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 announcementAgentic 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.
50+ workflows agentified since 2017
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~20% → ~40% conversion to a final saleThree-agent product advisor guiding customers through print-order configuration.
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42% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmarkHIPAA-compliant guideline-executing triage — 42% to 98% on the open 50-scenario benchmark.
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Up to 1 hr → ~2 min to create 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 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.
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Where it stallsEngineers practice prompting in a sandbox.
What we do Engineers build, test and release in your repositories.
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Where it stallsProcess owners get an awareness session, no role.
What we do Process owners approve what the agent does.
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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.
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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
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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
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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
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
10M+ PyPI downloads
4,000+ GitHub stars
33 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.
Case studies
Agent systems our clients' teams 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 the manual setup.
Up to 1 hrto prepare a workflow by hand
~2 minto create 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.
42% → 98%accuracy, raw LLM vs guideline-executing
96%weighted F1 across 566 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.
FAQ
Questions about agentic AI training
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.
From the blog
Reading for teams adopting agents
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 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
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.