Playbook

Designing approvals for unsafe AI agent actions

How to decide when agents can act automatically and when humans should approve.

Playbook7 min readHead of AI, AI Product Teams

Action Severity

Not every agent action carries the same risk. Reading a public document differs from sending an email, changing a record, exporting data, initiating a refund, or calling an administrative API.

Approval Triggers

Approvals should consider user role, data sensitivity, action reversibility, system criticality, and confidence. Triggering approval on every action creates fatigue; triggering only after failure is too late.

Human Context

Approvers need the prompt, retrieved context, proposed action, affected system, policy reason, and business impact. Approvals without context become rubber stamps.

Continuous Tuning

Measure approval frequency, denial reasons, false positives, and time to decision. Use those metrics to refine policy and automate low-risk paths safely.

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