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 platform migration
Move agents off the platform without changing what they do
Your agents off the platform with no break in service, rebuilt in code you own and checked against today's answers on real cases.
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 platform migrations go wrong
Your agents' behavior lives only in platform screens.
Rebuild from memory and the answers change; your team hears it from users.
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Where it stallsThe rebuild starts from the platform's screens.
What we do We record today's answers on real cases first.
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Where it stallsEverything switches over on one day.
What we do Traffic moves in stages while the platform runs.
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Where it stallsNew code tied to one model again.
What we do The model sits behind one swappable interface.
How the migration runs
Your agents, rebuilt in your repository
Every step is checked against the same real cases.
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Behavior recorded before the move
We export prompts, flows and logs, and record how today's agents answer real cases.
- Agents and flows inventoried
- Pass threshold agreed
- Today's wrong answers flagged
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Rebuilt in code, checked per case
Each flow becomes code you own and must pass the same cases before it moves.
- Integrations with scoped permissions
- Logic the platform blocked
- Mismatches fixed or signed off
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Switched in stages, then handed over
The platform keeps running until the last agent moves, so rollback stays one switch away.
- Staged cut-over, checked each step
- Code on your infrastructure
- Cases kept as regression suite
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
Agents clients run in their own code
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. Changes ship only once they meet the agreed target.
Start with the agent you run
We measure your agent on your own cases and agree the target before changing it.
Best whenYour agent runs, but accuracy, cost or speed falls short.
Technology
Where the agents land
Chosen per project, with the model as a replaceable part.
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- Evaluation set
- Tracing
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- Self-hosted
- Your cloud
FAQ
Questions about AI platform migration
What does AI platform migration include?
Moving four parts of each agent into your repository: agent logic and prompts as typed code, tools and integrations rebuilt with the permissions they need and no more, knowledge sources and conversation history exported where the platform allows it, and evaluation and monitoring in your own environment. Before the rebuild, a parity eval set records how the agents answer today; after it, traffic moves in stages while the platform stays available for rollback. You keep the code, the parity set as your regression suite, and the runbooks.
Which platforms can you migrate agents from?
No-code agent builders, workflow tools with AI steps, and vendor agent platforms where the logic lives in the vendor's interface. What matters is whether the prompts, flows, knowledge and logs can be exported or read. We check that in the first step, before anything is decided.
When is it worth moving off an AI platform?
When license or usage fees grow faster than the value the agents create, when the logic you need does not fit the platform, or when data has to stay in your environment. If none of those apply, staying can be the right call, and we will say so.
How do you make sure the agents behave the same after the move?
With a parity eval set built from real conversations before the rebuild starts. Every rebuilt agent runs the same cases as the platform version, and answers are scored for equivalence, not word for word. A mismatch is either fixed or approved by your team as an intended change, so nothing changes by accident.
Will our users notice the switch?
They should not: traffic moves in stages, the parity set runs at each step, and the platform stays available for rollback until the last agent has moved.
What happens to our data and conversation history?
Where the platform allows an export, it moves into storage you control. Anything that cannot be exported is listed in the first step, so you know what stays behind before you commit.
Will we be tied to a model or framework after the migration?
No. The model sits behind one interface, so hosted and open-weight models can be swapped without rewriting the agents; prompts are retuned and the parity set decides whether the new model passes. The framework is open source, the code is in your repositories, and Vstorm charges no license or platform fee.
Moving off a platform
See what moving your agents would take
We review what the platform holds and say whether moving is worth it.
From the blog
Reading on platforms and custom builds
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Custom Agentic AI vs Off-the-Shelf AI Platforms: Pricing and Service Comparison 2026
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Buy vs build agentic AI: a single-workflow decision
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Off-the-shelf AI platform or Custom AI Agent solution?
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