Shaping agentic AI
Agentic AI Foundation
Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.
Read the announcementAI agent development company
Your processes, redesigned for agents
Agents that take the manual work out of one workflow, tested on your data against a target agreed before the build. The code and decision logic are yours by contract.
Mixam: conversion to a final sale from about 20% to about 40%
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~20% → ~40% conversion to a final saleThree-agent product advisor guiding customers through print-order configuration.
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44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmarkHIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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Supply chain intelligence agents reducing manual coordination overhead.
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AI agent implementation for a global automotive enterprise.
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Every inquiry answered in English and Arabic, from ARIJ's own knowledge baseBilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base.
Why agent pilots stall
Most agent pilots stall on the process around them.
We redesign the process, prove it on your data, and you decide go or no-go on measured results.
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Where it stallsThe old workflow gets an agent on top.
What we do We redesign the work first: fixed rules stay in code, agents take the judgment calls, people keep the decisions that matter.
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Where it stallsThe pilot ends with a demo and no decision.
What we do A Proof of Value on your data ends in go or no-go, with measured accuracy and running costs.
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Where it stallsYour logic ends up locked in a vendor's platform.
What we do You own the code, data and decision logic by contract, and the model stays swappable.
How we work
A redesigned process your team owns
One team, from the first workshop to a system your people run.
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Your process, redesigned
We map the process, cut the steps that only move data, keep fixed rules as code and keep people where judgment matters.
- Human checkpoints in the redesign
- Go/no-go Proof of Value
- Accuracy and running costs measured
Mixam's print-order agent lifted conversion to a final sale from about 20% to about 40%. - 0203
Built from parts we already run
Agents where the work needs judgment, code where it must be exact. Our engineers contribute to the open standards agents run on, so your system keeps up as they change.
- Components we already run
- Integrations, guardrails and evaluation suites
- Every agent action monitored
44% for a raw LLM, 98% for the guideline-executing agent, on an open 50-scenario clinical benchmark. - 0303
Sovereign by design
The agent runs in your environment on hosted or open-weight models, and the model stays replaceable.
- Your code, prompts and evaluations
- Runbooks and team training
- No license or platform fee
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 before you hire us.
Open source
We build open-source agent tooling that other engineering teams install
9M+ PyPI downloads
3,900+ GitHub stars
31 public repositories
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.
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.
Choose the architecture
Pick the simplest agent that does the job
We choose the architecture in the first phase, test it on your data and write down why the alternatives lost.
Fits whenOne bounded workflow, clear inputs, a handful of tools.
Watch outIt hits a ceiling when the workflow keeps branching into new cases.
Fits whenToo broad for one agent, splits into specialist steps.
Watch outMore moving parts to test, monitor and pay for on every run.
Fits whenAnswers must come from your documents, with sources shown.
Watch outOnly as good as the documents behind it and the permissions on them.
Fits whenPeople ask in chat, and some of them need a person.
Watch outNeeds a clear handoff and firm limits on what it may promise.
Case studies
AI agents our clients run
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
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
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
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.
Technology
The stack behind our AI agents
Chosen per project, with the model kept replaceable.
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- LlamaIndex
- Milvus
- Qdrant
- pgvector
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- Self-hosted
- Your cloud
FAQ
Questions about AI agent development
What do AI agent development services include?
The full path, from choosing the workflow to handing code and runbooks to your team. In between: the agent architecture, a Proof of Value on your data, the full build with integrations and guardrails, and deployment with monitoring. You can also bring us in for one part of that path, such as an audit of an agent you already run.
Should we build a single agent or a multi-agent system?
Start with one agent when the workflow is bounded and the data sources are manageable, because it is faster to build and easier to audit. A multi-agent system is worth the extra complexity when the work is too broad for one agent or needs several specialists running in parallel. We make that call in the first phase of the engagement, before any build.
How is a custom AI agent different from RPA or an automation platform?
RPA and workflow platforms follow fixed rules and break when the input or the process changes. An AI agent chooses the next step or tool based on the situation, so it can work with unstructured documents and handle exceptions. That flexibility needs evaluation, guardrails and monitoring, and we build them in from the start.
How long does AI agent development take?
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.
What does a Proof of Value include, and what does it cost?
One workflow, a working agent on your real data and edge cases, and an evaluation suite that measures it. Before it starts, your experts and our engineers agree the success threshold. From your side it needs access to the data and systems of that workflow and a named owner. It ends in a go or no-go decision backed by measured accuracy and running costs. Its cost depends on the workflow and the systems it touches, and it keeps your first commitment limited.
How much does AI agent development cost?
Cost depends on the number of workflows, the systems the agent connects to, and your hosting and compliance requirements. Starting with a Proof of Value keeps the first commitment limited and shows the running costs of the agent before the full build.
Do we own the AI agents you build?
Yes. The code, the IP and the decision logic are yours by contract. Code, configuration and runbooks move to your infrastructure, and the system keeps running without us. The model is the one you choose, hosted or open-weight, and it can be swapped. Vstorm charges no license or platform fee for the agent itself; model and hosting costs are yours and visible from the Proof of Value onwards.
Can the agent run on our own infrastructure?
Yes, in infrastructure you control, with a hosted or an open-weight model; we scope that per project. The model is designed as a replaceable component, so changing it does not mean rebuilding the system.
Which frameworks and models do you use?
We choose per project and write down why: Pydantic AI, LangChain, LangGraph or LlamaIndex for the agent, and a model per task, hosted or open-weight. Our engineers contribute to the open standards and frameworks agents run on, and we publish our agent tooling as open source.
What happens after the agent goes live?
An agent that works on day one can drift as your data and workflows change. We set up monitoring and feedback cycles at launch, and can keep maintaining models and integrations afterwards, or train your team to do it.
Which companies do you work with?
Enterprises and technology companies with an agent system to build, in Europe, the United States, the United Kingdom and the Middle East.
Start with one workflow
Test one workflow on your own data
A Proof of Value shows accuracy and running costs before the full build.
From the blog
Reading on AI agent development
Agentic AI
Top 10 AI agent development companies for business automation (2026)
Ten AI agent development companies for business automation, ranked on delivered agent projects, team credentials, services and thought leadership.
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
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.