Your Agent Eval Set Is Rotting: Build a Failure-Mining Loop for Google ADK
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Maintaining reliable AI agents in production requires moving beyond static test suites toward continuous failure-mining evaluation loops. Static evaluation sets degrade over time because they rely on happy-path development scenarios that miss unpredictable production failures. Rather than blindly copying raw production transcripts into test folders, this guide outlines a structured pipeline for capturing and curating failure candidates. Effective evaluation sets analyze failures across key dimensions: tool execution errors, trajectory anomalies, escalation behavior, and prompt or model version drift. For backend engineers building agentic workflows, constructing a feedback loop that systematically transforms real-world production edge cases into reproducible tests is crucial for long-term system stability and software reliability.