AI Agent Evaluation: Grade Traces, Not Resolution Rate

Support teams should evaluate AI agents using independently defined metrics and full execution traces rather than relying on vendor-reported resolution rates, which may obscure flawed tool use, missed verification steps, unsafe behavior, or poor handoffs. A robust program measures outcomes alongside trajectory, reasoning, efficiency, safety, consistency, handoff quality, and policy adherence, with clear definitions tailored to the organization’s workflows. Test sets should be drawn from real customer transcripts and include common intents, difficult escalations, and compliance-sensitive cases, while each case specifies expected tool use and pass criteria. Teams can scale evaluation with LLM judges calibrated and regularly checked against human reviewers, but should run regression tests on every change to prompts, tools, or models to detect altered execution paths before release. The recommended approach is to begin with a single queue or intent, set expansion thresholds in advance, and broaden deployment only when predefined outcome, safety, and judge-human agreement standards are met.

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