: Article 50 transparency obligations, which require disclosure to users interacting with AI systems, remain on the 2026 schedule; and the prohibition on unacceptable-risk AI practices remains in force. These are live obligations now. For the **EU AI Act mid-market** operator, the deferral should not be read as a signal to pause. Multiple legal sources characterise the May 2026 agreement as a one-time extension, not the first in a series, with the political case for further delay exhausted. The compliance build; AI inventory, risk classification, governance documentation, technical files; typically takes 12 to 18 months for a mid-market organisation to execute properly. Non-compliance fines reach €35M or 7% of global annual turnover for prohibited practices, a higher ceiling than GDPR. ( [Source: Legiscope penalty summary](https://www.legiscope.com/blog/eu-ai-act-timeline-deadlines.html)) ## What ISO 42001 requires and why it matters [#](#what-iso-42001-requires-and-why-it-matters) Published in December 2023, ISO/IEC 42001 is the first certifiable international AI management system standard. Think of it as ISO 27001 applied to AI governance: a repeatable, auditable framework covering the full AI lifecycle from risk identification through to continuous improvement. It is not a legal requirement. Organisations pursue **ISO 42001 implementation** because enterprise procurement increasingly demands it, because it creates the documentation foundation that EU AI Act compliance builds on, and because it provides a structured way to demonstrate responsible AI use to auditors and customers. ISO 42001 operates across 38 controls using a Plan-Do-Check-Act cycle, covering AI risk management, data governance, transparency, ethics, human oversight, and continuous improvement. It is deliberately aligned with ISO 27001 and ISO 9001, so manufacturers holding either certification will find significant structural overlap when beginning ISO 42001 work. The adoption signal is hard to ignore. The Cloud Security Alliance 2025 Compliance Benchmark Report, surveying over 1,000 compliance professionals, found that 76% of organisations planned to pursue AI compliance with a framework like ISO 42001 soon. ( [Source: Cloud Security Alliance, June 2025](https://cloudsecurityalliance.org/blog/2025/06/10/why-early-adoption-of-iso-42001-matters)) Enterprise buyers are already requiring it. Manufacturers that delay ISO 42001 work risk not only regulatory exposure but procurement disqualification in contracts where AI governance certification has become a supplier qualification criterion. ## How ISO 42001 and the EU AI Act work together and where they diverge [#](#how-iso-42001-and-the-eu-ai-act-work-together-and-where-they) The two frameworks serve different purposes and are not interchangeable. The table below maps the six dimensions where they differ most. **Dimension** **ISO 42001** **EU AI Act** Nature Voluntary international standard Mandatory EU regulation Enforcement No penalties Up to €35M or 7% of global turnover Scope All AI systems (providers and deployers) Risk-tiered; high-risk systems face full obligations Risk management Required — 38 controls, PDCA cycle Required under Articles 9–15 for high-risk systems Conformity assessment Not required Required for high-risk systems EU database registration Not required Required for Annex III high-risk systems ISO 42001 covers approximately 70–80% of the EU AI Act's high-risk system requirements, making it an effective foundation rather than a complete substitute. ( [Source: GLACIS crosswalk guide](https://www.glacis.io/guide-iso-42001-vs-eu-ai-act)) Both frameworks require risk assessment, data governance, human oversight, and documentation of AI system properties. Organisations with ISO 42001 certification in place can compress EU AI Act compliance work by an estimated 30–40%. The gaps that ISO 42001 does not close are specific: EU conformity assessment procedures, CE marking for high-risk hardware, Annex IV technical documentation files, EU AI database registration, and post-market surveillance reporting. These require legal and technical input that sits outside a management system programme. The practical approach is sequential: build the governance foundation through ISO 42001 first, then layer the Act's technical requirements on top. Treating them as two parallel, unconnected projects duplicates effort on documentation and misses the overlap that makes both achievable within a realistic budget. ( [Source: ISACA, December 2025](https://www.isaca.org/resources/news-and-trends/industry-news/2025/isoiec-42001-and-eu-ai-act-a-practical-pairing-for-ai-governance)) [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## A practical compliance pathway for mid-market manufacturing operators [#](#a-practical-compliance-pathway-for-mid-market-manufacturing-) Five steps, in order of dependency: 1. **Build a complete AI inventory.** Every AI system across production, quality, logistics, HR, and procurement, including SaaS tools with embedded AI features and AI-enabled machinery procured from third parties. This step is a prerequisite for every other action and is required by both ISO 42001 and the EU AI Act. 2. **Classify each system by risk tier.** Map against Annex III. Worker monitoring AI is high-risk by definition. Equipment-focused predictive maintenance typically is not. Classification must be documented and defensible, not assumed. The EU AI Office publishes ongoing guidance on classification methodology at [ai-act-service-desk.ec.europa.eu](https://ai-act-service-desk.ec.europa.eu/en/faq). 3. **Identify your role as provider, deployer, or both.** Manufacturers who sell AI-embedded equipment to EU buyers carry provider obligations; conformity assessment, technical documentation, CE marking; in addition to deployer obligations. Determine which tracks apply before allocating compliance resource. 4. **Pursue ISO 42001 as the governance foundation.** It addresses the majority of documentation, risk management, and governance work required by the EU AI Act. It also creates an audit-ready programme that enterprise procurement processes increasingly require. Start here, not with the Act's technical requirements. 5. **Close the gaps ISO 42001 does not cover.** Conformity assessments, Annex IV technical documentation, EU AI database registration for high-risk systems, and post-market monitoring plans all require dedicated legal and technical input beyond management system work. For organisations with existing ISO 27001 certification, the ISO 27001-to-ISO 42001 structural overlap significantly reduces the effort of this final layer. ## The added compliance surface of agentic AI in manufacturing [#](#the-added-compliance-surface-of-agentic-ai-in-manufacturing) Until recently, most manufacturing AI deployments were bounded systems: a quality inspection camera reviewing one production line, a scheduling tool managing one shift. Agentic AI systems change this materially. They plan, retrieve data from multiple sources, and act across enterprise systems; workforce records, quality data, maintenance logs, procurement databases; within a single autonomous workflow. The classification risk is architectural. An agent that accesses worker scheduling data alongside production quality records must be classified by its highest-risk use case. Worker-data access can place an otherwise minimal-risk system into Annex III high-risk territory, even when the stated purpose is production optimisation. The governance challenge is equally specific to agentic systems. Traditional periodic review does not capture what autonomous agents do between audit cycles. An agent can gain new data permissions, initiate tool calls, and execute consequential actions between checks, unless observability, logging, and role-based access controls are built into the architecture from the start. A McKinsey survey published in 2026 identified security, risk management, and governance concerns as among the most frequently cited barriers to scaling AI, including agentic deployments. ( [Source: TechTarget, April 2026](https://www.techtarget.com/searchdatamanagement/feature/How-agentic-AI-governance-tackles-data-security-challenges)) The systems we have built at Vstorm for engineering environments make this concrete. In our work with Synera, an engineering automation platform used by Airbus, BMW, Hyundai, and others, the agentic system combined an LLM, a RAG component, and a validator operating across a CAD and PLM tool ecosystem simultaneously. ( [Read the full case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/)) When an agentic system touches that many data sources and tools at once, compliance posture must be a design input from the outset, not an audit item addressed after deployment. > "At Vstorm, we see compliance as something that has to be built into agentic systems from the start, not added later as a checklist. In production, things like observability and data access limits are what let teams understand what an agent did, why it did it, and whether it stayed within the right boundaries." Wojciech Achtelik, PhD AI Engineer Lead, Vstorm ## Frequently asked questions [#](#frequently-asked-questions) **Is the EU AI Act mandatory for mid-market manufacturers operating in the EU?** Yes. The EU AI Act applies to all organisations that place AI systems on the EU market or use AI systems in EU operations, regardless of company size or where the organisation is headquartered. Mid-market manufacturers deploying AI across production, HR, or logistics within the EU are in scope. **Does ISO 42001 certification satisfy EU AI Act compliance requirements?** No. ISO 42001 covers approximately 70–80% of EU AI Act high-risk system requirements and significantly accelerates compliance work, but it does not satisfy the Act's specific requirements for conformity assessment, CE marking, EU AI database registration, or post-market surveillance. It is a foundation, not a substitute. **Which manufacturing AI use cases are classified as high-risk under the EU AI Act?** AI systems used for worker monitoring, task allocation, performance evaluation, and any decision affecting employment status are high-risk under Annex III. AI systems that infer the emotional state of workers in the workplace are prohibited outright, regardless of purpose or deployment context. **What is the current compliance deadline for high-risk AI in manufacturing?** A provisional political agreement reached May 7, 2026 defers standalone Annex III high-risk obligations to December 2, 2027, and AI embedded in regulated Annex I products (including machinery) to August 2, 2028. Formal adoption is pending Official Journal publication, expected before August 2026. Until publication, August 2, 2026 remains binding law. **What is the difference between a provider and a deployer under the EU AI Act, and why does it matter for manufacturers?** A provider develops and places an AI system on the EU market. A deployer uses an AI system in their operations. Most manufacturers are deployers; those who sell AI-embedded equipment to EU buyers are also providers. Provider obligations, including conformity assessment and Annex IV technical documentation, are more demanding. Both roles can apply to the same organisation, and both must be assessed separately. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Agentic AI in manufacturing: five use cases for SMBs](/app/uploads/2026/06/Group-1777-4.png) ### Agentic AI in manufacturing: five use cases for SMBs Read article ](/agentic-ai/agentic-ai-in-manufacturing-five-use-cases-that-deliver-measurable-results-for-mid-market-operators/)[ ![Why most agentic AI projects fail before a single line of code is written](/app/uploads/2026/03/95012D22-051B-4AA2-B47D-51FC15D1C707_1_105_c-768x512.jpeg) ### Why most agentic AI projects fail before a single line of code is written Read article ](/agentic-ai/why-most-agentic-ai-projects-fail-before-a-single-line-of-code-is-written/)[ ![What is a healthcare AI agent? How it works and where it is being deployed](/app/uploads/2026/05/Group-1777-2-1.png) ### What is a healthcare AI agent? How it works and where it is being deployed Read article ](/agentic-ai/what-is-a-healthcare-ai-agent-how-it-works-and-where-it-is-being-deployed/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AI for business process automation URL: https://vstorm.co/agentic-ai/ai-for-business-process-automation AI for business process automation — agents that handle the judgment-requiring steps inside your existing BPM or workflow platform, without replacing it. [Home](/)/[Industries](/industries/)/Ai For Business Process Automation # AI for business process automation The step your process map routes to a human. We build agents that handle the judgment-requiring steps inside your existing BPM or workflow platform — not a replacement for the orchestration you already have. [Book a discovery call](/schedule-a-meeting/) [See what you get](#proof) Intake Check rules Judgment step Continue _queue_ routes to a human agent rule cited _today_ _cycle time_ 1. 01Today 2. 02Agent step 3. 03Traced Trusted by operators * ![Mixam](/logos/clients/mixam.png)[ Mixam , Print on Demand ~20% → ~40% conversion to a final sale Three-agent product advisor guiding customers through print-order configuration. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) * ![Tetra Pak](/logos/clients/tetra-pak.svg) Tetra Pak , Packaging & Processing Supply chain intelligence agents reducing manual coordination overhead. * ![Intel](/logos/clients/intel.svg) Intel , Semiconductor Tech 37 kernels ported from CUDA to Intel Gaudi Production AI workloads moved to new hardware for on-prem LLM deployment. * ![Mercedes-Benz](/logos/clients/mercedes-benz.svg) Mercedes-Benz , Automotive AI agent implementation for a global automotive enterprise. * ![Synera](/logos/clients/synera.svg)[ Synera , Engineering Automation 2 hrs → 3 min to generate a workflow Text-to-workflow agents building validated node graphs inside the platform. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) * ![STCC](/logos/clients/schmitt-thompson.svg)[ Schmitt-Thompson , Healthcare, Clinical Triage 44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark. Read case study](/case-study/reaching-expert-level-medical-triage-with-guideline-executing-llms/) Why agentic AI ## Every process map has a step labeled 'human review.' That's where an agent fits. BPM platforms execute the branches you designed in advance — but every process map has at least one step where the branch depends on judgment: classify this document, verify this exception, decide this edge case. That step is where an agent adds real capacity, without replacing the process map around it. We map which of those judgment steps an agent can take over directly, and which should keep a person as the decision-maker. Sources Agent Outcomes Decision-step agent Process step input Business rules Historicaldecisions Process continues Human review Audit log every decision traces to the rule or precedent behind it Use cases ## Where an agent slots into an existing process map The judgment steps a fixed branch can't resolve. 01 ### Document classification and extraction Reads an incoming document, classifies it, and extracts structured data for the rest of your process map to consume. 02 ### Exception and edge-case handling Takes over the branch your process map currently routes to a person, resolving documented patterns directly. 03 ### Pre-approval verification Cross-references a request against policy and prior decisions before it reaches a human approver, with reasoning attached. 04 ### Cross-system data reconciliation Resolves data that lives in disconnected systems within your process, instead of a manual lookup step. Delivery path ## From process map to a working agent step TriStorm keeps the existing process intact and adds the agent where it earns its place. 01 ### Map the process and the judgment step We audit the existing process map, identify where a fixed branch fails, and assess the data available to resolve it. Process map auditJudgment-step analysisPrioritised use case 02 ### Build and validate the agent We implement the agent for that specific step, with an evaluation suite scored against historical decisions before production. Working prototypeEvaluation suiteEscalation rules 03 ### Deploy inside the existing process Production rollout as a step inside your current BPM or workflow platform, with monitoring and a structured handoff. Production deploymentAudit trail & monitoringOperator runbook Client results ## Proof from production process automation [View all case studies](/case-studies/) [![Synera](/_astro/Synera-Logo.Dbrwvc22.svg) Engineering software **2 hrs → 3 min**to generate a validated workflow Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass. Read case study](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) [![Mixam](/_astro/mixam-logo.B59_GcxD.png) Print on demand **95.4%**Success rate in workflow results A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. Read case study](/case-study/ai-agent-for-order-recommendation-and-completion/) FAQ ## AI for business process automation, answered All FitUse casesDeliveryOperations How does an AI agent actually differ from a BPM workflow engine? + A BPM engine executes a process map you designed in advance — every branch predefined. An agent reads the situation and decides which branch applies, including ones you didn't explicitly map, escalating when it's genuinely unsure. Do we need to replace our BPM or workflow platform to use agentic AI? + No. Agents typically sit inside your existing BPM platform as a smart step — handling the judgment call a fixed branch can't — rather than replacing the orchestration layer you already have. What business processes benefit most from adding an agent? + Steps in an existing process map that currently route to a human for judgment — document classification, exception handling, cross-referencing data before an approval — not the deterministic steps that already work fine. Can an agent read documents as part of a larger business process? + Yes — this is one of the most common entry points: an agent extracts and classifies information from a document, then hands a structured result to the rest of your process map. What is the typical timeline to a working system? + A scoped Proof of Value — one process step, real data, a working agent — typically lands in 3-6 weeks, following the same TriStorm phases as any Vstorm engagement. Do we own the system after it's built? + Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in. Start with one process step ## Find the judgment step worth automating. A 30-minute call identifies the step in your process map that currently needs a human, and whether an agent can take it over. [Book a discovery call](/schedule-a-meeting/)[Agentic process automation services](/agentic-ai/agentic-process-automation-services/) --- ## Sovereign AI in Europe: 5 EU-hosted platforms (2026) URL: https://vstorm.co/agentic-ai/ai-platforms/sovereign-ai-platforms-europe Bielik, PLLuM, Mistral, Aleph Alpha and Scaleway compared on EU data residency, self-hosting and managed inference. For teams keeping AI inside the EU. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Sovereign AI in Europe: 5 EU-hosted platforms (2026) [Agentic AI](/ai-blog-news/#agentic-ai) # Sovereign AI in Europe: 5 EU-hosted platforms (2026) Bielik, PLLuM, Mistral, Aleph Alpha and Scaleway compared on EU data residency, self-hosting and managed inference. For teams keeping AI inside the EU. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst April 28, 2026 Updated April 29, 2026 22 min read ![Sovereign AI in Europe: 5 EU-hosted platforms (2026)](/app/uploads/2026/04/Group-1777-6.png) On this page0% 1. [Introduction](#introduction) 2. [Quick comparison table](#quick-comparison-table) 3. [What to look for in a sovereign AI platform](#what-to-look-for-in-a-sovereign-ai-platform) 4. [1\. Bielik — Best for: Polish-language enterprise AI with maximum data sovereignty](#1-bielik-best-for-polish-language-enterprise-ai-with-maximum) 5. [2\. PLLuM — Best for: Polish public administration and EU AI Act-compliant enterprise deployment](#2-pllum-best-for-polish-public-administration-and-eu-ai-act-) 6. [3\. Mistral AI — Best for: EU enterprises requiring production-grade frontier AI with no CLOUD Act exposure](#3-mistral-ai-best-for-eu-enterprises-requiring-production-gr) 7. [4\. Aleph Alpha — Best for: Maximum EU sovereignty in critical infrastructure, defence, and German-regulated industries](#4-aleph-alpha-best-for-maximum-eu-sovereignty-in-critical-in) 8. [5\. Scaleway — Best for: EU-sovereign AI infrastructure with institutional-grade certification](#5-scaleway-best-for-eu-sovereign-ai-infrastructure-with-inst) 9. [How to choose the right sovereign AI platform for your organisation](#how-to-choose-the-right-sovereign-ai-platform-for-your-organ) 10. [Frequently asked questions](#frequently-asked-questions) 11. [Conclusion](#conclusion) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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European organisations face growing pressure to deploy AI within fully sovereign infrastructure. This article ranks five platforms and models by data sovereignty posture, EU and Polish regulatory compliance, and regional fit — from Polish-origin open-source large language models to EU-certified cloud infrastructure providers. The ranking draws exclusively from verified public sources. Two Polish models lead. Entry pricing ranges from free to enterprise custom. The right choice depends on language requirements, compliance posture, and deployment complexity. The top five **sovereign AI platforms in Europe** for 2026 are Bielik, PLLuM, Mistral AI, Aleph Alpha, and Scaleway. All five support on-premise or EU-only deployment with no exposure to US CLOUD Act jurisdiction. Two are Polish-origin open-source models. Entry pricing ranges from free to enterprise custom, depending on deployment model and support requirements. The right choice depends on language requirements, compliance posture, and whether the organisation needs a model or the infrastructure to run one. ## Introduction [#](#introduction) The question European organisations are asking today is not whether to adopt AI, but whether the AI they adopt can be trusted with sensitive data. Since the EU AI Act began phased enforcement in 2024 and the United States CLOUD Act confirmed that data held by US-headquartered providers can be compelled regardless of server location, **GDPR compliant AI deployment** has moved from a compliance checkbox to an active procurement requirement. For Polish organisations specifically, the challenge is compounded by language. General-purpose frontier models perform well in English. They perform materially worse in Polish — a morphologically complex language that standard tokenisers handle inefficiently. Two domestically developed models have changed that equation, and both are freely available for self-hosted deployment. This article ranks five **sovereign AI platforms in Europe** by four criteria: deployment sovereignty (where inference runs and who controls it), regulatory alignment (GDPR, EU AI Act, and sector-specific certifications), Polish language capability, and commercial readiness for enterprise use. The ranking draws exclusively from verified public sources. Estimated figures are identified as such. Where facts could not be independently verified, they have been omitted. The five platforms span purpose-built open-source language models, a European frontier AI company, a German sovereign AI platform, and an EU-certified cloud infrastructure provider. ![Four model providers above Scaleway as shared infrastructure, and Aleph Alpha running on STACKIT. Logos for Mistral AI, Aleph Alpha and Scaleway; Bielik, PLLuM and STACKIT are names only.](/app/uploads/2026/10/fig-sovereign-ai-platforms-europe-rk-sv-layers.png) Four platforms are models; Scaleway is where models run. ## Quick comparison table [#](#quick-comparison-table) The table below summarises key data points across all five platforms. Company names link to the respective provider websites. **Platform** **Country of origin** **Deployment options** **Language support** **Key certifications** **Entry pricing** [Bielik](https://huggingface.co/speakleash) Poland On-premise, private cloud, air-gapped Primary: Polish and English. Polish: best-in-class — outperforms models two to three times its size on all major Polish benchmarks. General multilingual capability is limited beyond these two base languages Apache 2.0; EU and Polish copyright-compliant training data Free — infrastructure costs only [PLLuM](https://pllum.org.pl) Poland On-premise, private cloud Primary: Polish, with English supplemental training data. Selected Slavic and Baltic languages supported. General multilingual capability outside Polish is limited EU AI Act-compliant by design; GDPR; built-in anonymisation; open licence Free — infrastructure costs only [Mistral AI](https://mistral.ai) France On-premise, private cloud, EU-hosted API Primary: French, German, Spanish, Italian, English. Broad multilingual capability across major European languages. Polish is supported but not optimised — outperformed by Bielik and PLLuM on Polish-specific benchmarks GDPR; EU AI Act-aligned; France and Germany government framework contracts Pay-per-token API; open-weight models free to self-host; enterprise on request [Aleph Alpha](https://aleph-alpha.com) Germany On-premise, STACKIT sovereign cloud, air-gapped Primary: German and English. The T-Free tokeniser-free architecture improves efficiency for morphologically complex languages. Major European languages supported. Polish is not specifically optimised GDPR; BDSG (German data protection law); EU AI Act-aligned; government and defence procurement Custom enterprise licensing — not publicly disclosed [Scaleway](https://www.scaleway.com) France Managed API (serverless), dedicated GPU, Warsaw availability zone Language capability depends on the model deployed. Pre-configured Generative APIs serve multilingual open-weight models (Mistral, Llama, Qwen families) covering French, English, German, Spanish, and other major languages. Bielik and PLLuM can be deployed via Managed Inference, adding Polish and English base-language optimisation ISO/IEC 27001:2022; HDS (healthcare); SEAL-3 (EU Commission Cloud Sovereignty Framework) Free tier of one million tokens; pay-per-token thereafter; Managed Inference billed hourly ## What to look for in a sovereign AI platform [#](#what-to-look-for-in-a-sovereign-ai-platform) Selecting from among **sovereign AI platforms in Europe** requires evaluating more than a data residency policy. Six criteria consistently determine whether a platform delivers genuine sovereignty or marketing language. ![Two-by-three grid of Polish optimisation against licence openness; only Bielik and PLLuM sit in the open and Polish-optimised cell. Logos for Mistral AI, Aleph Alpha and Scaleway.](/app/uploads/2026/10/fig-sovereign-ai-platforms-europe-rk-sv-fit.png) Only the two Polish models are both open and tuned for Polish. **Legal jurisdiction of the provider.** A server in Frankfurt operated by a US-headquartered company still falls under CLOUD Act jurisdiction. Only EU-incorporated, EU-owned providers are structurally free of that exposure. Jurisdiction follows the company, not the data centre. **Deployment flexibility.** Organisations handling sensitive data — particularly in healthcare, legal services, public administration, and financial services — require on-premise or air-gapped deployment where no data ever crosses an API boundary. Managed services are convenient; they are not always acceptable. **Compliance certifications.** GDPR compliance is the minimum. EU AI Act alignment, ISO/IEC 27001:2022, sector-specific certifications (HDS for healthcare, BDSG alignment for German public sector), and the EU Commission's SEAL-3 sovereignty standard distinguish serious sovereign platforms from those using the term loosely. **Polish language support.** For Polish enterprises and public bodies, general-purpose multilingual models carry a measurable accuracy gap on Polish text. Purpose-built models close it. The gap matters most in legal, medical, and administrative contexts where precision is not negotiable. **Open-source versus proprietary.** Open-weight models can be self-hosted, fine-tuned, and migrated. Proprietary platforms create dependency at the model layer — a distinct risk from infrastructure dependency, and one that persists long after contracts are signed. **Commercial support and enterprise readiness.** Open-source models offer maximum sovereignty but require in-house MLOps capability or an external implementation partner. Managed platforms reduce that overhead but reintroduce dependency. Understanding where that trade-off sits in an organisation's capability profile is a precondition for a sensible choice. ## 1\. [Bielik](https://huggingface.co/speakleash) — Best for: Polish-language enterprise AI with maximum data sovereignty [#](#1-bielik-best-for-polish-language-enterprise-ai-with-maximum) ### Overview **Best for:** Polish enterprises and public bodies requiring the highest Polish-language accuracy in a fully sovereign, self-hosted deployment. [Bielik](https://huggingface.co/speakleash) is an open-source **Polish AI model enterprise** deployment option developed by the SpeakLeash Foundation in partnership with ACK Cyfronet AGH. It is trained on the Helios and Athena supercomputers on a corpus of 292 billion tokens drawn from 303 million documents. Polish and English are both base languages — Polish is the primary optimisation target and the language in which Bielik outperforms much larger models, while English is included as a foundational training language. All training data is sourced in compliance with Polish and EU copyright law. Three model sizes are available — 1.5B, 4.5B, and 11B parameters — all released under the Apache 2.0 licence for on-premise deployment without royalty obligations. Jan Maria Kowalski of SpeakLeash has described Bielik as building a stable base that can secure Polish sectors in banking, administration, medicine, and law — the sectors where Polish-language accuracy matters most and where data leaving national infrastructure is most likely to create regulatory exposure. (Source: [Science in Poland, 2024](https://scienceinpoland.pl/en/news/news%2C104221%2Cbielik-has-landed-polish-language-model-spreads-its-wings-thanks-supercomputers)) ### Key facts * **Deployment options:** On-premise, private cloud, air-gapped; no external API dependency required at any stage of inference * **Data sovereignty:** Complete — inference runs locally, no data leaves client infrastructure * **Key certifications:** Training data compliant with Polish and EU copyright law; Apache 2.0 commercial licence * **Language support:** Primary languages are Polish and English. Polish is the primary optimisation target — the 4.5B parameter model outperforms models two to three times its size on all five major Polish benchmarks: Open PL LLM Leaderboard, CPTUB, MT-Bench-PL, EQ-Bench-PL, and the Polish Medical Leaderboard. A custom Polish tokeniser (APT4) significantly improves token efficiency on Polish text compared to standard tokenisers. General multilingual capability beyond Polish and English is limited * **Open-source:** Yes — Apache 2.0 (commercial use permitted) * **Notable clients:** Not publicly disclosed * **Entry pricing:** Free; infrastructure costs borne by the deploying organisation ### Strengths [Bielik](https://huggingface.co/speakleash) delivers the highest accuracy on Polish-language tasks of any available model, including models with parameter counts two to three times larger. The Apache 2.0 licence permits commercial deployment without royalty obligations, and the compact model sizes — 1.5B to 11B parameters — allow deployment on modest hardware, keeping the infrastructure cost threshold for a fully sovereign **Polish AI model enterprise** capability within reach of mid-market organisations. ### Limitations [Bielik](https://huggingface.co/speakleash) does not carry a commercial support SLA. Enterprise deployment at production scale requires in-house MLOps capability or an external implementation partner. While Polish and English are both base languages, general multilingual capability beyond these two is limited, making Bielik unsuitable as a general-purpose model for organisations operating across a broad range of languages. ## 2\. [PLLuM](https://pllum.org.pl) — Best for: Polish public administration and EU AI Act-compliant enterprise deployment [#](#2-pllum-best-for-polish-public-administration-and-eu-ai-act-) ### Overview **Best for:** Polish public bodies and enterprises in regulated sectors where EU AI Act compliance must be demonstrable at the model architecture level. [PLLuM](https://pllum.org.pl) is a Polish-language large language model developed by a consortium of six research institutions led by Wrocław University of Science and Technology, funded by the Polish Ministry of Digitisation. It is the only model in this ranking designed specifically for Polish public administration, and the only one in which EU AI Act compliance — incorporating transparency, auditability, and safety-by-design — is an architectural requirement rather than a retrospective addition. 18 open-access model variants are available, ranging from 8B to 70B parameters, and the model has been deployed in the Ministry of Digitisation's mObywatel citizen application. ### Key facts * **Deployment options:** On-premise, private cloud; fully self-hostable with no external API dependency * **Data sovereignty:** Complete — open-weight model; built-in anonymisation module detects and redacts personal and sensitive information at inference time * **Key certifications:** EU AI Act-compliant by design; GDPR-compliant architecture; open licence for commercial and public administration use; initial development funded at approximately 14.5 million złoty (approximately €3.5 million) by the Polish Ministry of Digitisation * **Language support:** Polish is the primary training language — 140-billion-token Polish pre-training corpus; 77,574-entry Polish instruction dataset. English is included as supplemental training data. Selected Slavic and Baltic languages receive partial support. General multilingual capability outside Polish is limited * **Open-source:** Yes — open licence permitting commercial and public administration use * **Notable clients:** Polish Ministry of Digitisation (mObywatel application); Polish public administration * **Entry pricing:** Free; infrastructure costs borne by the deploying organisation ### Strengths [PLLuM](https://pllum.org.pl) is the only model in this ranking with EU AI Act compliance built into its architecture from inception — output correction, auditability, and anonymisation are native components, not add-on tooling. Government backing from the Polish Ministry of Digitisation provides long-term development continuity that privately funded open-source projects cannot match. The 8B to 70B parameter range is the widest of any Polish model, giving deployment teams flexibility to match model size to hardware and task complexity. ### Limitations [PLLuM](https://pllum.org.pl) 's primary design target is public administration. Enterprise business tooling — observability dashboards, production guardrails, integration middleware — must be sourced and configured externally. Deployment at production enterprise scale requires either significant in-house MLOps capability or an external integration partner. ## 3\. [Mistral AI](https://mistral.ai) — Best for: EU enterprises requiring production-grade frontier AI with no CLOUD Act exposure [#](#3-mistral-ai-best-for-eu-enterprises-requiring-production-gr) ### Overview **Best for:** European enterprises in regulated sectors that need production-grade frontier AI model capability, EU data residency, and a dual deployment path (managed API or full self-hosting). [Mistral AI](https://mistral.ai) is a French frontier AI company and, as of April 2026, the only EU-headquartered provider in this ranking with a production enterprise track record at scale across regulated industries. Key models — including Mistral Small 4 and the Ministral family — are released under the Apache 2.0 licence for self-hosting. The flagship Le Chat Enterprise product provides a managed deployment path with EU data residency and no data transfer to third countries. Valued at €11.7 billion as of April 2026, Mistral has signed framework agreements with the governments of France and Germany for public administration AI deployment running through 2030. Arthur Mensch, CEO of Mistral AI, has stated that European governments choose Mistral because they want to build technology that directly serves their own citizens — a framing that reflects the company's deliberate positioning as the primary sovereign alternative to US frontier AI providers. (Source: [Sovereign Magazine, February 2026](https://www.sovereignmagazine.com/eu-focus/mistral-ai-europes-push-autonomous-ai-systems/)) ### Key facts * **Deployment options:** On-premise, private cloud, or EU-hosted serverless API; available via AWS Bedrock Frankfurt, Azure AI, and Google Vertex AI (EU regions only); Mistral Compute data centre (18,000 NVIDIA Grace Blackwell Superchips, Essonne, France) expected Q2 2026 * **Data sovereignty:** Strong — French HQ under EU jurisdiction; no CLOUD Act exposure by default; Le Chat Enterprise offers conversation history disabled mode and training data opt-out * **Key certifications:** GDPR-compliant by architecture; EU AI Act-aligned; confirmed framework contracts with French and German governments (2026–2030) * **Language support:** Primary languages are French, German, Spanish, Italian, and English. Broad multilingual capability across major European languages. Polish is supported but not optimised — outperformed by Bielik and PLLuM on Polish-specific benchmarks * **Open-source:** Partial — Mistral Small 4 and Ministral family under Apache 2.0; Mistral Large 3 is proprietary * **Notable clients:** HSBC (credit assessment and compliance review automation), Stellantis, Veolia; French and German governments (2026–2030 framework) * **Entry pricing:** API pricing published at [mistral.ai/pricing](https://mistral.ai/pricing) (pay-per-token); Apache 2.0 open-weight models free to self-host; Le Chat Enterprise and on-premise enterprise deployment pricing available on request ### Strengths [Mistral AI](https://mistral.ai) offers the most mature dual deployment path of any sovereign AI provider: a managed, EU-resident API for teams that want production access without infrastructure overhead, and self-hostable open-weight models for teams that require full air-gapped sovereignty. The combination of EU-native legal jurisdiction, confirmed production clients in regulated sectors, and open-weight model availability is unique among frontier AI providers operating at this scale. ### Limitations The flagship proprietary model (Mistral Large 3) introduces lock-in risk at the model layer for teams that build workflows dependent on it. Polish language performance trails purpose-built Polish models for tasks requiring high accuracy on Polish text. Full air-gapped deployment requires self-hosting the open-weight models, which adds infrastructure complexity beyond a standard API integration. > Note: Mistral AI's ISO 27001 and GAIA-X certification status could not be confirmed from public sources at the time of research. Check the current certification scope directly at mistral.ai. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## 4\. [Aleph Alpha](https://aleph-alpha.com) — Best for: Maximum EU sovereignty in critical infrastructure, defence, and German-regulated industries [#](#4-aleph-alpha-best-for-maximum-eu-sovereignty-in-critical-in) ### Overview **Best for:** Organisations in government, defence, and critical infrastructure where German data protection law applies and air-gapped deployment is non-negotiable. [Aleph Alpha](https://aleph-alpha.com) (operating its enterprise platform as PhariaAI) is a German AI company with the strongest institutional sovereignty posture of any commercial AI platform in this ranking. It operates on German-headquartered infrastructure via STACKIT — the cloud division of Schwarz Group — with no US hyperscaler infrastructure at any layer. PhariaAI's built-in explainability layer makes every output traceable, a capability rarely found in commercial LLMs and directly relevant for regulated-sector compliance audits. The platform is backed by over €500 million from SAP, Bosch, Schwarz Group, Deutsche Bank, and the German government DTCF fund. ### Key facts * **Deployment options:** On-premise, STACKIT sovereign cloud (German-operated, German-headquartered), or air-gapped; HPE hardware partnership enables full on-premise AI lifecycle management; no US hyperscaler infrastructure required at any layer * **Data sovereignty:** Maximum — German HQ, German infrastructure; no CLOUD Act exposure; built-in explainability layer makes every output traceable for audit purposes * **Key certifications:** GDPR-compliant; BDSG (German Federal Data Protection Act)-compliant; EU AI Act-aligned; confirmed procurement by German federal ministries and European defence agencies * **Language support:** Primary languages are German and English. The T-Free tokeniser-free architecture improves inference efficiency for morphologically complex languages, giving it an advantage for fine-tuning on non-standard European languages. Major European languages are supported. Polish is not specifically optimised * **Open-source:** Pharia-1-LLM (7B parameters) released under the Open Aleph License for non-commercial research only; PhariaAI enterprise platform is proprietary * **Notable clients:** Governments of Baden-Württemberg and Bavaria; German federal ministries; European defence agencies; Schwarz Group (Lidl/Kaufland); HPE * **Entry pricing:** Custom enterprise licensing — not publicly disclosed ### Strengths [Aleph Alpha](https://aleph-alpha.com) holds the longest documented track record in EU-specific sovereign AI procurement, with confirmed clients across German federal ministries and European defence agencies. The built-in explainability layer — where every model output is traceable — addresses a compliance requirement that most commercial LLM platforms do not meet natively, making it particularly appropriate for regulated-sector applications where output auditability is a contractual or regulatory requirement. ### Limitations The open-weight Pharia-1-LLM is restricted to non-commercial research use, limiting its utility compared to Apache 2.0 alternatives. Pricing is not publicly disclosed, which complicates procurement comparison. Commercial reach is narrower than Mistral, and there is no Polish-language optimisation. > **Note:** Cohere's acquisition of Aleph Alpha was announced on 24 April 2026 and remains subject to regulatory approval at the time of writing. Cohere is Canadian-headquartered. The impact of this acquisition on Aleph Alpha's EU sovereignty commitments, data residency guarantees, and PhariaAI product strategy is unconfirmed. Procurement teams evaluating Aleph Alpha should request explicit, contractually binding data residency commitments and monitor the regulatory approval outcome before committing. (Source: [TechCrunch, April 2026](https://techcrunch.com/2026/04/25/why-cohere-is-merging-with-aleph-alpha/)) ## 5\. [Scaleway](https://www.scaleway.com) — Best for: EU-sovereign AI infrastructure with institutional-grade certification [#](#5-scaleway-best-for-eu-sovereign-ai-infrastructure-with-inst) ### Overview **Best for:** Organisations that want to deploy any open-weight model — including Bielik or PLLuM — on EU-sovereign GPU infrastructure without building their own cluster, and that require institutional-grade sovereignty certification. [Scaleway](https://www.scaleway.com) is a French cloud infrastructure provider and the only platform in this ranking awarded the European Commission's SEAL-3 sovereignty certification — the highest level under the EU Cloud Sovereignty Framework. In April 2026, it was selected as one of four providers for the EU Commission's €180 million, six-year sovereign cloud framework contract. In October 2025, Scaleway was selected as infrastructure provider for the European Central Bank's digital euro project — the first major central bank payment infrastructure to specify EU-only cloud providers as a procurement requirement. Damien Lucas, CEO of Scaleway, has stated that the company commits to Europe's digital autonomy not only through technology but through how it builds and invests in the European ecosystem — a position reinforced by its selection for the two most demanding public-sector sovereignty procurement processes run in Europe in 2025–2026. (Source: [EC press release, April 2026](https://www.scaleway.com/en/news/scaleway-selected-by-the-european-commission-to-deliver-a-sovereign-public-cloud-and-ai-platform-to-eu-institutions/)) ### Key facts * **Deployment options:** Generative APIs (serverless, pay-per-token, pre-configured open-weight models, stateless inference); Managed Inference (dedicated GPU instances, private network isolation, custom model upload); 10 data centres across Europe including four in Paris, Amsterdam, and Warsaw * **Data sovereignty:** Full — 100% French-owned, no US parent company, no CLOUD Act exposure; stateless models retain no prompts or completions after processing; customer data is not accessible to other customers or to the underlying model creators; data is not used to train or improve models * **Key certifications:** ISO/IEC 27001:2022 (certified by BSI); HDS (French Health Data Hosting, since July 2024); SEAL-3 (EU Commission Cloud Sovereignty Framework, April 2026); SecNumCloud qualification process underway (ANSSI) * **Language support:** As an infrastructure platform, Scaleway's language capability is determined by the model deployed. Pre-configured Generative APIs serve multilingual open-weight models from Mistral, Meta Llama, and Qwen families, covering French, English, German, Spanish, and other major European and global languages. Bielik (Polish and English base) and PLLuM (Polish primary) can be deployed via Managed Inference using custom model upload, extending the platform's coverage to Polish-optimised inference within EU-sovereign infrastructure * **Open-source:** Scaleway is an infrastructure platform — it hosts and serves open-weight models from leading research labs (Mistral, Meta Llama, Qwen); OpenAI SDK-compatible; no lock-in at the model layer * **Notable clients:** European Commission (€180 million sovereign cloud framework, 2026–2032); European Central Bank (digital euro infrastructure, 2025); Mistral AI (primary GPU training infrastructure) * **Entry pricing:** Free tier of one million tokens per new account; pay-per-token from token 1,000,001; Managed Inference billed hourly per dedicated GPU instance ### Strengths [Scaleway](https://www.scaleway.com) holds the strongest institutional validation of any platform in this ranking: selection for both the EU Commission's sovereign cloud framework and the ECB's digital euro project within six months confirms that its sovereignty posture meets the strictest public-sector procurement standards in Europe. The stateless inference architecture — where no input or output is retained after processing — provides a data protection guarantee that goes beyond standard GDPR compliance requirements. The Warsaw availability zone makes it a natural hosting layer for Polish-origin models. ### Limitations An independent technical analysis (Xomnia, 2025) identified that Scaleway's management console infrastructure uses some US-based services. This does not affect data centre operations or storage jurisdiction but is relevant for organisations with strict operational sovereignty requirements across every infrastructure layer. Pre-configured Polish-optimised models are not available through Generative APIs — deploying Bielik or PLLuM requires using the Managed Inference product, which involves more configuration than a standard API integration. ## How to choose the right sovereign AI platform for your organisation [#](#how-to-choose-the-right-sovereign-ai-platform-for-your-organ) Polish enterprises and public bodies working primarily in Polish and handling regulated data — medical records, legal documents, administrative files — have the clearest starting point. [Bielik](https://huggingface.co/speakleash) addresses accuracy-sensitive language tasks: it outperforms models two to three times its size and is free to deploy commercially under Apache 2.0. [PLLuM](https://pllum.org.pl) is the better choice where EU AI Act compliance needs to be demonstrable at the model architecture level — its built-in anonymisation and output correction layers address a compliance requirement that Bielik does not include natively. Both are free, self-hostable, and keep data entirely within client infrastructure. ![Decision tree: if you need a model, four needs lead to Bielik, PLLuM, Mistral AI or Aleph Alpha; if you need somewhere to run it, Scaleway. Logos for Mistral AI, Aleph Alpha and Scaleway.](/app/uploads/2026/10/fig-sovereign-ai-platforms-europe-rk-sv-choose.png) Decide first whether you need a model or a place to run one. For European enterprises in regulated sectors — financial services, healthcare, critical infrastructure — that require production-grade frontier AI without US jurisdiction exposure, [Mistral AI](https://mistral.ai) offers the most mature commercially available option. The combination of self-hostable open-weight models under Apache 2.0, an EU-resident managed API, and confirmed deployment at HSBC and across European government frameworks makes it the pragmatic choice for organisations that cannot wait for smaller models to close the capability gap on complex reasoning tasks. Organisations in government, defence, or sectors where German data protection law (BDSG) governs and air-gapped deployment is mandatory will find [Aleph Alpha](https://aleph-alpha.com) 's PhariaAI the most appropriate choice on sovereignty grounds. The caveat is material: the Cohere acquisition announced on 24 April 2026 introduces strategic uncertainty. Procurement teams should request explicit, contractually binding data residency commitments and monitor the regulatory approval outcome before making long-term commitments to this platform. For teams that want to deploy Bielik or PLLuM — or any other open-weight model — on EU-sovereign GPU infrastructure without building their own cluster, [Scaleway](https://www.scaleway.com) provides the certified hosting layer. The Warsaw availability zone and Managed Inference product create a fully Polish and EU-sovereign AI stack — model origin, inference infrastructure, and data residency all within EU jurisdiction — with no component falling under foreign legal exposure. For organisations that need to demonstrate compliance to public-sector procurement standards, Scaleway's SEAL-3 certification and EC/ECB client references are the strongest third-party validation currently available. ## Frequently asked questions [#](#frequently-asked-questions) ### What is a sovereign AI platform? A sovereign AI platform is one where the organisation deploying it retains full legal and operational control over where data is processed, who can access it, and under which jurisdiction. In practice, this means the platform provider must be incorporated and operated in the EU (to avoid CLOUD Act exposure), and deployment must be possible on infrastructure the organisation controls — whether on-premise, in a private cloud, or in a certified EU-sovereign hosting environment. Data residency alone — servers in Europe operated by a US company — is not sufficient for full sovereignty. ### What is the difference between Bielik and PLLuM? Both are Polish-origin open-source large language models, both are free for commercial deployment, and both keep data entirely on client infrastructure when self-hosted. The key difference is design intent. [Bielik](https://huggingface.co/speakleash) is optimised for Polish-language accuracy — its 4.5B parameter model outperforms models two to three times its size on Polish benchmarks — making it the better choice for language-sensitive tasks. [PLLuM](https://pllum.org.pl) is designed for Polish public administration and built with EU AI Act compliance (transparency, auditability, output correction, anonymisation) as a native architectural feature — making it the better choice where demonstrable regulatory compliance is a procurement requirement. ### Which sovereign AI platform is best for a Polish enterprise? For Polish-language tasks requiring high accuracy, [Bielik](https://huggingface.co/speakleash) is the strongest choice — it is free, commercially licensed, and outperforms general-purpose frontier models on Polish benchmarks. For regulated sectors where EU AI Act compliance must be demonstrable at the model level, [PLLuM](https://pllum.org.pl) is the more appropriate option. Organisations that lack internal MLOps capability to self-host either model can deploy both on [Scaleway](https://www.scaleway.com) 's Managed Inference product using the Warsaw availability zone, creating a fully Polish and EU-sovereign stack without managing GPU infrastructure independently. ### What is the EU AI Act and how does it affect AI platform selection? The EU AI Act is the European Union's primary legislation governing the development, deployment, and use of AI systems. It classifies AI applications by risk level and requires that high-risk systems — including AI used in healthcare, education, critical infrastructure, employment, and public administration — meet requirements for transparency, human oversight, auditability, and accuracy. For platform selection, this means that AI systems deployed in high-risk categories must be able to demonstrate compliance with these requirements, which favours platforms that build auditability and output traceability into their architecture rather than relying on third-party add-ons. Of the five platforms in this ranking, [PLLuM](https://pllum.org.pl) is the only one designed with EU AI Act compliance as a core architectural requirement from inception. ### What is the CLOUD Act and why does it matter for European AI deployment? The US Clarifying Lawful Overseas Use of Data Act (CLOUD Act) permits US authorities to compel US-headquartered companies to provide data stored anywhere in the world, including on servers physically located in the EU. This means that choosing a cloud provider or AI platform operated by a US company — even one with EU data centres and EU data residency commitments — does not provide full legal protection against US government data access. The only way to eliminate CLOUD Act exposure is to use providers incorporated and operating entirely within the EU, with no US parent company. All five platforms in this ranking meet that requirement at the provider level. ### Can open-source AI models meet EU data sovereignty requirements? Yes — open-source models that can be self-hosted on EU infrastructure are among the strongest options for data sovereignty, because inference never requires data to leave the organisation's own systems. [Bielik](https://huggingface.co/speakleash) and [PLLuM](https://pllum.org.pl) are both deployable in fully air-gapped environments, meaning no network traffic leaves the client infrastructure during model inference. The compliance requirements — GDPR, EU AI Act, sector-specific certifications — apply at the infrastructure and operational level, not the model level, so they can be addressed independently of which open-source model is chosen. ### Can Bielik be used commercially? [Bielik](https://huggingface.co/speakleash) is released under the Apache 2.0 licence, which permits commercial use, modification, and distribution without royalty obligations. Infrastructure costs — GPU compute, storage, and network — are borne by the deploying organisation. There is no per-token licensing fee and no requirement to pay the model's developers for commercial deployment. The Apache 2.0 licence does not restrict commercial use by company size or deployment scale, which distinguishes it from some other open-weight model licences that carry restrictions for very large deployments. ### How does Scaleway's SEAL-3 certification differ from ISO 27001? ISO/IEC 27001:2022 is an international information security management standard that certifies an organisation's processes for managing data security risks. SEAL-3 is the European Commission's Cloud Sovereignty Framework rating, which specifically assesses whether a cloud provider's service, technology, and operations are immune from supply chain disruption from non-EU third parties. SEAL-3 — the highest level — means that the provider not only stores data in Europe but develops its own technology and cannot be blocked or compelled by a non-EU actor. [Scaleway](https://www.scaleway.com) holds both ISO/IEC 27001:2022 and SEAL-3, making it the only platform in this ranking certified under the EU's specific sovereignty framework. ### What does **GDPR compliant AI deployment** actually require in practice? **GDPR compliant AI deployment** requires that personal data processed by an AI system is handled lawfully, with appropriate legal basis; that data subjects retain rights (access, erasure, portability) over their data; that processing is limited to the stated purpose; and that data is not transferred outside the EU without adequate protection. In AI deployment specifically, this means ensuring that inference data — the inputs and outputs of model processing — is not retained, shared with third parties, or used to train models without explicit consent. All five platforms in this ranking address these requirements, though the mechanisms differ: open-source self-hosted models do so by design (no data leaves the client); managed platforms (Mistral, Scaleway) provide contractual and architectural guarantees. ### Do I need in-house MLOps capability to deploy Bielik or PLLuM? Deploying [Bielik](https://huggingface.co/speakleash) or [PLLuM](https://pllum.org.pl) directly on client infrastructure requires MLOps capability to manage the model serving layer, monitoring, and integration with existing systems. Organisations without that capability have two practical options. The first is to use [Scaleway](https://www.scaleway.com) 's Managed Inference product, which handles the infrastructure layer while retaining EU sovereignty. The second is to engage an external agentic AI implementation partner — such as [Vstorm](/), which specialises in deploying production-grade agentic AI systems using open-source stacks including sovereign Polish-language models. The partner route is particularly appropriate where the deployment requires integration with existing enterprise systems, observability, or human-in-the-loop workflows. ## Conclusion [#](#conclusion) The most consequential distinction across these five platforms is not model capability — it is the question of where inference runs and who holds legal access to what happens during it. For Polish organisations, two domestically developed models resolve both questions by design. [Bielik](https://huggingface.co/speakleash) leads on Polish-language accuracy. [PLLuM](https://pllum.org.pl) leads on EU AI Act architectural compliance. Both are free. Neither requires an API call that leaves national infrastructure. For broader European enterprise deployment, [Mistral AI](https://mistral.ai) and [Scaleway](https://www.scaleway.com) represent the most commercially mature options — one at the model layer, one at the infrastructure layer — both free of CLOUD Act exposure and both carrying the strongest institutional validation currently available in the EU. [Aleph Alpha](https://aleph-alpha.com) remains the strongest option for German-jurisdiction and defence deployments, but the Cohere acquisition warrants close monitoring before any long-term procurement commitment. **GDPR compliant AI deployment** does not require choosing between capability and compliance. All five platforms in this ranking demonstrate that the two are compatible. The choice is a question of fit — language requirements, compliance posture, infrastructure capability, and deployment complexity — not a question of whether sovereign AI in Europe is achievable. It already is. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Human in the loop for AI agents: when the agent needs permission to change the plan](/app/uploads/2026/10/cover-human-in-the-loop-ai-agents-permission-to-change-the-plan-v3.png) ### Human in the loop for AI agents: when the agent needs permission to change the plan Read article ](/agentic-ai/human-in-the-loop-ai-agents-permission-to-change-the-plan/)[ ![Process mapping for agentic AI: a step-by-step guide](/app/uploads/2026/10/cover-process-mapping-for-agentic-ai-v3.png) ### Process mapping for agentic AI: a step-by-step guide Read article ](/agentic-ai/process-mapping-for-agentic-ai/)[ ![Agentic AI governance in regulated industries: what actually binds you](/app/uploads/2026/10/cover-agentic-ai-governance-in-regulated-industries-v3.png) ### Agentic AI governance in regulated industries: what actually binds you Read article ](/agentic-ai/agentic-ai-governance-in-regulated-industries/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AI process automation for SMB: traditional automation vs agentic AI URL: https://vstorm.co/agentic-ai/ai-process-automation-for-smb-traditional-automation-vs-agentic-ai EY finds 30–50% of RPA projects fail, often on maintenance. Where rule-based automation is still right, and where agentic AI is the only option. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/AI process automation for SMB: traditional automation vs agentic AI [Agentic AI](/ai-blog-news/#agentic-ai) # AI process automation for SMB: traditional automation vs agentic AI EY finds 30–50% of RPA projects fail, often on maintenance. Where rule-based automation is still right, and where agentic AI is the only option. ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer April 22, 2026 Updated July 26, 2026 7 min read ![AI process automation for SMB: traditional automation vs agentic AI](/app/uploads/2026/04/Group-1777-1-3.png) On this page0% 1. [How SMBs currently automate, and where it stops working](#how-smbs-currently-automate-and-where-it-stops-working) 2. [What traditional automation does well](#what-traditional-automation-does-well) 3. [Where rule-based automation breaks down](#where-rule-based-automation-breaks-down) 4. [What agentic AI workflow automation actually does](#what-agentic-ai-workflow-automation-actually-does) 5. [Side-by-side: business process automation AI vs RPA](#side-by-side-business-process-automation-ai-vs-rpa) 6. [A practical decision framework for SMB leaders](#a-practical-decision-framework-for-smb-leaders) 7. [What the right implementation looks like](#what-the-right-implementation-looks-like) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. Share [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-for-smb-traditional-automation-vs-agentic-ai%2F "LinkedIn")[](https://x.com/intent/tweet?text=AI%20process%20automation%20for%20SMB%3A%20traditional%20automation%20vs%20agentic%20AI&url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-for-smb-traditional-automation-vs-agentic-ai%2F "X")[](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-for-smb-traditional-automation-vs-agentic-ai%2F "Facebook")[](https://www.reddit.com/submit?url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-for-smb-traditional-automation-vs-agentic-ai%2F&title=AI%20process%20automation%20for%20SMB%3A%20traditional%20automation%20vs%20agentic%20AI "Reddit")[](mailto:?subject=AI%20process%20automation%20for%20SMB%3A%20traditional%20automation%20vs%20agentic%20AI&body=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-for-smb-traditional-automation-vs-agentic-ai%2F "Email") Link copied Summarize with AI [GPT](https://chat.openai.com/?prompt=Summarize%20the%20following%20page%3A%20AI%20process%20automation%20for%20SMB%3A%20traditional%20automation%20vs%20agentic%20AI%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-for-smb-traditional-automation-vs-agentic-ai%2F\))[Claude](https://claude.ai/new "Opens Claude and copies the summarize prompt — paste with ⌘V / Ctrl+V")[Perplexity](https://www.perplexity.ai/?q=Summarize%20the%20following%20page%3A%20AI%20process%20automation%20for%20SMB%3A%20traditional%20automation%20vs%20agentic%20AI%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-for-smb-traditional-automation-vs-agentic-ai%2F\))[Grok](https://grok.com/?q=Summarize%20the%20following%20page%3A%20AI%20process%20automation%20for%20SMB%3A%20traditional%20automation%20vs%20agentic%20AI%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-for-smb-traditional-automation-vs-agentic-ai%2F\)) Traditional RPA and agentic AI are not competing technologies; they solve different problems. RPA handles high-volume, rule-based tasks with stable inputs. Agentic AI handles variable, decision-intensive workflows where exceptions are routine. Ernst & Young found 30–50% of initial RPA projects fail, often due to maintenance demands on processes that were never suited to script-based automation. This article provides a practical decision framework for mid-market leaders choosing between the two approaches, with three diagnostic questions, a side-by-side comparison, and real implementation examples from healthcare and engineering. The question is not which automation approach is more advanced. It is which one fits the process you are actually trying to automate. **AI process automation for SMB** has split into two distinct approaches, traditional rule-based automation (commonly called RPA) and agentic AI, and choosing between them without understanding the difference costs mid-market companies both money and time. This article explains what each approach does, where each one is genuinely the right tool, and how to make a decision that holds up in production. ## How SMBs currently automate, and where it stops working [#](#how-smbs-currently-automate-and-where-it-stops-working) Most mid-market companies run process automation through a combination of three layers: manual workflows handled by staff, SaaS integration tools such as Zapier or Make for connecting applications, and in some cases RPA platforms like UiPath or Blue Prism for repetitive back-office tasks. This infrastructure handles a significant share of operational work. Invoice processing, data entry across systems, order status updates, appointment scheduling: these are the tasks that RPA was designed for and continues to manage well in many organisations. The problem surfaces when the underlying conditions change. A supplier updates their invoice format. A CRM field is renamed after a software update. A customer submits an order with non-standard specifications. At each of these points, the script-based automation stops, sometimes completely, sometimes silently producing errors that are only discovered downstream. For larger organisations with dedicated IT teams, broken bots are a maintenance task. For SMBs, they are a genuine operational risk. Understanding [what AI automation actually means](/agentic-ai/what-do-we-mean-by-ai-automation-actually/) in practice, beyond the vendor claims, is where this decision has to start. ## What traditional automation does well [#](#what-traditional-automation-does-well) Before making any comparison, it is worth being direct about where RPA performs well. It is the right choice for high-volume, predictable tasks with stable inputs and outputs. If your organisation processes thousands of invoices per month in a consistent format, transfers data between systems on a fixed schedule, or runs compliance checks against structured records, RPA delivers genuine value. It executes without fatigue, maintains audit trails, and integrates with legacy systems that do not expose APIs, a critical advantage for mid-market companies running infrastructure that predates modern integration standards. The organisations that get the most from RPA are those that deploy it on processes that match its design: repeatable, rules-based, and unlikely to change frequently. The failures almost always come from applying it to processes that do not fit that profile. ## Where rule-based automation breaks down [#](#where-rule-based-automation-breaks-down) The core architectural limitation of RPA is not a flaw; it is a design choice. Bots execute precise scripts. When the environment those scripts were written for changes, the bot fails. Ernst & Young's global RPA consulting practice, spanning implementations across 20 countries, found that 30–50% of initial RPA projects fail, not due to the technology itself, but due to misapplication and maintenance demands that were not accounted for at the outset ( EY). The cost structure compounds this problem. According to HfS Research, RPA licensing represents only 25–30% of total cost of ownership. The remaining 70–75% is consumed by implementation, maintenance, and ongoing support ( [HfS Research](https://www.hfsresearch.com/research/how-to-make-sense-of-nonsensical-rpa-software-pricing/)). For an SMB without a dedicated automation team, this is rarely budgeted at the outset, and rarely sustainable once the true cost becomes clear. The processes that break RPA most often are precisely the ones mid-market companies most need to automate: cross-departmental workflows, customer-facing processes with variable inputs, and anything that touches unstructured data such as emails, documents, or voice. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## What **agentic AI workflow automation** actually does [#](#what-agentic-ai-workflow-automation-actually-does) Agentic AI systems work differently at an architectural level. Rather than following a fixed script, an AI agent receives a goal, determines the steps required to achieve it, calls the tools and systems it needs, handles exceptions as they arise, and adjusts its approach based on what it finds. The practical difference is most visible in exception handling. An RPA bot processing purchase orders stops when an order arrives in a format it was not scripted for. An AI agent reads the order, identifies the structural difference, adapts its extraction logic, processes the order, and flags the anomaly for human review, without halting the workflow. > "AI agents are evolving rapidly, progressing from basic assistants embedded in enterprise applications today to task-specific agents by 2026 and ultimately multiagent ecosystems by 2029." Anushree Verma, Senior Director Analyst, Gartner ( Gartner, August 2025 ) The market reflects this shift. Gartner predicts that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. We have seen this play out directly in client work. At a healthcare provider, appointment scheduling had previously required administrative staff to manage requests across phone, email, and a patient portal separately, each channel handled as a distinct manual task. A [multi-channel AI agent](/case-study/multi-channel-ai-agent-in-healthcare/) now handles all three, routes complex cases to human staff based on defined criteria, and manages exceptions without interrupting the workflow. In engineering, we deployed a [text-to-workflow agentic platform](/case-study/text-to-workflow-cuts-engineers-tedious-task-time-to-seconds-with-agentic-ai-platform/) that replaced manual task configuration. Engineers now provide a natural language instruction; the agent determines and executes the required sequence. Tasks that previously occupied significant manual time were reduced to seconds. Neither outcome was achievable with RPA. Both involved variable inputs, multi-system coordination, and decision-making at the point of exception. ## Side-by-side: **business process automation AI vs RPA** [#](#side-by-side-business-process-automation-ai-vs-rpa) **Dimension** **Traditional automation (RPA)** **Agentic AI process automation** Process type Structured, rule-based, predictable Variable, cross-system, exception-heavy Input handling Structured data only Structured and unstructured (emails, documents, speech) Response to change Halts; requires re-scripting Adapts within defined parameters Maintenance burden High; every process or UI change requires bot update Lower; agents adjust without re-scripting Decision-making None; executes fixed logic Context-aware; plans and reasons across steps Best fit for SMB High-volume back-office tasks with stable inputs Cross-departmental workflows with variation and exceptions ## A practical decision framework for SMB leaders [#](#a-practical-decision-framework-for-smb-leaders) Three questions determine which approach fits a given process. **Does the process change frequently?** If workflows are regularly updated, new fields, new formats, new systems, RPA maintenance costs will compound. Each change requires re-scripting, which requires IT time that most SMBs cannot consistently absorb. Agentic AI adapts within its operational parameters without manual updates to the underlying logic. **Does the process require decision-making or exception handling?** If the answer is yes in any consistent proportion, RPA is not the right tool. It was designed for execution, not judgment. Agentic AI handles the variable layer: routing, prioritising, interpreting, and escalating to human staff when the situation requires it. **Do you have dedicated IT capacity to manage bot maintenance?** If not, the HfS Research cost structure applies directly. The majority of your total automation spend will go to maintenance rather than capability, reducing the ROI case with every process change. The most effective mid-market automation architectures use both approaches: RPA for reliable, high-volume core execution and agentic AI for the variable, decision-intensive layer above it. This is not a choice between technologies; it is an allocation decision based on process type. One important counterbalance: Gartner predicts that over 40% of agentic AI projects will be cancelled by end of 2027 due to unclear ROI and inadequate risk controls ( [Gartner, June 2025](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027)). That figure measures enterprise project outcomes; it is separate from Gartner's parallel prediction that 40% of enterprise applications will embed AI agents by end of 2026, which measures software vendor adoption, not implementation success. Choosing the right technology is only half the decision. Choosing the right use case, and the right implementation approach, determines whether the investment produces results. For SMBs without existing automation infrastructure, the practical starting point is one high-value, variable process, not a broad RPA deployment. Customer intake, document processing, and cross-system order management are common first candidates. Deploying a single reliable agent for one bounded workflow produces faster ROI and a clearer picture of where to expand. For SMBs with existing RPA, the starting point is an audit of which bots are breaking most often and consuming the most maintenance hours. Those are the processes where agentic AI creates the most immediate operational value, not as a wholesale replacement for everything RPA does, but as the right tool for the workflows that RPA was never suited to handle. In both cases, the principle is the same: prove value in production before expanding scope. Multi-agent architectures and complex orchestration follow from a single working agent; they do not precede it. The implementation partner question matters here more than it does with conventional software. Agentic AI in production requires expertise in system integration, observability, and failure mode management that is distinct from general software development. The [Vstorm](/ai-consultancy/) approach starts with process discovery before engineering, because the choice of what to automate determines whether the investment returns what it should. ## What the right implementation looks like [#](#what-the-right-implementation-looks-like) ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer [Author profile](/author/konrad-budek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Healthcare process automation with Agentic AI: what is deployable today vs in two years](/app/uploads/2026/04/Group-1777-2-1.png) ### Healthcare process automation with Agentic AI: what is deployable today vs in two years Read article ](/agentic-ai/healthcare-process-automation-with-agentic-ai-what-is-deployable-today-vs-in-two-years/)[ ![How to integrate AI agents with EHR systems: Epic, Cerner, and custom integrations](/app/uploads/2026/04/Group-1777-5.png) ### How to integrate AI agents with EHR systems: Epic, Cerner, and custom integrations Read article ](/agentic-ai/how-to-integrate-ai-agents-with-ehr-systems-epic-cerner-and-custom-integrations/)[ ![Agentic AI vs RPA in healthcare: what is the difference and which should you implement](/app/uploads/2026/04/Group-1777-3.png) ### Agentic AI vs RPA in healthcare: what is the difference and which should you implement Read article ](/agentic-ai/agentic-ai-vs-rpa-in-healthcare-what-is-the-difference-and-which-should-you-implement/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AI process automation in print on demand: five use cases that deliver measurable results for SMBs URL: https://vstorm.co/agentic-ai/ai-process-automation-in-print-on-demand-five-use-cases-that-deliver-measurable-results-for-smbs Complex custom orders, thin margins, small teams on email and spreadsheets. Five print on demand automation use cases grounded in shipped work. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/AI process automation in print on demand: five use cases that deliver measurable results for SMBs [Agentic AI](/ai-blog-news/#agentic-ai) # AI process automation in print on demand: five use cases that deliver measurable results for SMBs Complex custom orders, thin margins, small teams on email and spreadsheets. Five print on demand automation use cases grounded in shipped work. ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer April 30, 2026 9 min read ![AI process automation in print on demand: five use cases that deliver measurable results for SMBs](/app/uploads/2026/04/Group-1777-6-1.png) On this page0% 1. [Introduction](#introduction) 2. [Why process automation is a strategic priority for POD SMBs](#why-process-automation-is-a-strategic-priority-for-pod-smbs) 3. [Guided order configuration: turning a support cost into a conversion channel](#guided-order-configuration-turning-a-support-cost-into-a-con) 4. [AI order management and intelligent routing](#ai-order-management-and-intelligent-routing) 5. [Customer support and post-order inquiry handling](#customer-support-and-post-order-inquiry-handling) 6. [Production scheduling and print workflow management](#production-scheduling-and-print-workflow-management) 7. [Reporting and business intelligence automation](#reporting-and-business-intelligence-automation) 8. [Where to start: selecting the highest-value use case first](#where-to-start-selecting-the-highest-value-use-case-first) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. Share [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-in-print-on-demand-five-use-cases-that-deliver-measurable-results-for-smbs%2F "LinkedIn")[](https://x.com/intent/tweet?text=AI%20process%20automation%20in%20print%20on%20demand%3A%20five%20use%20cases%20that%20deliver%20measurable%20results%20for%20SMBs&url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-in-print-on-demand-five-use-cases-that-deliver-measurable-results-for-smbs%2F "X")[](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-in-print-on-demand-five-use-cases-that-deliver-measurable-results-for-smbs%2F "Facebook")[](https://www.reddit.com/submit?url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-in-print-on-demand-five-use-cases-that-deliver-measurable-results-for-smbs%2F&title=AI%20process%20automation%20in%20print%20on%20demand%3A%20five%20use%20cases%20that%20deliver%20measurable%20results%20for%20SMBs "Reddit")[](mailto:?subject=AI%20process%20automation%20in%20print%20on%20demand%3A%20five%20use%20cases%20that%20deliver%20measurable%20results%20for%20SMBs&body=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-in-print-on-demand-five-use-cases-that-deliver-measurable-results-for-smbs%2F "Email") Link copied Summarize with AI [GPT](https://chat.openai.com/?prompt=Summarize%20the%20following%20page%3A%20AI%20process%20automation%20in%20print%20on%20demand%3A%20five%20use%20cases%20that%20deliver%20measurable%20results%20for%20SMBs%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-in-print-on-demand-five-use-cases-that-deliver-measurable-results-for-smbs%2F\))[Claude](https://claude.ai/new "Opens Claude and copies the summarize prompt — paste with ⌘V / Ctrl+V")[Perplexity](https://www.perplexity.ai/?q=Summarize%20the%20following%20page%3A%20AI%20process%20automation%20in%20print%20on%20demand%3A%20five%20use%20cases%20that%20deliver%20measurable%20results%20for%20SMBs%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-in-print-on-demand-five-use-cases-that-deliver-measurable-results-for-smbs%2F\))[Grok](https://grok.com/?q=Summarize%20the%20following%20page%3A%20AI%20process%20automation%20in%20print%20on%20demand%3A%20five%20use%20cases%20that%20deliver%20measurable%20results%20for%20SMBs%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-process-automation-in-print-on-demand-five-use-cases-that-deliver-measurable-results-for-smbs%2F\)) Print on demand SMBs operate under specific pressure: complex customised orders, thin margins, and customer expectations set by enterprise-grade platforms, managed by small teams running on email, phone calls, and spreadsheets. This article covers five AI process automation print on demand use cases grounded in what we have built and deployed. Each section covers how the process works today without AI, what agentic automation replaces, and what measurable results follow. We anchor each use case in real implementations. That is the only honest way to make the case. ## Introduction [#](#introduction) One of the most common questions we hear from print on demand operators is not "should we automate?" The question is always: "where do we start?" The operational complexity of a POD business is easy to underestimate. Every order involves custom variables. Every customer arrives with different expectations. Every supplier relationship requires coordination. The global POD market stood at $10.78 billion in 2025 and is projected to grow at a CAGR of 23.6% through 2033, according to [Grand View Research](https://www.grandviewresearch.com/industry-analysis/print-on-demand-market-report). That growth creates scale pressure that most manual workflows cannot absorb. This article is for the operators, automation leads, and CTOs who need to move from that question to a specific answer. We cover five AI process automation print on demand use cases; not as possibilities, but as systems we have built, measured, and deployed into production. ## Why process automation is a strategic priority for POD SMBs [#](#why-process-automation-is-a-strategic-priority-for-pod-smbs) Print on demand SMBs sit in a structural bind. Profit margins per unit typically run between 10% and 30%, which means errors are expensive, manual overhead compounds quickly, and scaling volume without scaling headcount is a genuine business constraint, not a preference. The operational reality is that most POD SMBs still run critical workflows on the same tools they used when they were a fraction of their current size. A [2025 Parseur survey](https://www.prnewswire.com/news-releases/survey-manual-data-entry-costs-american-companies-more-than-28-000-per-employee-each-year-302516867.html) found that professionals spend an average of nine hours per week manually transferring data from emails, PDFs, and spreadsheets, at an average cost of $28,500 per employee per year. That figure does not include the downstream cost of errors that these processes produce. McKinsey research indicates that automation can reduce operational costs by up to 30%, a gap that grows more significant as order volume rises. The five use cases below represent the highest-value automation opportunities we have identified across our POD engagements. They are ordered by the frequency with which they surface as the most acute pain point, not by technical complexity. ## Guided order configuration: turning a support cost into a conversion channel [#](#guided-order-configuration-turning-a-support-cost-into-a-con) **How it works today.** When a new customer arrives on a POD platform to order a custom book, brochure, or merchandise item, they encounter a decision tree that most non-specialists find overwhelming: paper weight, binding type, trim size, cover finish, quantity tiers, and finish options that interact with one another. Most POD SMBs manage this through a combination of FAQ pages, email back-and-forth, and phone support. Staff interpret the customer's intent, check it against production constraints, and manually build the specification. At Mixam, 70% of new customers needed significant guidance before placing an order; this support overhead is both a direct cost and a conversion barrier. **The agentic AI solution.** A conversational AI agent trained on the company's product catalogue and constrained to validated output formats handles this interaction in natural language. The agent accesses live product specifications, validates each selection against production rules, and generates a confirmed order specification without human involvement. Guardrails prevent the agent from operating outside its defined scope. It will not recommend a binding type that conflicts with a selected paper weight, and it will not drift into topics unrelated to the order. **What we built at Mixam.** We designed and deployed a three-agent system: a product advisor, a specification validator, and an escalation handler, using PydanticAI and a RAG vector store built on Mixam's product knowledge base. Within one day of launching in Australia, orders increased by 11.76%. The agent now handles 10,000 users daily, processes 100,000 custom orders per month, and converts 62.11% of quotes into confirmed, paid orders. The workflow success rate reached 95.4%, exceeding the client's own target of 80%. > "My wish was to come to at least an 80% success rate in the workflow results, and by the time we finished the project, the success rate is, I believe, over 95.4%, so it definitely exceeded expectations. But at the same time, we did listen thoroughly to all the advice we got from Vstorm and I think that made a big impact." Lucian Puca, Digital Product Manager, Automation and Workflow Lead, Mixam Read the full case study: [AI agent for order completion in print on demand](/case-study/ai-agent-for-order-recommendation-and-completion/) ## AI order management and intelligent routing [#](#ai-order-management-and-intelligent-routing) **How it works today.** Order management in most POD SMBs involves staff manually checking incoming orders from multiple sales channels: Shopify, Etsy, and direct platforms. They verify print specifications against production constraints and route each job to the correct queue or fulfilment supplier. When an order arrives with a file that does not meet colour profile requirements, or a specification that conflicts with a selected substrate, a human has to catch it. When they do not, the error surfaces at the press, producing a reprint, a delay, and a customer complaint that erodes a margin that was already thin. **The agentic AI solution.** An AI order management print on demand agent ingests orders from all active channels, validates specifications against production rules, identifies discrepancies before they reach production, and routes each job to the correct queue or supplier. For operations with multiple fulfilment partners across geographies, a multi-agent architecture separates the routing logic from supplier communication; each is handled by a dedicated agent with a defined scope and clear escalation path. McKinsey research cited in manufacturing operations analysis indicates a 30–40% productivity improvement through automation in order management workflows. In a POD context, the most immediate gain is not speed. It is error elimination at the intake stage, before a mis-specified job enters production. The architecture here mirrors what we built for [Mixam's order completion system](/agentic-ai-in-print-on-demand/); agents access 15 distinct tools to validate, route, and confirm orders with near-zero manual intervention. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Customer support and post-order inquiry handling [#](#customer-support-and-post-order-inquiry-handling) **How it works today.** Support queues in POD businesses follow a predictable pattern. The majority of tickets cover a short list of queries: where is my order, can I change the paper type, my proof looks different from what I expected, when will my job ship. Each query requires a support agent to look up the order record, check production status, cross-reference supplier systems, and compose a response. None of this work requires human judgment. It requires access to data. For SMBs with two or three support staff, this volume is manageable until the business grows, at which point adding headcount becomes the only available lever. **The agentic AI solution.** A support agent with RAG access to order records, production status data, and product knowledge resolves routine queries autonomously. Complex or sensitive cases are escalated with full context pre-assembled; the human agent does not reconstruct the situation from scratch. The agent operates across email, chat, and platform messaging from a single deployment. [75% of SMBs are already investing in AI](https://www.salesforce.com/ap/blog/2025-smb-trends/) to address operational inefficiencies of this kind, and those that do are 1.8x more likely to experience revenue growth, according to Salesforce's 2025 SMB AI Trends Report. The mechanism is straightforward: when support capacity is no longer the bottleneck, the business can absorb more volume without proportional cost growth. The Mixam implementation includes support-adjacent functionality; the order advisor handles specification queries, product questions, and configuration corrections that would otherwise arrive as support tickets. ## Production scheduling and print workflow management [#](#production-scheduling-and-print-workflow-management) **How it works today.** Scheduling production jobs in a POD operation is a coordination problem with real cost implications. Jobs need to be grouped by substrate, colour profile, and equipment requirements to minimise setup waste. Artwork files need to be validated, confirmed against resolution, colour profile, and bleed requirements, before they reach the press. Both tasks are typically handled manually: a production coordinator builds the schedule based on experience and available queue slots, and a preflight operator checks files one by one. As order volume grows, the preflight step becomes the constraint that limits throughput. **The agentic AI solution.** An agent monitors incoming jobs, groups them by production requirements, and generates optimised schedules. A separate preflight agent validates file specifications automatically, flagging non-conforming files before they enter the queue. This removes the reprint cost of errors caught downstream. In a POD context, this typically means a full job restart. This use case applies most directly to POD companies with in-house production capability. For pure fulfilment-only businesses, the equivalent value is captured at the print on demand workflow automation layer: supplier routing and real-time job status coordination, rather than internal scheduling. ## Reporting and business intelligence automation [#](#reporting-and-business-intelligence-automation) **How it works today.** Most POD SMBs compile business performance reports manually, pulling data from their e-commerce platform, print MIS, supplier portals, and accounting software into a spreadsheet, typically on a weekly or monthly cycle. By the time a decision is made based on that report, the underlying data has changed. The [Parseur 2025 survey](https://www.prnewswire.com/news-releases/survey-manual-data-entry-costs-american-companies-more-than-28-000-per-employee-each-year-302516867.html) found this type of manual data consolidation consumes over nine hours per week per employee; time that sits entirely outside any revenue-generating activity. **The agentic AI solution.** A reporting agent connects to data sources across the business, runs queries on demand, and generates structured reports in natural language, accessible to operations leads without a data analyst in the loop. For businesses with complex historical customer and order data, a Text-to-SQL agent provides instant access to years of operational records through a conversational interface. We built this architecture for a manufacturing client with 20+ years of customer interaction data. The resulting system handles thousands of queries per day and delivers answers that previously required a manual database lookup and a specialist to interpret the results. The architecture translates directly to POD businesses managing complex supplier relationships, multi-channel order histories, and customer reorder patterns. Read the case study: [From single agent to hybrid agent-graph architecture](/case-study/from-single-agent-to-hybrid-agent-graph-architecture-our-journey-with-pydantic-ai-and-text-to-sql/) ## Where to start: selecting the highest-value use case first [#](#where-to-start-selecting-the-highest-value-use-case-first) Not all five use cases carry equal priority for every POD SMB at every stage of growth. The right entry point depends on where manual overhead is most costly today, not on which use case is the most technically sophisticated. For businesses with high new-user volume and complex product configurations, guided order configuration delivers the fastest visible ROI. Mixam's 11.76% order increase on day one is a direct proof point, and the 95.4% workflow success rate demonstrates that accuracy and conversion can improve simultaneously. For businesses with high support ticket volume and small support teams, inquiry automation unlocks capacity without headcount growth. The cost of a single support agent's time, compared to the cost of deploying and maintaining an AI agent, rarely favours the manual approach at scale. For businesses managing multi-channel order intake across multiple suppliers and fulfilment partners, print on demand workflow automation at the order management layer reduces error costs and coordination overhead most directly. The financial impact is immediate and measurable: fewer reprints, faster job throughput, and less time spent on manual cross-channel reconciliation. The common error we observe is starting with the most impressive use case rather than the one with the clearest current cost. The correct sequence is: map the workflow, quantify the manual cost, identify the use case with the highest ROI at the lowest implementation risk, build, measure, and expand from there. Our [TriStorm methodology](/tristorm/) is structured exactly around this sequence. Discovery before engineering. Proof of value before scale. Every Vstorm engagement begins with a structured process mapping phase that identifies which automation opportunity delivers the highest operational leverage, before any code is written. ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer [Author profile](/author/konrad-budek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![AI agents for patient scheduling: what works, what does not, and what to avoid](/app/uploads/2026/05/Group-1777-7.png) ### AI agents for patient scheduling: what works, what does not, and what to avoid Read article ](/agentic-ai/ai-agents-for-patient-scheduling-what-works-what-does-not-and-what-to-avoid/)[ ![What is patient intake automation?](/app/uploads/2026/04/Group-1777-7.png) ### What is patient intake automation? Read article ](/agentic-ai/what-is-patient-intake-automation/)[ ![Sovereign AI in Europe: 5 EU-hosted platforms (2026)](/app/uploads/2026/04/Group-1777-6.png) ### Sovereign AI in Europe: 5 EU-hosted platforms (2026) Read article ](/agentic-ai/ai-platforms/sovereign-ai-platforms-europe/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## AI proof of concept vs production-grade agent: key differences is design and intent URL: https://vstorm.co/agentic-ai/ai-proof-of-concept-vs-production-grade-agent-key-differences-is-design-and-intent A proof of concept shows an agent can do the work. A production agent has to keep doing it. The design and intent differences between the two. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/AI proof of concept vs production-grade agent: key differences is design and intent [Agentic AI](/ai-blog-news/#agentic-ai) # AI proof of concept vs production-grade agent: key differences is design and intent A proof of concept shows an agent can do the work. A production agent has to keep doing it. The design and intent differences between the two. ![Wojciech Achtelik](/app/uploads/2025/09/1741016754159-Photoroom.webp) [Wojciech Achtelik](/author/wojciech-achtelik/) PhD(c), AI Tech Lead May 6, 2026 11 min read ![AI proof of concept vs production-grade agent: key differences is design and intent](/app/uploads/2026/05/Group-1777-8.png) On this page0% 1. [Evaluations: measuring quality before it becomes a problem](#evaluations-measuring-quality-before-it-becomes-a-problem) 2. [Guardrails: building security into the architecture, not bolting it on afterwards](#guardrails-building-security-into-the-architecture-not-bolti) 3. [Observability: from infrastructure monitoring to operational intelligence](#observability-from-infrastructure-monitoring-to-operational-) 4. [PoC and production have different goals](#poc-and-production-have-different-goals) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. 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Share [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-proof-of-concept-vs-production-grade-agent-key-differences-is-design-and-intent%2F "LinkedIn")[](https://x.com/intent/tweet?text=AI%20proof%20of%20concept%20vs%20production-grade%20agent%3A%20key%20differences%20is%20design%20and%20intent&url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-proof-of-concept-vs-production-grade-agent-key-differences-is-design-and-intent%2F "X")[](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-proof-of-concept-vs-production-grade-agent-key-differences-is-design-and-intent%2F "Facebook")[](https://www.reddit.com/submit?url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-proof-of-concept-vs-production-grade-agent-key-differences-is-design-and-intent%2F&title=AI%20proof%20of%20concept%20vs%20production-grade%20agent%3A%20key%20differences%20is%20design%20and%20intent "Reddit")[](mailto:?subject=AI%20proof%20of%20concept%20vs%20production-grade%20agent%3A%20key%20differences%20is%20design%20and%20intent&body=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-proof-of-concept-vs-production-grade-agent-key-differences-is-design-and-intent%2F "Email") Link copied Summarize with AI [GPT](https://chat.openai.com/?prompt=Summarize%20the%20following%20page%3A%20AI%20proof%20of%20concept%20vs%20production-grade%20agent%3A%20key%20differences%20is%20design%20and%20intent%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-proof-of-concept-vs-production-grade-agent-key-differences-is-design-and-intent%2F\))[Claude](https://claude.ai/new "Opens Claude and copies the summarize prompt — paste with ⌘V / Ctrl+V")[Perplexity](https://www.perplexity.ai/?q=Summarize%20the%20following%20page%3A%20AI%20proof%20of%20concept%20vs%20production-grade%20agent%3A%20key%20differences%20is%20design%20and%20intent%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-proof-of-concept-vs-production-grade-agent-key-differences-is-design-and-intent%2F\))[Grok](https://grok.com/?q=Summarize%20the%20following%20page%3A%20AI%20proof%20of%20concept%20vs%20production-grade%20agent%3A%20key%20differences%20is%20design%20and%20intent%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fai-proof-of-concept-vs-production-grade-agent-key-differences-is-design-and-intent%2F\)) Proofs of concept are useful. They help a team validate whether an AI agent can understand a workflow, call the right tools, and create a business outcome that is worth further investment. A good PoC can be built quickly, shown to stakeholders, and used to clarify what should be automated first. But a PoC is not a production system. This distinction matters because AI agents behave differently from traditional software. A normal application usually fails in predictable ways: an API returns an error, a validation rule blocks a form, a database query times out. But an AI agent can fail more subtly. It may choose the wrong tool, misread the user's intent, produce a confident but incorrect answer, ignore a business constraint, or be manipulated by a prompt injection hidden inside user-provided content. In a PoC, these issues are often acceptable because the system is being tested internally. In production, they become significant business risks. At Vstorm, we see the same pattern across many AI agent projects. The first version proves the concept, but the production-grade agent proves that the company can trust it. The difference comes down to three key areas of distinction: * Evaluations * Guardrails * Observability This article discusses the gap between an AI proof of concept and a production-grade agent in depth. ## Evaluations: measuring quality before it becomes a problem [#](#evaluations-measuring-quality-before-it-becomes-a-problem) Most PoCs are validated through informal testing, a few internal users, a handful of edge cases, a prompt adjustment after a bad response. That is enough to answer whether an idea is worth pursuing, but it produces no measurable baseline and no way to detect regression as the system evolves. Production-grade agents require structured evaluation datasets that make quality observable and every change to a prompt, model, or retrieval system testable against a consistent standard. ### A PoC proves that something can work once A PoC is usually designed for speed with the team wanting to answer a practical question: can an AI agent handle this workflow well enough to justify a larger build? This is the right approach. A PoC should not take six months. It should be narrow, concrete, and focused on learning. For example: * A support agent answers questions from internal documentation * A sales agent qualifies inbound leads and drafts follow-up emails * A healthcare admin agent helps schedule appointments * A finance agent summarizes invoices or flags unusual transactions * A research agent gathers information from several approved sources In each case, the first version can look impressive. The agent responds naturally, it saves time, it handles happy-path scenarios. Stakeholders can easily see the potential. The problem is that many PoCs are evaluated informally. Someone from the internal team asks twenty questions. A product manager tries five edge cases. The engineering team changes the prompt after seeing a bad response. And gradually, the demo improves and everyone agrees the agent is promising. This process is useful for discovery, but it is not enough for production readiness. ### Production starts with evaluation datasets The biggest difference between a PoC and a production-grade AI agent is that production systems need measurable quality. You cannot improve what you do not measure. You also cannot safely change prompts, models, retrieval logic, or tools if you do not know whether the agent has become better or worse at its task. This is where evaluation datasets become critical. An evaluation dataset is a curated set of test cases that represents the real work the agent must handle. It should include normal requests, difficult requests, known failure cases, ambiguous messages, edge cases, security-sensitive examples, and examples where the correct answer depends on tool usage rather than model creativity. For an AI customer support agent, for example, the dataset might include: * Common product questions * Refund and cancellation requests * Angry or frustrated customers * Questions outside the company's support scope * Cases where the agent must ask a clarifying question * Cases where the agent must escalate to a human * Attempts to extract confidential internal instructions This dataset becomes the agent's quality baseline and every meaningful change should be tested against it. In our projects, we treat evals as part of the engineering workflow, not as a final QA activity. When the prompt changes, evals run. When the model changes, evals run. When retrieval changes, evals run. When a new tool is added, evals run. When a production failure is discovered, that failure is converted into a new test case so the same issue does not quietly return later. This is also why we use [Logfire Evals](https://pydantic.dev/docs/logfire/evaluate/evals/) as a best practice in Vstorm AI agent projects. Logfire supports datasets and experiments for AI systems, making it possible to compare evaluation runs over time, inspect traces, and turn production observations into test cases. That creates a practical improvement loop: allowing us to observe real behavior, curate important cases, run evaluations, compare results, improve the agent, and repeat. Without this loop, teams are left with subjective confidence. The agent "feels better" after a prompt update. The demo "looked good." A few internal testers "did not find anything serious." But these sorts of feelings and impressions are not enough when the agent is about to interact with real customers, real employees, or real business data. ## Guardrails: building security into the architecture, not bolting it on afterwards [#](#guardrails-building-security-into-the-architecture-not-bolti) A PoC tested by internal users carries a natural layer of protection that disappears the moment the agent is exposed to real users. An agent that reads natural language, retrieves documents, and calls tools becomes part of the security surface; one that can be targeted through prompt injection, permission exploitation, or manipulation hidden inside uploaded content. Guardrails define what the agent is permitted to do and must be designed into the architecture from the outset, not added once risks become visible. ### PoCs are usually protected by limited exposure Most PoCs are tested by internal team members. That gives the system a hidden layer of safety: the users are friendly. Internal testers usually do not try to attack the agent. They do not paste malicious instructions into documents. They do not attempt to override system prompts. They do not ask the agent to reveal API keys, internal policies, hidden reasoning, private customer records, or confidential data from another account. Real users are different. Once an AI agent is exposed publicly, or even broadly inside a company, it becomes part of the security surface. The agent reads natural language, it may retrieve documents, it may call tools, it may take actions. That combination is powerful, but it also creates new vulnerabilities. Prompt injection is the most common example. A malicious user can try to convince the agent to ignore its original instructions. A hostile web page or uploaded document can contain hidden instructions telling the agent to leak data or call the wrong tool, make the agent reveal system prompts, bypass permissions, or perform actions outside the intended workflow. A PoC often does not include proper guardrails because the risk is not yet visible. Teams typically test whether the agent can complete the task, not whether it can resist manipulation. That changes in production. ### Production agents need guardrails by design Guardrails are not a cosmetic safety layer added at the end of the project. For production-grade AI agents, guardrails are part of the architecture. They define what the agent is allowed to do, what it is not allowed to do, when it must ask for confirmation, when it must escalate, and which data each tool can access. Effective guardrails usually operate at several levels: * **Input guardrails** detect malicious, irrelevant, or unsafe user requests before they reach the core agent logic. * **Tool guardrails** enforce permissions, parameter validation, approval steps, and least-privilege access. * **Output guardrails** check whether the response contains sensitive data, unsupported claims, unsafe advice, or content outside the agent's permitted scope. The key point is that the model should not be the only component responsible for staying safe, i.e. a production-grade agent should not rely on a prompt that says "do not reveal confidential information" and hope the model follows it every time. Security should be enforced outside the model as well. For example, if a customer support agent is allowed to check order status, the order lookup tool should verify the user's identity and return only that user's order data. The agent should not receive access to the full orders database and be trusted to choose correctly. If an HR agent is empowered to answer policy questions, it should not automatically have access to employee salary records. If an agent can send emails, high-impact messages should require confirmation or human approval. Good guardrails reduce the blast radius of model mistakes and potential liability. They also make the system easier to audit, because the company can see not only what the agent said, but what it was permitted to access and why. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Observability: from infrastructure monitoring to operational intelligence [#](#observability-from-infrastructure-monitoring-to-operational-) Knowing that a service is running and responding within acceptable latency is the starting point, not the finish line, for AI agent observability. Production systems also need to answer whether the agent chose the right tool, followed business policy, and resolved the user's intent; questions that require capturing tool calls, retrieval results, escalation events, and sentiment signals at scale. Without this layer, teams are left managing individual conversation failures manually rather than detecting patterns and improving the system continuously. ### Production observability is more than logs Traditional observability answers questions such as: * Is the service running? * How much latency do we have? * Are API calls failing? * Which errors are increasing? * What is the infrastructure cost? AI agent observability has to answer those questions, but also much more difficult ones: * Did it choose the right tool? * Did it use the right context? * Did it follow business policy? * Did the user leave satisfied or frustrated? * Did it escalate at the right moment? * Which topics are causing most failures? * Which user segments experience the worst outcomes? This is why production-grade AI agents require more complex observability pipelines than PoCs. In a PoC, a team may read chat transcripts manually. In production, that does not scale. Companies need automated systems that continuously analyze conversations, detect failures, measure sentiment, cluster recurring topics, and surface the issues that matter most. A mature observability pipeline should capture: * User message and agent response, with appropriate privacy controls * Tool calls, tool inputs, tool outputs, and tool errors * Retrieval results and source documents used by the agent * Model, prompt, and agent version * Latency and cost per conversation * Escalation events and handoff reasons * User sentiment and satisfaction signals * Failure classifications such as hallucination, refusal error, tool error, policy violation, or unresolved intent * Topic clusters showing what users are actually asking about This allows teams to move from anecdotal feedback to operational intelligence. Instead of saying "some users complain that the agent is wrong," the company can see that 18% of failed conversations are related to refund policy, most of those failures happen after the agent retrieves an outdated help article, and user sentiment drops sharply when the agent asks the same clarification question twice. That is the level of visibility needed to improve production-grade agents. ### The production feedback loop The best production AI agent systems create a continuous feedback loop: 1. The agent handles real conversations. 2. Observability pipelines detect failures, user frustration, unusual topics, and risky behavior. 3. Important cases are reviewed and added to evaluation datasets. 4. The team improves prompts, tools, retrieval, guardrails, or model selection. 5. The updated agent is tested against the evaluation dataset. 6. Only changes that improve or preserve key metrics are released. 7. Production monitoring confirms whether the improvement works in real usage. This loop is what separates a promising PoC from a reliable system. It also changes how teams think about AI agent development. The goal is not to write one perfect prompt. The goal is to build an operating system around the agent: evaluation, tracing, monitoring, feedback, governance, and making continuous improvements. ### What usually breaks when companies skip this step The failure mode is rarely dramatic on day one. More often, the agent works well enough to get approved, then performance slowly becomes harder to trust. Maybe a model update changes behavior. A prompt edit improves one workflow but breaks another. A new data source introduces conflicting information. Users discover questions that were never tested internally. The agent starts giving different answers to similar requests. Then the business team loses confidence. Engineers begin debugging individual conversations manually. And nobody knows whether the system is improving or drifting. This is the cost of moving from PoC to production without production discipline. Common symptoms include: * No clear baseline for agent quality * No regression testing before prompt or model changes * No dataset of real edge cases * No systematic prompt injection testing * No tool-level permission model * No visibility into tool selection mistakes * No automatic detection of unresolved conversations * No sentiment analysis or topic clustering * No reliable way to explain why the agent failed * No process for turning failures into future test cases At that point, the agent may still look functional from the outside, but the team cannot operate it confidently. ## PoC and production have different goals [#](#poc-and-production-have-different-goals) A PoC should answer: **is this worth building?** A production-grade agent must answer: **can this be trusted repeatedly, at scale, with real users and real business consequences?** That second question requires a different engineering standard. Production-grade AI agents need **evaluation** datasets because quality must be measurable. They need **guardrails** because friendly internal testing does not represent real-world exposure. They need **observability** pipelines because failures must be detected, classified, and turned into improvements automatically. The companies that understand this distinction move faster in the long run. They do not treat the PoC as disposable, but they also do not pretend it is production-ready just because the demo looked good. At Vstorm, our best practice is to design the production path early: build the PoC quickly, then harden it with evals, guardrails, and observability before it becomes part of the business workflow. Tools like Logfire Evals help make that process measurable, repeatable, and visible to both engineering and business stakeholders, more on that [can be read here](/agentic-ai/one-dashboard-full-visibility-why-we-use-logfire-in-our-stack/). AI agents can create significant operational leverage. But their true value can be untilized only when the system is reliable enough to fully trust. That is the real difference between a PoC and a production-grade AI agent. ![Wojciech Achtelik](/app/uploads/2025/09/1741016754159-Photoroom.webp) [Wojciech Achtelik](/author/wojciech-achtelik/) PhD(c), AI Tech Lead [Author profile](/author/wojciech-achtelik/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![What makes a decision-maker ready for AI adoption?](/app/uploads/2026/04/2024-01-20__Y4A0426-scaled.webp) ### What makes a decision-maker ready for AI adoption? Read article ](/agentic-ai/what-makes-a-decision-maker-ready-for-ai-adoption/)[ ![What is HIPAA-compliant AI?](/app/uploads/2026/05/Group-1777-9.png) ### What is HIPAA-compliant AI? Read article ](/agentic-ai/what-is-hipaa-compliant-ai/)[ ![AI agents for patient scheduling: what works, what does not, and what to avoid](/app/uploads/2026/05/Group-1777-7.png) ### AI agents for patient scheduling: what works, what does not, and what to avoid Read article ](/agentic-ai/ai-agents-for-patient-scheduling-what-works-what-does-not-and-what-to-avoid/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## An engineering voice at PRINTING United Expo in Las Vegas URL: https://vstorm.co/agentic-ai/an-engineering-voice-at-printing-united-expo What printers at PRINTING United Expo asked about AI, and what surprised them most: the need for engineering, unexpected outcomes, pace and no magic. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/An engineering voice at PRINTING United Expo in Las Vegas [Agentic AI](/ai-blog-news/#agentic-ai) # An engineering voice at PRINTING United Expo in Las Vegas What printers at PRINTING United Expo asked about AI, and what surprised them most: the need for engineering, unexpected outcomes, pace and no magic. ![Bartosz Adam Gonczarek](https://vstorm.co/app/uploads/2025/02/2024-01-20__Y4A0464_small-1-e1738697918189.jpg) [Bartosz Adam Gonczarek](/author/bartosz-adam-gonczarek/) Chief Transformation Officer and Co-founder of Vstorm October 13, 2026 5 min read ![An engineering voice at PRINTING United Expo in Las Vegas](/app/uploads/2026/10/cover-an-engineering-voice-at-printing-united-expo.jpg) On this page0% 1. [Where are printing companies in terms of AI adoption?](#where-are-printing-companies-in-terms-of-ai-adoption) 2. [Know before doing](#know-before-doing) 3. [What AI-stuff surprised printers the most?](#what-ai-stuff-surprised-printers-the-most) 4. [Where it left us](#where-it-left-us) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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As with previous conferences, what we brought to all the panel discussions I participated in turned out to be of value for participants: a down-to-earth perspective on what value and challenges to expect from AI deployments. ## Where are printing companies in terms of AI adoption? [#](#where-are-printing-companies-in-terms-of-ai-adoption) What surprised me is that the printers at the Expo were insight-hungry and so far had very little experience with agentic AI. Their high-paced, margin-shrinking businesses are severely challenged by market forces, yet the opportunity to automate customer-facing order processing or knowledge management inside their companies with AI is mostly untapped. What printers have focused on is job scheduling and fulfillment, where innovative ML solutions are being offered as part of existing dedicated software packages. Other opportunities to optimize operations for increased margins remain generally unexplored. The interest in those was the driving force bringing the audience to our PRINTING AI Pavilion. ## Know before doing [#](#know-before-doing) Vstorm participated as the official development partner of PRINTING AI, which offers consulting services to printing companies. These come in the form of a readiness assessment and defining the scope and milestones of an AI transformation. PRINTING AI is led by Amy Servi-Bonner, who acted as a host and interviewer, and boy, she does know what questions to ask engineers. Our intellectual dues on the Expo stage were paid by providing an opportunity to hear the unspoken truths about AI deployment, look under the hood of it and, by that, cut through the marketing fluff surrounding AI in online media. ![Bartosz Gonczarek at the Vstorm and PRINTING AI pavilion stage at PRINTING United Expo, booth N6600, with the panel chairs set up behind him](/app/uploads/2026/10/printing-united-expo-vstorm-pavilion.jpg) Did we succeed? I think so, judging by my experience of being pulled aside by people on the Expo floor who shared with me that it was an eye-opening experience for them. The only complaint I've heard was that the scale of our pavilion was too small, and the message should be broadcast more widely. Another good indicator of success was that companies that are yet to begin their AI transformation lined up to secure initial advisory services from PRINTING AI, to plan their steps first, before doing anything AI-related. ![A full audience of printing professionals seated in front of the PRINTING AI stage during a panel discussion at PRINTING United Expo](/app/uploads/2026/10/printing-united-expo-panel-audience.jpg) [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## What AI-stuff surprised printers the most? [#](#what-ai-stuff-surprised-printers-the-most) Here are a few tidbits that the audience found most surprising (and, to a degree, most revealing): ### The need for engineering in AI is typically underappreciated It is so because their software vendors provide them with "AI enhancements" that are positioned as a panacea to their challenges, until they realize they are not. But that only comes with the negative experience of trying to use the AI enhancements of their existing systems and failing. Most printing companies are not yet at that stage. The need to put engineers on the job to apply AI the right way only arises later on, after initial tinkering and investment falls short of delivering on expectation. This is where most of our clients find us. Better, in our opinion, to skip the failure stage and onboard qualified engineers from the start. ### The unforeseen outcomes of adopting agentic AI With or without AI, each system deployment is measured against preestablished assumptions to check if goals were accomplished. With AI, however, the outcomes can be genuinely surprising. For example, in an AI-supported web-to-print ordering process, our goal was to remove friction, which we measured in decreased time from product selection to print order. But the real gain was seen in fewer abandoned orders and increased quantity and conversion. In support automation, where agentic AI was to take over 70% of simpler inquiries, it actually outperformed that assumption, taking 94% of simple queries to resolution without the need for human intervention. The measure of its success was conversations ending with: "I don't care if you're AI, tell your humans you did good." Each project completed by Vstorm had its own interesting moments where positive results caught customers by surprise, revealing unexpected but beneficial side effects. ### AI is moving fast... too fast for many For AI engineers, the way systems were developed in 2025 is already obsolete. The field moves fast due to the evolution of models, surrounding frameworks, and standards being used in the process. This means that the systems created at the end of 2026 can be simpler, faster, and more capable than the elaborate and complex systems from the past year. Where a few agents were needed to accomplish a task, one can do. Larger contexts can be used. Better tools can help agents navigate the data sources. Transparency of agentic AI operations increases, too. What this means for printing companies is that starting late doesn't mean being left behind, as long as they approach AI adoption the right (modern) way. ### No magic, actually Each opportunity to meet early adopters, or those that don't adopt AI just yet, is an opportunity for Vstorm to take the position to dispel the myths. We, as engineers, fight magical thinking, and there's a lot of it around (especially in Vegas, where the conference took place this year). What I offered (with appreciation from the audience) was a call to sober up and think of AI as a tool, not a sentient entity or superintelligence. It is a powerful tool today and should be seen as such. One that completes the toolbox of methods at hand that may improve a printing business. AI is not shipped as a standalone component (ever); it is actually shipped as a combination of earlier, more deterministic 'tools' that work with AI, which is probabilistic in nature. But the results of combining the two, although they may look magical, are not magic: they can be measured, bounded and controlled. Therefore, for each company adopting agentic AI, if it pulls a magic trick, our job is to make sure that owners understand what happens behind the scenes, and control it, too. ## Where it left us [#](#where-it-left-us) After observing how our sober hints and project-based examples landed, we are seriously considering opening a YouTube channel. What holds us back, however, is that the algorithms will prefer the flashy and catchy vids on how superintelligence will steal your print job away, instead of sober, experience-grounded talk. Luckily, conferences such as PRINTING United Expo in Las Vegas let our message bypass the algorithm and reach participants directly. What I'm really proud of is how well it was received and appreciated by those who spent time listening to our panel discussions. ![Bartosz Gonczarek with a guest on the Expo floor next to the PRINTING AI pavilion at PRINTING United Expo in Las Vegas](/app/uploads/2026/10/printing-united-expo-printing-ai-pavilion.jpg) For printing-related consulting services, please contact Amy Servi-Bonner from PRINTING AI at aservi@printing.org. For AI engineering, please [book a session with Vstorm](https://vstorm.co/schedule-a-meeting/). ![Bartosz Adam Gonczarek](https://vstorm.co/app/uploads/2025/02/2024-01-20__Y4A0464_small-1-e1738697918189.jpg) [Bartosz Adam Gonczarek](/author/bartosz-adam-gonczarek/) Chief Transformation Officer and Co-founder of Vstorm [Author profile](/author/bartosz-adam-gonczarek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Agentic AI governance in regulated industries: what actually binds you](/app/uploads/2026/10/cover-agentic-ai-governance-in-regulated-industries-v3.png) ### Agentic AI governance in regulated industries: what actually binds you Read article ](/agentic-ai/agentic-ai-governance-in-regulated-industries/)[ ![AI agent evaluation and reliability](/app/uploads/2026/10/cover-ai-agent-evaluation-and-reliability-v3.png) ### AI agent evaluation and reliability Read article ](/agentic-ai/ai-agent-evaluation-and-reliability/)[ ![What agentic AI is still taken for: three lessons from Stockholm](/app/uploads/2026/10/cover-agentic-ai-misconceptions-three-lessons-from-stockholm-v3.png) ### What agentic AI is still taken for: three lessons from Stockholm Read article ](/agentic-ai/agentic-ai-misconceptions-three-lessons-from-stockholm/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Beyond ChatGPT: Why POD Sellers Need AI Agents, Not Just Assistants URL: https://vstorm.co/agentic-ai/beyond-chatgpt-why-pod-sellers-need-ai-agents-not-just-assistants Generic assistants have no live catalogue, cannot validate an order against business rules, and hallucinate on specialist detail. What agents add. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Beyond ChatGPT: Why POD Sellers Need AI Agents, Not Just Assistants [Agentic AI](/ai-blog-news/#agentic-ai) # Beyond ChatGPT: Why POD Sellers Need AI Agents, Not Just Assistants Generic assistants have no live catalogue, cannot validate an order against business rules, and hallucinate on specialist detail. What agents add. ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer March 10, 2026 Updated June 15, 2026 8 min read ![Beyond ChatGPT: Why POD Sellers Need AI Agents, Not Just Assistants](/app/uploads/2026/03/Group-1777-6.png) On this page0% 1. [How POD companies handle sales conversations today](#how-pod-companies-handle-sales-conversations-today) 2. [What ChatGPT assistants can and cannot do in sales flow](#what-chatgpt-assistants-can-and-cannot-do-in-sales-flow) 3. [What separates an AI agent from a ChatGPT assistant](#what-separates-an-ai-agent-from-a-chatgpt-assistant) 4. [Where ChatGPT stalls in print on demand: the four structural gaps](#where-chatgpt-stalls-in-print-on-demand-the-four-structural-) 5. [How AI agents convert customers: the Mixam case](#how-ai-agents-convert-customers-the-mixam-case) 6. [Choosing the right tool for your sales flow](#choosing-the-right-tool-for-your-sales-flow) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. Share [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fbeyond-chatgpt-why-pod-sellers-need-ai-agents-not-just-assistants%2F "LinkedIn")[](https://x.com/intent/tweet?text=Beyond%20ChatGPT%3A%20Why%20POD%20Sellers%20Need%20AI%20Agents%2C%20Not%20Just%20Assistants&url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fbeyond-chatgpt-why-pod-sellers-need-ai-agents-not-just-assistants%2F "X")[](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fbeyond-chatgpt-why-pod-sellers-need-ai-agents-not-just-assistants%2F "Facebook")[](https://www.reddit.com/submit?url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fbeyond-chatgpt-why-pod-sellers-need-ai-agents-not-just-assistants%2F&title=Beyond%20ChatGPT%3A%20Why%20POD%20Sellers%20Need%20AI%20Agents%2C%20Not%20Just%20Assistants "Reddit")[](mailto:?subject=Beyond%20ChatGPT%3A%20Why%20POD%20Sellers%20Need%20AI%20Agents%2C%20Not%20Just%20Assistants&body=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fbeyond-chatgpt-why-pod-sellers-need-ai-agents-not-just-assistants%2F "Email") Link copied Summarize with AI [GPT](https://chat.openai.com/?prompt=Summarize%20the%20following%20page%3A%20Beyond%20ChatGPT%3A%20Why%20POD%20Sellers%20Need%20AI%20Agents%2C%20Not%20Just%20Assistants%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fbeyond-chatgpt-why-pod-sellers-need-ai-agents-not-just-assistants%2F\))[Claude](https://claude.ai/new "Opens Claude and copies the summarize prompt — paste with ⌘V / Ctrl+V")[Perplexity](https://www.perplexity.ai/?q=Summarize%20the%20following%20page%3A%20Beyond%20ChatGPT%3A%20Why%20POD%20Sellers%20Need%20AI%20Agents%2C%20Not%20Just%20Assistants%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fbeyond-chatgpt-why-pod-sellers-need-ai-agents-not-just-assistants%2F\))[Grok](https://grok.com/?q=Summarize%20the%20following%20page%3A%20Beyond%20ChatGPT%3A%20Why%20POD%20Sellers%20Need%20AI%20Agents%2C%20Not%20Just%20Assistants%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fbeyond-chatgpt-why-pod-sellers-need-ai-agents-not-just-assistants%2F\)) Generic ChatGPT assistants help customers explore ideas but consistently fall short at the point of sale in print on demand. They lack access to live product catalogues, cannot validate order specifications against business rules, and carry a meaningful hallucination risk in specialist domains, all of which are critical when a customer is configuring a technically complex print job. Purpose-built AI agents close each of these gaps through live data integration, constrained generation, and multi-step reasoning. The Vstorm-built agent for Mixam demonstrates the practical difference: a 95.4% workflow success rate, a 62.11% conversion rate on agent-generated quotes, and an 11.76% increase in orders recorded on day one of launch. ## **How POD companies handle sales conversations today** [#](#how-pod-companies-handle-sales-conversations-today) Most print on demand platforms rely on a combination of static FAQ pages, email support queues, and human customer service staff to guide customers through their first order. The problem is structural: customers arrive knowing what they want to produce but lacking the technical vocabulary to achieve it. Paper weight, binding type, CMYK colour profiles, bleed margins, and trim sizes are not terms most first-time buyers understand. At Mixam, a UK-based self-publishing and print fulfilment platform, [70% of new users required significant guidance](/case-study/ai-agent-for-order-recommendation-and-completion/) before completing their first order. That is not just a marginal support burden, it is a direct constraint on conversion and scale. The traditional response to this problem is to grow the support team alongside order volume. That approach has a clear ceiling: staff cost, availability, and training time do not scale linearly with customer demand. Therefore, automating the guidance layer became a commercial priority, not just a technological experiment. When looking to automate this choke-point across the industry, ChatGPT-style assistants are the first tool many PoD operators consider. This article examines whether they solved the problem, or only partially addressed it. ## **What ChatGPT assistants can and cannot do in sales flow** [#](#what-chatgpt-assistants-can-and-cannot-do-in-sales-flow) A fair assessment starts with what generic ChatGPT assistants do well. They understand natural language, including vague, non-technical requests, and can translate them into coherent responses. For generating product descriptions, marketing copy, or responses to FAQ-style queries backed by a manually maintained knowledge base, they are capable and cost-effective tools. The limitations become structural when the use case shifts from content generation to sales conversion. ### **Disconnected from live business data** A ChatGPT assistant has no native access to live inventory, live pricing, or real-time product specifications. It is completely disconnected from your business data, it cannot access your helpdesk, internal knowledge base, or order management system, all essential components to complete customer service. In a print on demand context, that means the assistant cannot tell a customer whether a given paper stock is currently available, what the lead time is, or whether a chosen specification is compatible with the customer's delivery location. ### **Hallucination risk in specialist domains** [OpenAI itself advises against using ChatGPT](https://www.fiverr.com/resources/guides/programming-tech/chatgpt-limits) for high-stakes tasks, noting that the model tends to hallucinate, generating confident-sounding but incorrect outputs. In a print on demand sales flow, a hallucinated paper size or an invented binding option does not produce a wrong answer in a chat thread, it produces a failed or incorrectly specified order downstream. ### **Weak conversion performance in commerce contexts** A 12-month study by researchers at the University of Hamburg and Frankfurt School of Finance and Management found that [ChatGPT converts less effectively than nearly all traditional e-commerce channels](https://www.emarketer.com/content/chatgpt-minimal-influence-on-ecommerce-sales-for-now). Awareness and exploration are where generic AI assistants add value. Conversion is where they do not, at least without significant custom engineering. ## **What separates an AI agent from a ChatGPT assistant** [#](#what-separates-an-ai-agent-from-a-chatgpt-assistant) The distinction is not primarily about the underlying language model. Both AI agents and ChatGPT assistants can use the same foundation models. The difference is in what surrounds it. A ChatGPT assistant is a language interface, it receives input, generates a response, and stops. A purpose-built AI agent is a language interface connected to a decision-making and action layer. In a print on demand sales flow, the action layer is what closes the order. The table below maps the functional gaps across the dimensions that matter in a POD conversion context. **Capability** **ChatGPT assistant** **Purpose-built AI agent** Live product data access No Yes — via tool integrations Order specification validation No Yes — constrained generation Multi-step reasoning Limited — session context only Yes — iterative plan and act cycles Memory across conversation Session only Persistent, configurable Scope control / guardrails Prompt-dependent, not enforced Engineered into architecture System integrations None by default CRM, OMS, catalogue, pricing Hallucination risk on specialist data High without live data access Mitigated by validation layer Readers who want a technical primer on how agentic AI systems are structured can refer to the [Vstorm AI Glossary](/glossary/), which defines these terms in plain language. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## **Where ChatGPT stalls in print on demand: the four structural gaps** [#](#where-chatgpt-stalls-in-print-on-demand-the-four-structural-) In the specific context of print on demand sales flows, the gaps outlined above become concrete and costly. ### **Gap 1: Product specification accuracy** A print on demand order is not a simple transaction. A customer configuring a book may make 12 to 15 sequential decisions, such as trim size, page count, paper stock, cover finish, binding type, quantity, and delivery destination. Each decision informs the next. A generic assistant without engineered state management and live catalogue access will lose coherence across that configuration sequence. Worse, it may suggest a specification that does not exist in the operator's current product range, sending a customer to checkout with an order the system cannot process. ### **Gap 2: Hallucination in a business-critical context** Products with incomplete or stale data produce failures at every stage of the agentic commerce chain, they do not appear in recommendations, they generate incorrect quotes, and they create abandoned orders at checkout ( [MetaRouter](https://www.metarouter.io/post/agentic-commerce-in-2025-what-we-learned)). Generic assistants without validated product data compound this: they fill knowledge gaps with plausible-sounding answers. In a printing context, a convincing but wrong answer about bleed margins carries a direct operational cost. ### **Gap 3: No order completion loop** A ChatGPT assistant can discuss the steps required to place an order, but it cannot take a validated specification, generate a quote, and submit it to an order management system. That handoff reintroduces manual steps and friction that the assistant was supposed to eliminate. It can talk about doing these things, but it cannot perform the actions that actually close the loop. ### **Gap 4: Inability to maintain complex multi-step context** A POD customer configuring a complex product may generate dozens of conversational turns before reaching a completed specification. A generic assistant without persistent working memory will drift, repeating questions, losing earlier selections, or contradicting choices the customer has already made. An agent with engineered state management does not face this problem because it tracks the evolving specification as a structured object, not as a conversational context window. ## **How AI agents convert customers: the Mixam case** [#](#how-ai-agents-convert-customers-the-mixam-case) We at Vstorm built a multi-agent order advisor for [Mixam](https://mixam.com/), a self-publishing and print fulfilment platform operating across the United Kingdom, the United States, Canada, Australia, and Germany. The core challenge was the same described above: 70% of new users needed significant guidance before placing a first order, and Mixam's product range is technically complex enough that generic conversational AI could not reliably meet the need. The architecture we delivered comprised three specialist agents working in sequence, with access to 15 distinct tools including a RAG vector store connected to Mixam's live product catalogue. Constrained generation and validation processes eliminated hallucinations. Guardrails were engineered into the system to ensure the agents remained focused solely on helping customers navigate Mixam's printing offer, refusing to engage with out-of-scope queries. The results at a glance: * **11.76%** increase in orders on day one of launch * **62.11%** of all quotes generated by the agent were paid and confirmed * **95.4%** workflow success rate, exceeding the client's own target of 80% * Conversion improved from approximately 20% to approximately 40% overall * 10,000 users per day, 100,000 custom orders per month processed through the agent The 95.4% figure is worth examining specifically. A ChatGPT assistant without validation architecture would have no mechanism to prevent a malformed specification from reaching the order management system. The engineered validation layer is what produces that number, not the language model capability alone. > "My wish was to come to at least an 80% success rate in the workflow results, and by the time we finished the project, the success rate is, I believe, over 95.4%, so it definitely exceeded expectations. But at the same time, we did listen thoroughly to all the advice we got from Vstorm and I think that made a big impact." — [Lucian Puca](/case-study/ai-agent-for-order-recommendation-and-completion/), Digital Product Manager and Automation and Workflow Lead, Mixam. For Lucian's own account of what made this implementation succeed, including the decision to partner with a specialist rather than attempt an in-house build, you can check out his [top 5 tips on launching the Agentic AI transformation.](/ai/top-5-tips-from-lucian-puca-of-mixam-on-launching-agentic-ai-transformation/) ## **Choosing the right tool for your sales flow** [#](#choosing-the-right-tool-for-your-sales-flow) Research from Rep AI found that shoppers complete purchases [47% faster](https://www.hellorep.ai/blog/the-future-of-ai-in-ecommerce-40-statistics-on-conversational-ai-agents-for-2025) when assisted by AI, but only when the AI has real-time integration and validated product knowledge. Without those two elements, a conversational interface adds interaction without adding conversion. The practical question is not whether to use AI in your sales flow, it is which type of AI is needed at each stage. ChatGPT assistants are well-suited to tasks where a wrong answer does not break a transaction, like in simple FAQ handling with a manually maintained knowledge base or early-stage customer education. They are inexpensive to deploy and require no engineering beyond prompt configuration. Purpose-built AI agents are appropriate for any customer-facing flow that requires product validation, multi-step configuration, live data access, or completion of a transaction. In print on demand, that describes the core conversion journey from the moment a customer specifies what they want to produce. The starting point for any POD operator evaluating this decision is to identify which step in the current sales flow produces the most drop-off. If that step requires a validated, integrated output, such as a confirmed quote, a compatible specification, or a real-time availability check, a ChatGPT assistant will not be equipped to resolve it. But a special tailored AI agent will. Our team at Vstorm uses the [TriStorm methodology](/tristorm/) framework to move our clients from use case identification to transformational workflow deployment, you can read more about our offer on our [print on demand industry page](/agentic-ai-in-print-on-demand/). #### **Wish to learn how agentic AI can transform your business workflows?** ![Konrad Budek](https://vstorm.co/app/uploads/2025/11/1667729533976.jpeg) [Konrad Budek](/author/konrad-budek/) Full-stack content marketer with a journalism background | AI-augmented marketer [Author profile](/author/konrad-budek/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![Why Agentic AI needs standards and best practices](/app/uploads/2026/03/Group-1777-7.png) ### Why Agentic AI needs standards and best practices Read article ](/agentic-ai/why-agentic-ai-needs-standards-and-best-practices/)[ ![Dynamic Tool Discovery: A Systematic Evaluation of 8 Search Strategies for AI Agents](/app/uploads/2026/03/Group-1777-4.png) ### Dynamic Tool Discovery: A Systematic Evaluation of 8 Search Strategies for AI Agents Read article ](/agentic-ai/dynamic-tool-discovery-a-systematic-evaluation-of-8-search-strategies-for-ai-agents/)[ ![Vstorm at Py AI: agentic AI in production with OpenAI, SurrealDB and Theory Ventures](/app/uploads/2026/03/Group-1777-3.png) ### Vstorm at Py AI: agentic AI in production with OpenAI, SurrealDB and Theory Ventures Read article ](/agentic-ai/vstorm-at-py-ai-agentic-ai-in-production-with-openai-surrealdb-and-theory-ventures/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Boutique AI consulting firms vs Large Consultancies: Pricing and Service Comparison 2026 URL: https://vstorm.co/agentic-ai/boutique-ai-consulting-firms-vs-large-consultancies-pricing-and-service-comparison Boutique agentic AI consultancies against large firms: capabilities, limits, pricing bands and which engagement each model is built to carry. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Boutique AI consulting firms vs Large Consultancies: Pricing and Service Comparison 2026 [Agentic AI](/ai-blog-news/#agentic-ai) # Boutique AI consulting firms vs Large Consultancies: Pricing and Service Comparison 2026 Boutique agentic AI consultancies against large firms: capabilities, limits, pricing bands and which engagement each model is built to carry. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst February 26, 2026 12 min read ![Boutique AI consulting firms vs Large Consultancies: Pricing and Service Comparison 2026](/app/uploads/2026/03/Group-1777-1-1.png) On this page0% 1. [Why your business needs tailored Agentic AI](#why-your-business-needs-tailored-agentic-ai) 2. [How business size and structure influences ROI](#how-business-size-and-structure-influences-roi) 3. [Boutique AI consulting firms excel in deep specialization and execution speed](#boutique-ai-consulting-firms-excel-in-deep-specialization-an) 4. [Large consultancy firms provide scale, brand trust, and regulatory sophistication](#large-consultancy-firms-provide-scale-brand-trust-and-regula) 5. [How to select the best agentic AI consultation partner for your business needs](#how-to-select-the-best-agentic-ai-consultation-partner-for-y) 6. [Summary of strategic recommendations for agentic AI success](#summary-of-strategic-recommendations-for-agentic-ai-success) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. Share [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fboutique-ai-consulting-firms-vs-large-consultancies-pricing-and-service-comparison%2F "LinkedIn")[](https://x.com/intent/tweet?text=Boutique%20AI%20consulting%20firms%20vs%20Large%20Consultancies%3A%20Pricing%20and%20Service%20Comparison%202026&url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fboutique-ai-consulting-firms-vs-large-consultancies-pricing-and-service-comparison%2F "X")[](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fboutique-ai-consulting-firms-vs-large-consultancies-pricing-and-service-comparison%2F "Facebook")[](https://www.reddit.com/submit?url=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fboutique-ai-consulting-firms-vs-large-consultancies-pricing-and-service-comparison%2F&title=Boutique%20AI%20consulting%20firms%20vs%20Large%20Consultancies%3A%20Pricing%20and%20Service%20Comparison%202026 "Reddit")[](mailto:?subject=Boutique%20AI%20consulting%20firms%20vs%20Large%20Consultancies%3A%20Pricing%20and%20Service%20Comparison%202026&body=https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fboutique-ai-consulting-firms-vs-large-consultancies-pricing-and-service-comparison%2F "Email") Link copied Summarize with AI [GPT](https://chat.openai.com/?prompt=Summarize%20the%20following%20page%3A%20Boutique%20AI%20consulting%20firms%20vs%20Large%20Consultancies%3A%20Pricing%20and%20Service%20Comparison%202026%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fboutique-ai-consulting-firms-vs-large-consultancies-pricing-and-service-comparison%2F\))[Claude](https://claude.ai/new "Opens Claude and copies the summarize prompt — paste with ⌘V / Ctrl+V")[Perplexity](https://www.perplexity.ai/?q=Summarize%20the%20following%20page%3A%20Boutique%20AI%20consulting%20firms%20vs%20Large%20Consultancies%3A%20Pricing%20and%20Service%20Comparison%202026%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fboutique-ai-consulting-firms-vs-large-consultancies-pricing-and-service-comparison%2F\))[Grok](https://grok.com/?q=Summarize%20the%20following%20page%3A%20Boutique%20AI%20consulting%20firms%20vs%20Large%20Consultancies%3A%20Pricing%20and%20Service%20Comparison%202026%20\(https%3A%2F%2Fvstorm.co%2Fagentic-ai%2Fboutique-ai-consulting-firms-vs-large-consultancies-pricing-and-service-comparison%2F\)) ## TL;DR * Boutique firms suit focused, tailored agentic AI projects, with typical timelines of 8 to 12 weeks to a first production agent and costs the article puts 30 to 40% lower, citing AIConsultingLab. * Large consultancies suit enterprise-wide, multi-geography transformations, compliance across many jurisdictions and round-the-clock support, with engagements that run from hundreds of thousands to millions of dollars. * The comparison table puts typical boutique projects at $50K to $500K with measurable ROI in 3 to 6 months, against $500K to $10M+ and 9 to 18 months for large firms. * Large enterprises get the best results from a hybrid model that pairs established providers for core transformation with boutique specialists for the technically complex agent components. _Within you will find a side-by-side comparison of the capabilities and limitations of boutique agentic AI consulting firms vs large enterprise consultancies so you can choose the best cooperation partner for your business success in 2026._ The choice between boutique and enterprise AI consulting firms for the execution of agentic AI projects essentially boils down to a question of specialization depth versus transformation scale. Boutique firms deliver faster implementations at 30-40% ( [AIConsultingLab](https://theaiconsultinglab.com/the-rise-of-ai-driven-boutique-consulting-firms/)) lower cost backed by senior-led technical expertise, while enterprise firms provide the scale and resources required for a comprehensive global reach, governance rigor, and the capacity for multi-year, organization-wide transformations. The agentic AI consulting market, valued at $5.25-7.55 billion in 2025, is projected to reach $93-199 billion by 2032-2034, making the choice to invest in agentic AI transformation ever more important. The boutique-versus-enterprise decision comes down to five key factors: * **Project scope and scale:** to determine baseline feasibility, enterprise-wide transformation across multiple geographies requires enterprise firm capacity, while focused and tailored implementations should lean on boutique agility and technical depth. * **Budget constraints:** pricing models create hard boundaries, with the 30-40% cost advantage making boutique firms the only viable option for SMBs and mid-market competitors. * **Speed requirements:** boutique firms tend to operate on an average 8-12 week timeline versus enterprise engagements who often measuring implementation across full quarters. * **Regulatory complexity:** companies from specific industries may require dedicated governance practices and demonstrated compliance credentials, where enterprise consultancies excell. * **Technical innovation requirements:** boutique specialists maintain closer connections to academic frontiers and demonstrate greater mastery of emerging approaches, applying leading expert knowledge to client cases. This while, according to [Deloitte predictions](https://www.deloitte.com/us/en/about/press-room/deloitte-technology-media-telecom-2025-predictions.html), up to 50% of enterprises using GenAI are forecast to deploy AI Agents by the end of 2026. But how do you choose the right AI consultation firm to best fit your business needs? > I do think of it as a workforce. This is a workforce that will conduct end-to-end processes, replacing many tasks being performed today by the human workforce. – Jorge Amar, McKinsey Senior Partner , June 3 2025, on The future of work is agentic Below you will find a direct comparison of the capabilities, costs and limitations of boutique agentic AI consultation firms vs larger enterprise providers. ## Why your business needs tailored Agentic AI [#](#why-your-business-needs-tailored-agentic-ai) The numbers are quite clear. With over 80% of attempted AI implementations failing outright, 87% of AI related projects never reaching production ( [MIT Sloan Review](https://sloanreview.mit.edu/article/achieving-return-on-ai-projects/)), and 42% of companies choosing to cut their losses and abandon AI initiatives before delivery ( [Fortune](https://fortune.com/2025/08/21/an-mit-report-that-95-of-ai-pilots-fail-spooked-investors-but-the-reason-why-those-pilots-failed-is-what-should-make-the-c-suite-anxious/)), there is a significant gap in common visions of AI’s potential applications and the technical reality which governs application. ### The GenAI Divide * **80% of AI Projects Fail:** AI projects fail at twice the rate of failure for information technology projects that do not involve AI. * **42% of Companies abandon their AI initiatives:** With organizations reporting that 46% of projects on average are abandoned between proof of concept and broad adoption. * **95% of Generative AI pilot programs are failing:** New MIT study finds that 95% of Generative AI pilots fail to deliver ROI, reflecting the limitations of one-size-fits-all approaches that lack deep business integration. But where is this wide gap between vision and outcomes originating from? Taking a deeper look at the numbers provided by [MIT](https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf), we can see that top AI consulting firms dramatically outperform general AI solution providers in live implementations, achieving an industry average of 67% successful implementation rates compared to the estimated 22% success rate overall. The most common reasons for project failure and abandonment include problem misalignment, insufficient data quality, technology-first approaches over solving user’s problems, poor integration with existing processes, and inadequate human oversight in development processes. Well publicized failures like the McDonald's AI drive-thru shutdown, IBM Watson Health's $4 billion discontinuation, and Zillow's $500+ million in losses show the full extent of potential misalignment of vision and technology. The best solution to overcome these pitfalls is to embed a dedicated Agentic AI squad that engineers and deploys production-grade agents for your core workflows right from the start. Vstorm leverages practiced and proven tactics to narrow the gap and achieve meaningful results. Our strategy begins with two locked blueprints. First comes the business blueprint, which ranks high-value use cases; and then follows the technical blueprint, which tests each potential use-case for feasibility, measuring complexity, data, integration, and compliance needs alongside setting realistic timelines, while determining required tools and necessary up-skilling. This blueprint allows us to build tailored AI agents that seamlessly integrate with your existing workflows, data, and software stack. ## How business size and structure influences ROI [#](#how-business-size-and-structure-influences-roi) When defining needs and choosing a provider, one must consider the requirements of your company above all. Boutique agentic AI and large enterprise consulting firms provide fundamentally different value propositions, with the overall price of implementation being a deciding factor for many businesses. Boutique AI consulting firms offer what large consultancies structurally cannot: deep technical specialization, agile delivery, and direct access to senior expertise throughout client engagement. These advantages become particularly important in agentic AI projects, where cutting-edge multi-agent architectures and rapid iteration cycles actually favor smaller, more agile organizations. Large AI consulting firms, meanwhile, offer a value proposition centered on large scale organizational transformation capacity, brand credibility, and comprehensive service portfolios. Globally, companies like Accenture employ approximately 801,000, Deloitte 470,000, PwC 370,000, and EY 400,000 people\*\*,\*\* representing large talent pools that enable simultaneous large-scale deployments across the full operational scope of enterprise clients. Large consulting firms also gain an advantage through their substantial investment capacity. Consultancy providers such as McKinsey, BCG, and the Big Four have collectively invested billions of dollars in AI labs and proprietary platforms. While Deloitte's Zora AI platform, PwC's Agent OS, Accenture's AI Refinery, and EY's EY.ai Agentic Platform represent substantial R&D investments that boutique firms simply cannot match. These platforms provide pre-built agent components, governance frameworks, and enterprise integration patterns that help accelerate deployment while ensuring compliance. And yet speed-to-value still represents what may be the most compelling boutique advantage, where typical implementation timelines run 8-12 weeks from inception to production deployment, as compared to the quarters or longer required for enterprise engagements. This surprising speed arises from streamlined decision-making, fewer bureaucratic approval layers, deep specialization, and organizational structures optimized for execution over process compliance. And for organizations seeking to escape the endless cycle of AI proofs-of-concept that never reach production, boutique specialists have the expertise to offer proven paths forward. Meanwhile, the premium pricing demanded by large consultancies places their services well beyond the reach of many SMBs and midmarket organizations, as major engagements commonly run from hundreds of thousands to millions of dollars. This cost-prohibition effectively excludes small and medium businesses from enterprise consulting entirely. With boutique firms operating on lower overhead structures and lean administration, they are in a position to provide savings directly to client budgets without sacrificing technical quality. For small and medium businesses exploring agentic automation, the question of pricing is often the final word in whether Agentic AI implementation is within reach. ### Boutique AI consulting firms versus Large Enterprise consultancies Attention Allocation: * Boutique: Senior consultants execute projects directly rather than delegating to junior staff—a critical distinction when implementing complex autonomous agent systems. * Enterprise: Partner-level attention concentration on flagship Fortune 500 accounts, while mid-market clients receive junior MBA staffing and standardized delivery. Pricing: * Boutique: Lower overhead structures and lean administration provide savings which translate directly to client budgets without sacrificing technical quality. * Enterprise: Premium pricing places services beyond reach for many organizations—major engagements commonly run from hundreds of thousands to millions of dollars. Speed of Implementation: * Boutique: Typical implementation timelines run 8-12 weeks from inception to production deployment. * Enterprise: Complex organizational structures with multiple approval layers slow implementation timelines and make pivoting difficult when requirements evolve or new technologies emerge. Agentic AI engineering companies like Vstorm exemplify this distinction. Our [agentic AI consulting services](/agentic-ai-consulting/) are priced for mid-market budgets and designed to turn cash positive within months, while you keep complete ownership of your code and data with zero lock-in contracts. Having gone over the unique approaches of these providers lets take a closer look into what sort of use case each is best suited to meet. Even among a choice of top AI consulting companies, no company is one size fits all, and there is no need to pay top dollar for a full scale enterprise transformation when special tailored agentic solutions are required. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## Boutique AI consulting firms excel in deep specialization and execution speed [#](#boutique-ai-consulting-firms-excel-in-deep-specialization-an) Specialized boutique consultation firms focus on custom AI strategy development, proprietary algorithm creation, and comprehensive business transformation to dramatically scale operations. These firms, such as Vstorm, employ PhD-level data scientists, specialized agentic AI engineers, and domain specific experts who develop bespoke solutions using advanced architectures like multi-agent systems, RAG pipelines, on-premise or cloud-based, and open-source LLM deployments. A few of the top benefits of partnering with boutique consultancy firms include: * Custom model development with domain-specific training data * Advanced RAG systems with vector databases * Multi-modal integration combining text, image, and structured data * Context-adaptive systems engineered to retain feedback and evolve with organizational workflows through continuous refinement—the #1 feature demanded by 66% of executives * Workflow-embedded solutions starting at high-value pain points before scaling to core processes, avoiding the 95% failure rate of generic implementations * Complex back-office integration across multiple legacy and native systems (that off-the-shelf tools cannot handle), delivering measurable financial returns ## Large consultancy firms provide scale, brand trust, and regulatory sophistication [#](#large-consultancy-firms-provide-scale-brand-trust-and-regula) Large enterprise consulting firms excel in engagement types that leverage their structural advantages of scale, global reach, and comprehensive capabilities. Enterprise-wide AI transformations requiring the embedding of autonomous agents across multiple business units simultaneously, including global multi-geography deployments dependent on coordinated implementation across countries and regulatory environments, demand the enterprise infrastructure and resource depth only large firms can provide. Large enterprise consultancies often achieve best results through: * Global multi-geography deployments requiring coordinated implementation across countries and regulatory environments * At scale enterprise deployments requiring HIPAA compliance and clinical governance in healthcare, or SOC 2 and PCI DSS compliance * Enterprise-wide AI transformations embedding autonomous agents across multiple business units simultaneously * Subscription-based pricing with AI features included in licensing tiers * Cooperation in which brand trust provides a deciding factor, providing significant value for risk-averse enterprises ## How to select the best agentic AI consultation partner for your business needs [#](#how-to-select-the-best-agentic-ai-consultation-partner-for-y) Quantitative analysis of 1,000+ enterprise implementations, provided by MIT, reveals dramatic performance disparities between provider types. The MIT research shows specialized vendor partnerships succeed 67% of the time, while internal builds and general AI approaches succeed only 33% as often (setting final success rates at around 22%). The research further points out that the industry is facing significant systemic challenges with 95% of generative AI pilots failing to produce any impact on operations, while McKinsey data claims that nearly 80% of companies have deployed GenAI but report no material impact on earnings. And while these trends paint a fairly clear picture, the choice between specialized boutique [agentic AI providers](/agentic-ai/deep-dive-top-10-ai-agent-development-firms-for-business-automation/) and large-scale AI enterprise integration should align with organizational needs, AI maturity, and complexity requirements. Organizations seeking to utilize the full potential of AI and gain revolutionary outcomes from complex internal systems should partner with specialized boutique consultancies, while global scale enterprise clients should employ enterprize partners for comprehensive transformation initiatives. Below we present a breakdown of the top aspects you should consider when choosing your provider. ### Choose boutique agentic AI consultancies when: * Complex AI transformations require custom solutions to link multiple internal data systems and domains * Cutting-edge requirements demand latest AI research and practiced solutions * AI is intended to be a core competitive differentiator rather than a simple operational enhancement * Highly regulated industries require custom governance frameworks to meet compliance requirements * Innovation focus prioritizes breakthrough capabilities to dramatically increase operational scale ### Choose large enterprise consultancies when: * Organizations are heavily invested in specific enterprise platforms requiring seamless integration * Risk mitigation favors supported solutions over integrated and owned approaches * When agentic AI systems must comply simultaneously with GDPR, HIPAA, financial services regulations, and local data sovereignty requirements across dozens of jurisdictions * Integration scope is on a global scale requiring coordinated implementation * Twenty-four hour support coverage across time zones is required for mission-critical agentic systems Boutique AI consulting firms deliver superior outcomes in providing cutting-edge agentic AI development requiring advanced RAG implementations, novel multi-agent orchestration patterns, and integration of emerging research benefits from specialists who maintain closer connections to academic frontiers than enterprise generalists. When the technical challenge demands innovation over methodology, the willingness of expert boutique firms to apply new and rigorously tested approaches creates a competitive advantage. Model ownership arises as an added level of complexity. Even within big enterprise agreements, client companies retain only their own inputs and outputs while model weights remain platform vendor property. Making fine-tuned models usable only while using the vendor's service, as no on-premises deployment option or client owned solutions exist. For SMB and mid-market AI transformation, boutique firms represent the only financially viable option in many cases. Affordable pricing enables small and medium businesses with limited budgets to access meaningful agentic automation capabilities. Organizations seeking collaborative partnerships where they remain in control of the process and gain ownership over provided solutions, rather than receiving packaged solutions leased from distant consultancies, find boutique engagement models better matched with their operating philosophy and business needs. ROI Timelines Boutique Consulting Firms Large Enterprise Consultation Typical Project Cost $50K-$500K $500K-$10M+ Time to First Production Agent 8-12 weeks 16-26 weeks Time to Measurable ROI 3-6 months 9-18 months Typical Break-Even Point 6-12 months 12-24 months Full Implementation Payback 12-24 months 24-48 months An observable trend on the market also suggests that the hybrid approach seems to often deliver desirable outcomes, with large companies often engaging boutique specialists for technically complex agentic AI components while leveraging enterprise consultancies for broader organizational transformation, governance frameworks, and global coordination. This approach captures the advantages of boutique technical excellence within the risk management structures of large enterprise firms. In fact, tinkering with various solutions in low risk, low impact settings can also provide companies with the internal knowledge required to properly leverage more advanced and lucrative AI transformations, as identified by Lucian Puca, Digital Product Manager and Automation and Workflow Lead of Mixam, in his [top 5 tips for launching the Agentic AI transformation](/ai/top-5-tips-from-lucian-puca-of-mixam-on-launching-agentic-ai-transformation/). ## Summary of strategic recommendations for agentic AI success [#](#summary-of-strategic-recommendations-for-agentic-ai-success) Success in AI implementation requires strategic vendor selection hand-in-hand with ongoing organizational transformation. Based on the analysis of 1,000+ case studies, organizations achieve the best results by following tested patterns regardless of their choice of provider. Universal success factors include: * Start with specific, high-value use cases demonstrating clear ROI rather than broad AI initiatives * Invest heavily in data quality and governance frameworks before model development * Implement gradual scaling with continuous validation rather than big-bang deployments * Maintain human-AI collaboration instead of pursuing full automation * Focus on business outcomes and user problems over technical sophistication But to get the best returns for your AI investment, the following strategies concerning providers should be considered based on your business’ market segment: * **Startups** should prioritize boutique specialists with direct startup experience, emphasizing technology transfer and internal capability building over ongoing dependencies. Budget-conscious approaches of $15,000-$50,000 to launch pilot projects enable fast iteration cycles with flexible engagement models. * **Mid-market companies and SMBs** with around $50M-$500M revenue achieve best results by partnering with specialist engineering companies like Vstorm, who provide end-to-end support, from strategy to deployment, and client owned solutions. The focus should be on firms with 10-100 employees who offer specialized expertise without the bureaucratic overhead, where projects range from $25,000-$250,000 and have clear ROI expectations. * **Large enterprises** with $500+ million revenue are best served engaging in hybrid models, combining Tier 1 platform providers with specialized boutique consultancies to optimize outcomes. Partner with established providers like IBM, Accenture, and Deloitte for core AI transformation, while engaging specialized boutiques, like Vstorm, in innovation projects for breakthrough applications. Vstorm is uniquely positioned to support the dynamic transformation of internal workflows for both SMBs and enterprise level businesses, allowing companies to dramatically scale operations and achieve new growth by utilizing internal data and streamlining processes with sophisticated AI agents precisely tailored to business needs at low cost, with no vender lock in. The AI consulting market's rapid annual growth and expanding sophistication create unprecedented opportunities for organizations that navigate their provider selection strategically, balancing specialized expertise with implementation pragmatism to join the successful minority achieving transformational AI value. ![Nicholas Berryman](https://vstorm.co/app/uploads/2026/03/Authorship.jpg) [Nicholas Berryman](/author/nicholas-berryman/) Content Lead and Market Analyst [Author profile](/author/nicholas-berryman/) ## See more from Vstorm in Google Search Add Vstorm as a preferred source, and Google shows our new articles more often in Top Stories. [Add on Google](https://www.google.com/preferences/source?q=vstorm.co) ## Keep reading [ ![What do we mean by AI automation, actually?](/app/uploads/2026/02/pexels-gdtography-277628-911738-1-6-768x513.png) ### What do we mean by AI automation, actually? Read article ](/agentic-ai/what-do-we-mean-by-ai-automation-actually/)[ ![Tech limitations in the print-on-demand industry](/app/uploads/2026/02/pexels-gdtography-277628-911738-1-5-768x513.png) ### Tech limitations in the print-on-demand industry Read article ](/agentic-ai/tech-limitations-in-the-print-on-demand-industry/)[ ![Custom Agentic AI vs Off-the-Shelf AI Platforms: Pricing and Service Comparison 2026](/app/uploads/2026/04/Group-1777-1-3.png) ### Custom Agentic AI vs Off-the-Shelf AI Platforms: Pricing and Service Comparison 2026 Read article ](/agentic-ai/custom-agentic-ai-vs-off-the-shelf-ai-platforms-pricing-and-service-comparison/) Work with us ## Ready to put agentic AI to work? Book a 30-minute session with the engineers who would do the work. We map one real workflow worth automating. [Book a session](/schedule-a-meeting/)[See case studies](/case-studies/) --- ## Building Production-Grade AI Agents: How We Brought Deep Agent Patterns to Pydantic URL: https://vstorm.co/agentic-ai/building-production-grade-ai-agents-how-we-brought-deep-agent-patterns-to-pydantic Vstorm delivered pydantic-deep - a comprehensive framework for building "deep agents" that can operate in isolated environments. [Home](/)/[Blog](/ai-blog-news/)/[Agentic AI](/ai-blog-news/#agentic-ai)/Building Production-Grade AI Agents: How We Brought Deep Agent Patterns to Pydantic [Agentic AI](/ai-blog-news/#agentic-ai) # Building Production-Grade AI Agents: How We Brought Deep Agent Patterns to Pydantic Vstorm delivered pydantic-deep - a comprehensive framework for building "deep agents" that can operate in isolated environments. ![Kacper Włodarczyk](/app/uploads/2025/11/1714077310001-Photoroom.webp) [Kacper Włodarczyk](/author/kacper-wlodarczyk/) Agentic AI/Python Engineer December 17, 2025 Updated July 31, 2026 7 min read ![Building Production-Grade AI Agents: How We Brought Deep Agent Patterns to Pydantic](/app/uploads/2025/12/pexels-gdtography-277628-911738-1.webp) On this page0% 1. [The problem: why simple agents fall short](#the-problem-why-simple-agents-fall-short) 2. [What are Deep Agents?](#what-are-deep-agents) 3. [Why we built pydantic-deep](#why-we-built-pydantic-deep) 4. [pydantic-deep: Deep Agents for the Pydantic ecosystem](#pydantic-deep-deep-agents-for-the-pydantic-ecosystem) 5. [pydantic-deep vs LangChain deepagents](#pydantic-deep-vs-langchain-deepagents) 6. [Real-World application: the full demo](#real-world-application-the-full-demo) 7. [Getting started](#getting-started) 8. [Key Take-aways](#key-take-aways) AI Newsletter Twice-monthly notes on agents in production. No spam. Work email Send me the newsletter. Unsubscribe anytime. [Privacy](/privacy-policy/) You're subscribed. 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At Vstorm, we had already been building similar patterns for our clients, and we recognized an opportunity to bring these proven architectures into the Pydantic ecosystem. The result is [**pydantic-deep**](https://github.com/vstorm-co/pydantic-deep) – a comprehensive framework for building "deep agents" that can plan, operate on files, delegate tasks, and execute code in isolated environments. Built on top of [pydantic-ai](https://github.com/pydantic/pydantic-ai), it provides the same capabilities as LangChain's [deepagents](https://github.com/langchain-ai/deepagents), but with the type safety, simplicity, and developer experience that Pydantic users expect. ## The problem: why simple agents fall short [#](#the-problem-why-simple-agents-fall-short) Anyone who has deployed an AI agent to production knows the pattern. The demo works beautifully. The proof of concept impresses stakeholders. Then reality hits. Real-world tasks are not single-step operations. When a user asks an agent to "analyze this CSV file and create a visualization," the agent needs to: 1. **Plan** the approach and break down the task 2. **Read** the file from storage 3. **Write** analysis code 4. **Execute** the code in a safe environment 5. **Handle errors** and retry if something fails 6. **Track progress** so users know what is happening Simple agents with a handful of tools cannot handle this complexity reliably. They lose track of multi-step tasks, cannot recover from errors gracefully, and provide no visibility into their reasoning process. Production agents need architecture patterns that address these challenges systematically. ## What are Deep Agents? [#](#what-are-deep-agents) Deep agents represent a maturation of AI agent design. The term, popularized by LangChain's research into production systems, describes agents with specific architectural capabilities: * **Planning and Progress Tracking** – Deep agents break complex tasks into steps and track their progress. Users can see what the agent is working on, what it has completed, and what remains. * **File System Operations** – Real work requires reading, writing, and editing files. Deep agents treat the file system as a first-class citizen, with proper abstraction layers that work across in-memory storage, real file systems, and sandboxed containers. * **Task Delegation** – Some tasks benefit from specialized sub-agents. A coding agent might delegate documentation writing to a specialized sub-agent with different instructions and capabilities. * **Sandboxed Execution** – Running code that an AI generates is inherently risky. Deep agents execute code in isolated environments, typically Docker containers, preventing accidents from affecting the host system. * **Context Management** – Long conversations exceed token limits. Deep agents automatically summarize older context while preserving essential information, enabling sessions that span hours or days. * **Human-in-the-Loop** – Certain operations require human approval before execution. Deep agents support approval workflows for dangerous operations like code execution or file deletion. These patterns emerged from teams building production agents and discovering what actually works at scale. ## Why we built pydantic-deep [#](#why-we-built-pydantic-deep) When we evaluated existing solutions for our client projects, we found a gap. LangChain's deepagents provides excellent deep agent capabilities, but it is built on LangGraph, which is a graph-based state machine that adds significant complexity. For teams already invested in the Pydantic ecosystem, switching frameworks was not attractive. We wanted deep agent capabilities with: * **Type safety** throughout the entire codebase * **Async-first design** for modern Python applications * **Pydantic models** for structured inputs and outputs * **Simpler mental models** than graph-based state machines * **100% test coverage** for production confidence The answer was to build on [pydantic-ai](https://github.com/pydantic/pydantic-ai), Pydantic's official AI framework. By extending pydantic-ai with deep agent patterns, we could deliver production-grade capabilities while maintaining the developer experience Pydantic users love. ## pydantic-deep: Deep Agents for the Pydantic ecosystem [#](#pydantic-deep-deep-agents-for-the-pydantic-ecosystem) [pydantic-deep](https://github.com/vstorm-co/pydantic-deep) provides everything needed to build sophisticated AI agents: ### Planning with todo lists Agents track their work through a todo system that makes reasoning visible: from pydantic\_deep import create\_deep\_agent, DeepAgentDeps, StateBackend agent = create\_deep\_agent( model="openai:gpt-4.1", instructions="You are a helpful coding assistant", include\_todo=True, include\_filesystem=True, ) deps = DeepAgentDeps(backend=StateBackend()) result = await agent.run( "Create a Python script that analyzes sales data", deps=deps ) When the agent receives this task, it creates a todo list breaking down the work: "Read and understand the data," "Write analysis script," "Execute and verify results." Users see real-time progress as each step moves from pending to in-progress to completed. ### Flexible backend architecture All file operations flow through a backend abstraction. This design enables: * **StateBackend** for testing with in-memory storage * **FilesystemBackend** for real file system operations * **DockerSandbox** for isolated execution environments * **CompositeBackend** for routing operations to different backends by path The same agent code works unchanged across all backends. Writes tests against StateBackend, develops locally with FilesystemBackend, deploys to production with DockerSandbox. ### Sub-Agent delegation Complex tasks benefit from specialization. So you can configure sub-agents that the main agent can delegate to: The main agent recognizes when a task matches a sub-agent's specialty and delegates appropriately. Sub-agents receive isolated context, they cannot see the parent's todo list or spawn their own sub-agents, preventing recursive delegation issues. ### Skills system Anthropic's research on [equipping agents for the real world with agent skills](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills) demonstrates how skills dramatically improve agent performance on complex tasks. pydantic-deep implements this pattern with skills as reusable instruction sets stored as markdown files with YAML frontmatter. When an agent encounters a task matching a skill's domain, it loads the relevant instructions. The data analysis skill, for example, provides templates for loading data with pandas, handling missing values, creating visualizations, and formatting reports. The agent loads these instructions on-demand, getting domain expertise exactly when needed without bloating the base prompt. ### Sandboxed code execution Executing AI-generated code requires isolation. pydantic-deep's DockerSandbox runs code in containers with: * Pre-configured runtime environments (Python data science, web development, Node.js) * Automatic container lifecycle management * Session isolation for multi-user applications * Idle timeout and cleanup The SessionManager handles container orchestration for production deployments, creating isolated sandboxes per user and cleaning up idle sessions automatically. ### Human-in-the-Loop approval Some operations should not proceed without human confirmation. So you have the power to configure which tools require approval: When the agent attempts to execute code, it pauses and requests approval. The calling application presents the command to the user, who can approve, modify, or reject it. Only after approval does execution proceed. ### Context management Long conversations exceed model token limits. The SummarizationProcessor automatically compresses older context while preserving essential information. You can configure triggers based on token count, message count, or context fraction, and specify how much recent context to preserve. [![](/ebooks/3d/the-llm-book.webp)](/the-llm-book/) Free guide The LLM Book AI basics Work email How did you find us? How did you find us? GoogleChatGPTPerplexityClaudeGeminiLinkedInAdsClutchOpen sourceFrom a friendOther Get the guide I agree that Vstorm may process my data to send this guide, per the [Privacy Policy](/privacy-policy/) and [GDPR note](/gdpr-compliance-note/). Your guide is ready [Download the PDF](/ebooks/the-llm-book.pdf) ## pydantic-deep vs LangChain deepagents [#](#pydantic-deep-vs-langchain-deepagents) Both libraries implement the same deep agent patterns, but with different architectural philosophies: Aspect pydantic-deep [LangChain deepagents](https://github.com/langchain-ai/deepagents) Foundation [pydantic-ai](https://github.com/pydantic/pydantic-ai) LangGraph Architecture Toolsets and dependencies Middleware stack with hooks Type Safety Pyright strict mode Standard Python typing Skills System Built-in Not included Docker Integration Native DockerSandbox Via SandboxBackend Session Management SessionManager LangGraph checkpointing Summarization Configurable triggers Auto-configured ## Real-World application: the full demo [#](#real-world-application-the-full-demo) To demonstrate pydantic-deep's capabilities in a production-like environment, we built a [full example application](https://github.com/vstorm-co/pydantic-deep/tree/main/examples/full_app) that showcases every feature working together. You can watch the [demo video here](https://drive.google.com/file/d/1hqgXkbAgUrsKOWpfWdF48cqaxRht-8od/view?usp=sharing) to see it in action. ### What the demo includes * **Multi-User Session Management** – Each user receives an isolated Docker container. Sessions persist across page refreshes and clean up automatically after idle timeout. * **WebSocket Streaming** – Real-time streaming of agent responses, including text generation, thinking content (for reasoning models), tool calls, and tool results. * **File Upload and Processing** – Users upload CSV, PDF, or text files. The agent accesses these files in its sandbox and can analyze, transform, or reference them. * **Custom Tools** – Mock GitHub tools demonstrate how to extend pydantic-deep with domain-specific capabilities. The pattern works identically for real API integrations. * **Human-in-the-Loop** – Code execution requires user approval. The frontend displays the proposed command and waits for confirmation before proceeding. * **Skills in Action** – A data analysis skill provides the agent with pandas expertise, visualization templates, and best practices for working with CSV data. * **Sub-Agent Delegation** – A joke generator sub-agent demonstrates task delegation. When users ask for humor, the main agent delegates to the specialized sub-agent. * **Todo Progress Tracking** – The frontend displays the agent's todo list in real-time, showing users exactly what the agent is working on. ### Architecture highlights The application demonstrates several production patterns: * **Stateless Agent, Stateful Sessions** – The agent itself is stateless and shared across all users. Per-user state lives in session objects that hold the Docker sandbox, message history, and todo list. * **Backend Injection at Runtime** – The agent is configured without a backend. Each session provides its own DockerSandbox, enabling per-user isolation without creating multiple agent instances. * **Approval Flow** – When the agent calls a tool requiring approval, it returns a DeferredToolRequests object. The application presents this to the user, collects their decision, and resumes the agent with DeferredToolResults. ## Getting started [#](#getting-started) pydantic-deep is available on [PyPI](https://pypi.org/project/pydantic-deep/): pip install pydantic-deep For Docker sandbox support: pip install pydantic-deep\[sandbox\] The [documentation](https://github.com/vstorm-co/pydantic-deepagents) covers installation, configuration, and advanced usage patterns. The [GitHub repository](https://github.com/vstorm-co/pydantic-deep) includes the full example application and comprehensive test suite. ## Key Take-aways [#](#key-take-aways) Deep agents represent the current state of the art in production AI systems. The patterns; planning, file operations, task delegation, sandboxed execution, context management, and human oversight; emerged from teams solving real problems at scale. With pydantic-deep, these patterns are now available in the Pydantic ecosystem. Whether you are building a coding assistant, data analysis tool, or any AI application that needs to interact with the world, pydantic-deep provides a solid, type-safe foundation. The framework reflects Vstorm's experience building production AI systems for clients across industries. We have seen what works and what fails, and we have encoded those lessons into a library that handles the hard parts so you can focus on your application's unique value.