Maida.AI is focused on a sharper problem for teams building AI agents: nobody catches behavioral regressions before they merge. We're fixing that.
Every week, engineering teams ship AI agent changes that silently break behavioral properties. Step counts triple. Unexpected APIs get called. Latency doubles. Eval tools pass. Users notice first.
We think every PR touching an AI agent should be gated on behavioral invariants — the same way every PR touching network code gets latency budgets. That's what Maida.AI does.
No LLM-as-judge in our check path. Behavioral checks compare measured properties (step count, tool calls, latency) against baselines. No false positives from scoring variance.
The right place to catch a regression is the PR, not the incident. We gate before merge so you never have to chase a production regression.
Maida runs on your machine or CI runner. Maida.AI does not receive traces by default unless you explicitly export them or configure external telemetry. Open-source core.
AI agents will be held to the same engineering standards as any production system.
Behavioral testing for agents is still manual and reactive — that's the problem we solve.
The devtools model is proven: developers adopt tools when they can try them in one repo, understand the signal, and wire the check into CI.
No golden dataset should be required to catch a behavioral regression.
Want help trying Maida on one agent workflow? Send a note.