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 announcementVibe coding hardening
Your AI-built agent, made safe to run and maintain
An AI-built agent that holds up with real users. Tests from real cases and failure paths, typed contracts, security review and logging, then handed to your team.
50+ workflows agentified since 2017
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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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~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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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.
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Supply chain intelligence agents reducing manual coordination overhead.
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AI agent implementation for a global automotive enterprise.
Why fast prototypes stall
The demo runs. Nobody has tried a bad input.
Written to pass the demo: one path checked, errors printed and forgotten.
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Where it stallsA full rewrite, because the code looks messy.
What we do Keep the working logic, harden around it.
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Where it stallsCalled finished because the demo passed.
What we do Tests for the paths the demo skipped.
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Where it stallsKeys, prompts and config in the code.
What we do Secrets stored, config in the environment, prompts versioned.
How we harden it
Code your developers can maintain
The features stay; what breaks them gets caught.
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Reviewed as it is
We read the whole codebase, run the agent and give each finding a severity and a fix.
- Security and data review
- Risky dependencies flagged
- Keep, harden or rewrite
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Hardened around the logic
Tests come first, from real cases and failure paths; typed contracts then catch bad values, model output included.
- Failed calls retried or reported
- Logs and traces per request
- Modules split under tests
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Handed over to your developers
Your developers review each change with us; the coding assistant can stay, with tests guarding it.
- CI that blocks failing builds
- README, runbook, decision notes
- Walkthroughs with your developers
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 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 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.
FAQ
Vibe coding hardening, answered
What is vibe coding hardening?
Vibe coding hardening makes an agent built by prompting AI coding assistants safe to run and change every day: review, tests, typed contracts, security, error handling, logging and a structure your developers can maintain. The features stay as they are.
What does vibe coding hardening include?
It includes a review of the whole codebase with ranked findings and a keep, harden or rewrite call for each part. Then come unit and integration tests from real cases and failure paths, evaluation cases for every step a model decides, and typed contracts at API, tool and model boundaries. Keys leave the code and the git history; dependencies, authentication, data access and prompt injection paths are reviewed. Failed calls get timeouts, retries or fallbacks, every request gets logs and traces, and the code gets clear module boundaries, CI, a runbook and walkthroughs with your developers.
Do you rewrite the agent from scratch?
Rarely as a whole. The review marks each part as keep, harden or rewrite, with the reason written down. Logic that works and passes the new tests stays.
Does it matter which AI coding tool built the agent?
No. We work from the code in the repository. If the agent lives inside a hosted app builder, the first step is moving the code into a repository you control.
How do you handle secrets that were already committed?
We find them in the code and in the git history, help you rotate them and move them to a secret store or the environment. Rotation matters more than deletion: a key that was ever committed has to be treated as exposed.
How long does hardening take?
That depends on the size of the codebase and on what the review finds. The review comes first and ends with a fix plan and an estimate per part, so you agree the scope before the work starts.
Can our developers keep using AI coding assistants afterwards?
Yes, if it helps them. After hardening, the tests, the typed contracts and the CI checks catch an assistant's mistakes before they are merged.
What does our team get at the end?
The repository with tests, typed contracts and CI, logs and traces, a README whose setup steps work, a runbook, notes on the main decisions, and walkthroughs with the developers who will own the code.
Vibe coding hardening
Bring the repository. Leave with a plan.
The review comes first, with a fix plan and an estimate per part.
From the blog
Reading on AI-written code
Engineering
Deep engineering at Vstorm in the age of AI-code generation
Vibe coding is fast and leaves technical debt behind. How Vstorm uses AI code generation in production work, and the review gates that bound it.
Agentic AI
AI proof of concept vs production-grade agent: key differences is design and intent
A proof of concept shows an agent can do the work. A production agent has to keep doing it. The design and intent differences between the two.
Engineering
Prompt injection is not the vulnerability. Your agent’s tools are.
Prompt injection will get through. AI agent security depends on what the agent can do when it does: break the lethal trifecta and enforce limits in code.