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.
Text-to-workflow agents building validated node graphs inside the platform.
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Three-agent product advisor guiding customers through print-order configuration.
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HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
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Bilingual 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.
AI agent implementation for a global automotive enterprise.
Written to pass the demo: one path checked, errors printed and forgotten.
Where it stallsA full rewrite, because the code looks messy.
What we do Keep the working logic, harden around it.
Where it stallsCalled finished because the demo passed.
What we do Tests for the paths the demo skipped.
Where it stallsKeys, prompts and config in the code.
What we do Secrets stored, config in the environment, prompts versioned.
The features stay; what breaks them gets caught.
We read the whole codebase, run the agent and give each finding a severity and a fix.
Tests come first, from real cases and failure paths; typed contracts then catch bad values, model output included.
Your developers review each change with us; the coding assistant can stay, with tests guarding it.
Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.
Shaping agentic AI
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
Framework contributors
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
Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.
Track record
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.
The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup.
2 hrsto prepare a workflow by hand
3 minto generate one with the agent
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
The model answers the assessment questions. The guideline decides the disposition, the same way every time.
44% → 98%accuracy, raw LLM vs guideline-executing
94%weighted F1 across 591 scenarios
An English and Arabic agent inside ARIJ's Moodle that answers only from ARIJ's own knowledge base.
1% → 100%of inquiries answered, desk vs agent
22countries the network covers
Each path starts with a free call with a founder or senior AI engineer. Changes ship only once they meet the agreed target.
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.
The review comes first, with a fix plan and an estimate per part.
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.
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.
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.