Google Developers Blog walks through an Agents CLI evaluation loop for AI agents, covering test-case generation, grading, run comparison, failure analysis, prompt optimization, regression tracking, model-backend experiments, and automated alerting. Using a LangChain template, the tutorial demonstrates how per-case results can reveal regressions and model differences that summary scores alone obscure. It also argues that evaluation helps only when teams act on the findings.