AI Engineer · October 13, 2026

Agent autonomy reviews: measure missed issues and assign decision owners

video AI Compliance Agent Security Model Evaluation

Agent autonomy reviews: measure missed issues and assign decision owners video thumbnail

Why it matters

Michele Grant uses an illustrative integration workflow to connect agent authority with named owners, a decision log, and staged review. The proposed scorecard pairs business baselines with error severity and independent sampling. Financial calculations stay in deterministic code. Once reviewers focus on exceptions, missed issues need their own measurement; fewer overrides can also reflect weaker scrutiny. The scenario is an operating framework, not a measured deployment result.

My takeaway: For one workflow, record each decision, its authorized owner, required evidence, and escalation path. Establish a baseline before shadow operation. Sample apparently clean outputs as well as flagged cases, and define conditions for reducing autonomy when material misses or review quality worsen.

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