Hill-Climbing Agent Quality with Agents CLI Evaluation

Agents CLI provides a framework-agnostic evaluation loop for improving agent reliability over time through generating responses from test datasets, grading traces with custom metrics, comparing runs, analyzing failures, and, for supported ADK projects, optimizing prompts automatically. Using a LangChain weather-agent example, the walkthrough expands a minimal dataset into representative greeting, weather, and off-topic cases, establishes a baseline score, and demonstrates how a mistaken chef-focused system prompt causes a measurable regression that ordinary code checks would miss. Per-case comparisons reveal whether declines stem from tone, tool-use failures, hallucinations, or scope issues, while a model-backend experiment shows why aggregate averages can obscure a problem concentrated in a single scenario. The process can be integrated into nightly cron, Cloud Scheduler, or CI workflows, with scripts that alert when scores fall beyond a chosen threshold and preserve complete result files for later diagnosis. Effective evaluation practice emphasizes small, frequently run datasets for development, larger scheduled regression suites, version-controlled separation of test inputs and scoring expectations, careful interpretation of summary metrics, and human review of automated prompt-optimization results to avoid metric overfitting.

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