Run a calibrated verification sample
Design a verification sample that calibrates trust in an AI agent's current performance.
The controller needs evidence before reducing review on a finance reconciliation agent. Calibrated sample = random baseline + risk-weighted stress cases + recent-change cases. A random-only sample can look clean while rare, expensive, or newly changed cases fail. Baseline Take random cases from the full population. This estimates ordinary performance and catches broad drift. Impact Add high-value or high-consequence cases. Trust should be lower where one mistake is costly. Edges Add cases near policy thresholds or confidence boundaries. Agents often fail where rules and categories nearly overlap. Change Add cases touched by new data feeds, policies, or tool changes. Recent…
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