eval-harness

Eval-driven development is a structured framework that evaluates AI development through continuous testing, tracking, and reporting to ensure quality and reliability. It measures performance, validates changes, and detects regressions in AI workflows.

Run evaluations on Claude Code sessions by initializing the eval-harness with the `/eval define feature-name` command. This sets up continuous testing to measure performance, validate changes, and monitor stability across different model versions.

Yes, you can track regressions in AI agents across different model versions by running continuous evaluations throughout the development process. This monitors changes for stability and ensures reliability over time.

The best way to measure performance changes in AI workflows is using a formal evaluation framework that emphasizes continuous testing, tracking, and reporting. This approach validates changes and detects regressions systematically.

Eval-driven development principles are not ideal for quick prototyping or exploratory phases where continuous testing and tracking overhead outweigh the need for formal regression detection and performance measurement.