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.

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30+ production deployments since 2017
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
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.

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

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
All 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.

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  1. Pragmatics of AI workshop Best whenYou have not chosen the workflow yet.
  2. Engineering-led advisory Best whenYou need an engineering view before you commit to a build.
  3. Forward Deployed Engineering Best whenYour team owns the build and wants senior agent engineers next to it.
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.

Book a free consultationRead case studies
From the blog

Reading for teams adopting agents

More articles on the blog
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.
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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.
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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.