eval-harness

Eval-driven development for AI agents is implemented by defining pass/fail criteria and regression testing workflows to measure agent reliability using structured evaluation frameworks and pass@k metrics.

Benchmarking AI agent reliability involves running structured capability evaluations with deterministic code-based graders and model-based assessments to compare performance across different model versions.

Regression testing for agent refactors uses baseline comparisons and formal evaluation frameworks to ensure new changes do not break existing functionality or degrade agent performance.

Model-based evaluation supports complex task validation for open-ended outputs, complementing deterministic code-based checks and human-in-the-loop review within a multi-modal grading framework.

Capability evaluations require clear success criteria defined as pass/fail conditions to formally verify new agent features and measure reliability through structured pass@k metrics.

Human-in-the-loop review is utilized for complex task validation when deterministic code-based graders are insufficient, enabling multi-modal grading within a structured evaluation workflow.