AI Signal 173
Driving the Agent Quality Flywheel from Your Coding Agent
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Developers building AI agents currently lack disciplined feedback loops between prompt tweaks and production regressions. This skill automates evaluation by running traces through AutoRaters, clustering failures, and comparing before/after metrics—giving teams a repeatable way to know if a change actually improved quality or just shifted the vibe.
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The skill automates five stages—Prepare Data, Run Inference, Grade, Analyze Failures, and Optimize & Iterate—looping stages 2–5 until quality targets are met.
The optimizer and evaluator are deliberately decoupled: whatever proposes a fix never grades it, preventing metric gaming.
It ships as two packages (google-agents-cli-eval for ADK agents, agent-platform-eval-flywheel for the Evaluation SDK directly) and requires human approval for proposed fixes.
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