eval-harness

Regression testing for AI agents requires defining formal capability evaluations to ensure existing functionalities remain unbroken. This framework supports creating regression evals that track agent performance and prevent regressions during development, similar to unit tests.

Eval-driven development (EDD) is a methodology where you define success criteria before implementing AI agent features. This approach formalizes development by guiding coding with predefined capability evaluations and tracking metrics like pass@k.

Measuring AI agent reliability with pass@k metrics involves running formal evaluations and tracking the success rate across multiple attempts. This framework quantifies performance using pass@k to assess reliability and generate clear reports for benchmarking.

Yes, you can use model-based and human graders for AI agent evaluation. This framework supports code-based, model-based, and human graders to assess capability and regression evaluations, providing flexibility in how agent performance is measured.

To define success criteria before implementing AI agent features, establish capability evaluations that specify expected outcomes. This eval-driven development practice ensures structured development and clear benchmarking of what constitutes success for new functionalities.