BlueBear Insights · Human Approval · 5 min read

AI Agent Approval Fatigue: Designing Review Queues That Work

BlueBear evidence ladder connecting governed agent decisions with execution and outcome records
Operational evidence should connect every agent decision to its authority, execution, and accepted outcome.

Human approval is not a control when reviewers cannot understand the action or face enough requests to approve reflexively. Reduce volume through policy, not by hiding risk.

Design the review card

Show requester, agent and version, purpose, tool, resource, destination, material before-and-after values, data class, risk, expected side effect, rollback path, evidence link, and expiry. Bind the decision to an action fingerprint so changes require re-review.

Route by risk

ClassHandling
Reversible, low impactPolicy allow with sampling and audit
Material but routineNamed queue with clear SLA and bounded batch approval
Irreversible, sensitive, or high valueFresh review, step-up authentication, possible dual control
Unknown or changedStop and escalate; never inherit prior approval

Metrics

Track approval rate by action class, decision time, expiry, reversals, reviewer disagreement, incidents after approval, changes that invalidated approval, and queue abandonment. A near-100% approval rate with very short review time is a control-quality warning.

Apply these queue rules to BlueBear approval patterns.

Primary sources