eval-harness

Measuring AI agent performance involves applying an evaluation harness to quantify accuracy, efficiency, alignment, and quality across standardized testing scenarios, enabling repeatable metrics for cross-task comparisons and continuous improvement.

Agent evaluation metrics should include target thresholds like first-time success rate over 80%, iterations under 3, test coverage over 80%, and successful builds. These standardized benchmarks quantify performance across accuracy, efficiency, alignment, and quality dimensions.

Generate standardized reports for AI testing by running an evaluation harness at the end of a session. This produces structured evaluation reports capturing performance learnings and metrics to guide future improvements and continuous quality assurance.

Yes, you can use an evaluation harness to compare agent performance across sessions. It standardizes testing scenarios with defined dimensions and target metrics, producing repeatable results that enable direct cross-task comparisons and track continuous improvement over time.

Run agent performance evaluation at the end of a session. Integrating the evaluation harness at this stage captures structured learnings and generates reports for continuous improvement without disrupting active task execution.