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.
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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Text-to-workflow agents building validated node graphs inside the platform.
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Supply chain intelligence agents reducing manual coordination overhead.
AI agent implementation for a global automotive enterprise.
Bilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base.
Read case studyRebuild from memory and the answers change; your team hears it from users.
Where it stallsThe rebuild starts from the platform's screens.
What we do We record today's answers on real cases first.
Where it stallsEverything switches over on one day.
What we do Traffic moves in stages while the platform runs.
Where it stallsNew code tied to one model again.
What we do The model sits behind one swappable interface.
Every step is checked against the same real cases.
We export prompts, flows and logs, and record how today's agents answer real cases.
Each flow becomes code you own and must pass the same cases before it moves.
The platform keeps running until the last agent moves, so rollback stays one switch away.
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.
Chosen per project, with the model as a replaceable part.
We review what the platform holds and say whether moving is worth it.
Custom agentic AI against off-the-shelf platforms: what each covers, what each costs, and the workflow complexity where the choice flips.
A per-workflow buy vs build framework for agentic AI: four paths, a difficulty score from 4 to 12, data sensitivity, model choice and lock-in.
Key considerations to keep in mind when deciding whether to build a Custom AI Agent or choose an existing SaaS platform.