eval-harness

Eval-Driven Development (EDD) formalizes evaluation for AI-assisted development by defining success criteria before implementation. It uses capability and regression evals to ensure AI agents perform reliably and consistently across various tasks.

You measure AI agent reliability using pass@k and pass^k metrics. These metrics track success rates and task completion consistency, allowing you to quantify how dependably an AI agent performs a specific function like user registration.

To set up capability and regression evals, define the expected outcomes for a new AI feature before implementation. Use an eval harness to integrate code-based, model-based, and human graders for comprehensive assessment of feature functionality.

Yes, you can integrate code-based, model-based, and human graders to evaluate AI performance. This combination provides a comprehensive assessment framework for measuring AI agent reliability and ensuring consistent task completion.

Pass@k and pass^k metrics measure AI agent reliability and task completion from different statistical angles. These metrics track success rates to ensure new features work as expected without breaking existing functionality during regression testing.