Agent quality flywheels turn traces into evals

Google describes an agent evaluation loop that turns traces or scenarios into datasets, runs inference, grades behavior, clusters failures, and retests targeted fixes. Why it matters: Agent reliability improves faster when failures become reusable test cases instead of one-off debugging notes. The pattern makes evaluation part of the build loop rather than an afterthought.

Try this: For one agent workflow, save ten representative traces, grade task completion and source quality, cluster the misses, then rerun the same set after changing prompts or tools.