AI Agent Evals — GitHub Action for CI Scoring

Practical Notes

• Setup time ~25 minutes (wire workflow + prepare a tiny dataset JSON)
• Two measurable checks: Action run succeeds, and the summary contains evaluation outputs you can compare across runs
• GitHub stars + forks (verified): see Source & Thanks

The safest way to adopt this is to start with a “tiny dataset” and a single evaluator, then expand coverage. Keep your dataset versioned in-repo so it changes via PR review.

Practical CI pattern:

• Run on pull_request and main pushes.
• Fail the job when key metrics regress beyond an agreed threshold.
• Store the evaluation summary/report as an artifact for auditing.

Avoid judging only the final answer: include tool traces and system-level checks when your agents can execute actions.

FAQ

Q: Do I need a large dataset? A: No. Start with a tiny dataset, then add queries as your agent grows.

Q: Where do results show up? A: The README states outputs appear in the GitHub Actions run summary section.

Q: How do I make it a gate? A: Fail the job on regressions and require the check to pass before merge.