agent-evals

Automate AI agent evaluations by building suites with golden datasets, rubrics, and regression gates, then integrating them into CI/CD pipelines to track pass rates and block deploys on critical regressions.

To catch AI agent regressions, run layered evaluations including unit, tool, end-to-end, and safety checks. These repeatable checks ensure agents behave predictably and safely as they evolve.

Prevent unintended behavior changes by running automated regression tests on your AI agent every time new code is committed, detecting critical regressions before they reach production.

Yes, you can use golden datasets and rubrics to build automated evaluation suites for AI agents, establishing repeatable checks that improve safe behavior over time.

Yes, integrating agent testing into development pipelines tracks evaluation pass rates and blocks deploys automatically when critical regressions are detected.

The best way to ensure safe agent behavior over time is implementing layered safety evaluations alongside automated regression gates, providing repeatable checks that track and improve behavior as the agent evolves.