Evals

You can evaluate AI agent performance objectively by running workflows against a multi-grader system that combines code-based, model-based, and human graders to assess transcripts and tool calls.

Regression testing for AI agents involves executing a suite of predefined tasks to ensure new versions haven't lost core capabilities or introduced bugs before deployment. This Skill automates that process using structured YAML definitions.

Pass@k and pass^k are evaluation metrics used to quantify agent quality and consistency. This Skill supports these metrics within its structured YAML task and suite definitions to measure capability and regression.

Yes, workflow evaluation assesses entire agent interactions including transcripts and tool calls rather than just single outputs. This provides a comprehensive view of agent behavior during regression and capability testing.

Yes, you need structured YAML to define tasks and suites for agent evaluation. These YAML files configure the regression and capability tests that the multi-grader system executes to verify agent quality.

The best way to automate agent quality benchmarking is using a multi-grader framework that integrates code, model, and human graders to verify workflows, calculate metrics like pass@k, and identify regressions automatically.