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    AI AGENT AUTOMATIONS

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    Testing & Evaluation

    Validate Agent Behavior

    Build testing strategies, evaluation frameworks, and monitoring for AI agent systems.

    "You can't improve what you can't measure. Test early, test often."
    — AI Agent Automations

    Testing agents is harder than testing traditional software. Outputs are non-deterministic. Success can be subjective. Edge cases are hard to predict. But testing is essential for production systems. Unit tests verify individual components. Integration tests check agent workflows. Evaluation datasets measure quality over time. Monitoring catches drift and degradation. A good testing strategy builds confidence for production deployment.

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    Use Cases

    Common Pitfalls

    1

    Test behavior, not just outputs

    2

    Evaluation datasets enable regression testing

    3

    Monitor for drift over time

    4

    Tracing reveals decision-making paths

    5

    Non-determinism requires statistical approaches

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