AI agents can look reliable in a demo and still fail unpredictably across repeated runs. Agent Reliability Toolkit is an open-source developer tool for testing agent reliability at scale. Run your agent repeatedly, measure pass/fail rates, inspect failures, track latency, and detect regressions between versions, all from a simple dashboard. Instead of asking, “Did my agent work?” Ask: “How reliably does it work?” Built for developers shipping AI agents beyond the demo.
Hey Product Hunt 👋
I built Agent Reliability Toolkit around a problem I kept seeing with AI agents:
A successful demo doesn't tell you whether an agent is actually reliable.
An agent might work perfectly once, then fail intermittently across the next 50 or 100 runs.
So I built an open-source toolkit for repeated-run reliability testing.
It helps you measure pass/fail rates, inspect failures, track latency, and detect regressions so reliability becomes something you can measure rather than assume.
The project is still early, and that's exactly why I'm launching it now.
I'd especially love feedback from people building or deploying AI agents:
What reliability metric or failure mode would you want the toolkit to support next?
The project is completely open source, so contributions and GitHub issues are welcome.