eval-harness

To measure Claude Code agent performance, you need a structured evaluation framework that defines pass/fail criteria and tracks pass@k metrics. This approach applies eval-driven development to quantify reliability and catch performance drift across development cycles.

Eval-driven development for AI workflow validation is a define-implement-evaluate-report cycle that treats AI performance testing like unit tests. It requires setting up capability and regression evals to ensure existing functionality remains intact after changes.

Setting up regression testing for Claude Code prompts involves defining pass/fail criteria before implementation and running structured eval runs. This process generates a baseline performance report to automatically track if future changes break existing functionality.

Yes, you can benchmark cross-model performance by applying an evaluation framework that tracks pass@k and pass^k metrics. These reliability metrics measure agent consistency and catch performance drift over time across different model versions.

Yes, the agent testing framework operates without external dependencies, applying a formal evaluation framework directly to your AI-assisted development workflows. It integrates structured eval runs into existing development pipelines to satisfy capability testing requirements.

The best way to define success criteria for AI-assisted development workflows is implementing a standardized evaluation framework. This eliminates guesswork by formally defining pass/fail conditions and tracking reliability metrics across development cycles.