AI Engineer · October 23, 2026

Agent evaluation: follow tool evidence through to the outcome you actually need

video Model Evaluation AI Engineering Agent Security

Agent evaluation: follow tool evidence through to the outcome you actually need video thumbnail

Why it matters

Stephanie Jarmak uses a search-tool investigation to distinguish tool availability, invocation, returned evidence and its use in the final answer. She shows why retrieving every possibly relevant file can score well on recall while failing a precision-sensitive task. Her evaluation approach varies realistic configurations, repeats runs and separates warning signals from enforced stop conditions.

My takeaway: Choose representative task families and define the outcome metric before comparing configurations. Trace whether the needed tool was available, called correctly and used effectively; include omissions and failed calls. Repeat runs and inspect precision as well as recall. Test that a failed quality gate actually blocks progress and estimate the human workload from false alarms.

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