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 announcementForward Deployed Engineering
Senior agent engineers inside your team
Agents your own team ships and keeps running. Our engineers work in your codebase until your people can operate, maintain and extend them 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 outside builds do not stick
An agent built outside your team needs the vendor to change it.
Your team inherits code it cannot safely change.
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Where it stallsBuilt in the vendor's repository.
What we do Built in your repositories, on your tickets.
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Where it stallsYour engineers watch from the side.
What we do Your engineers build the agents with ours.
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Where it stallsThe knowledge leaves with the contract.
What we do Your team runs the agents before we leave.
How the engagement runs
From your backlog to a team that runs agents
Picked from our 25+ AI engineers to match your stack.
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Inside your repositories and tickets
Our engineers pick up tickets from your backlog, commit to your repositories and follow your review rules.
- Your backlog and sprints
- Commits in your repositories
- Your access and review rules
Mixam's print-order agent lifted conversion to a final sale from about 20% to about 40%. - 0203
Agents shipped with your engineers
Each agent is built with your engineers and ships behind an evaluation set they can rerun.
- Pairing on agent code
- Evaluation sets in your CI
- Traces your team can read
On an open 50-scenario clinical benchmark, a raw LLM scored 42% and the guideline-executing agent 98%. - 0303
Your people run it after we leave
We step back once your engineers can change, release and debug the agents without us.
- Runbooks your team has used
- Your code, prompts and evaluations
- No dependency on Vstorm
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
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 our engineers have shipped
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 Forward Deployed Engineering
What is Forward Deployed Engineering?
Senior agent engineers from Vstorm join your team and work in your backlog, your repositories, your tickets and your infrastructure. They build agents together with your engineers and stay until your people can operate, change and extend those agents without us.
How is Forward Deployed Engineering different from staff augmentation or outsourcing?
Staff augmentation sells engineering hours against a specification your team has already written. Outsourcing moves the build into the vendor's codebase and hands it back at the end. With Forward Deployed Engineering, our engineers bring the agent patterns, the evaluation practice and the judgment for edge cases no requirements document covers, they commit to your repositories, and the engagement is planned around your team running the agents once we step back.
When does Forward Deployed Engineering fit, and when is a full build better?
It fits when your team owns the build and wants senior agent engineers next to it. If you want one team accountable for the outcome, from redesigning the workflow to handing over the system, Process redesign and build is the better path. If an agent pilot has stalled before going live, start with Stalled pilot recovery.
Who joins our team, and how do they work with our engineers?
Senior engineers from our 25+ AI engineers, matched to your stack and to the agents you plan to build. They take tickets from your backlog, follow your review and access rules, pair with your engineers on the agent code and the evaluation sets, and code review runs in both directions.
What experience do the engineers bring?
Vstorm has worked in AI since 2017 and has 50+ workflows agentified behind it. Our open-source agent tooling has 9M+ PyPI downloads and 4,000+ GitHub stars. Published results include a guideline-executing clinical triage agent for Schmitt-Thompson Clinical Content (98% on an open 50-scenario benchmark, against 42% for a raw LLM) and Mixam's print-order agent, built inside Mixam's own infrastructure, which lifted conversion to a final sale from about 20% to about 40%.
Who owns the code, the data and the decision logic?
You do. Every commit lands in your repositories, the data stays in your systems, and the model is a replaceable component, hosted or open-weight. The prompts, evaluation sets and runbooks are yours too. Vstorm charges no license or platform fee for the agents.
How does a Forward Deployed Engineering engagement end?
Our engineers stay until your team can release, debug and extend the agents on its own, with runbooks your engineers have already used and evaluation sets they can rerun before every release. After that, the agents keep running without Vstorm.
Talk to an engineer
Put senior agent engineers on your backlog
Bring your backlog and we show where our engineers would start.
From the blog
Reading on engineers inside your team
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Lesson 1: Your experts' time is the ultimate AI bottleneck
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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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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.