AI Agent Approval Workflows for Consequential Actions
Some agent actions should execute automatically, some should be denied, and some require a human decision. AI agent approval workflows provide that review path without turning every action into a manual ticket.
Where human approval belongs
Human review is most useful when policy can identify a bounded class of actions that require additional authority. A REVIEW decision can pause propagation while an authorized approver evaluates the request.
This is different from asking a person to inspect every agent thought. The review is attached to the proposed consequential action.
Approval is not the whole security model
A human click should not bypass identity, policy, expiry or replay controls. Approval needs to operate inside the same governance chain as automated decisions.
The downstream executor should still validate the resulting authority before performing the side effect.
Bounded review state
Useful approval systems preserve the request context, agent identity, action digest, decision state and expiration window so an approval cannot silently mutate into authority for a different action.
Sentinel's review path is designed around governed decision records rather than an unstructured approval message.
Operational outcomes
The practical objective is selective escalation: routine authorized actions can continue, prohibited actions are denied, and higher-risk actions can be held for customer-controlled review.
That creates human-in-the-loop governance without making the human reviewer the only protection between an agent and production.