Autonomy is a set of permissions
An AI agent is not simply autonomous or supervised. It has specific permissions within a specific job. It may be allowed to retrieve approved information, prepare a response, update a low-risk record or route a request while being prohibited from making commitments, changing prices or acting on uncertain personal data.
Writing these permissions down gives the team a shared definition of useful autonomy. It also makes testing more concrete: the system must succeed at the job and reliably stop at the boundary.
Use confidence and consequence together
A confident output is not automatically a safe action. Review both how certain the system can be and what happens if it is wrong. Low-consequence, reversible actions can often proceed with logging. High-consequence or difficult-to-reverse actions should require approval even when the answer appears clear.
Escalation should carry context. The person taking over needs the original request, relevant evidence, the proposed action and the reason the agent stopped.
Make control visible in daily operation
Assign an owner, keep an audit trail and review a sample of completed work alongside every escalation. Watch for changes in input quality, business rules and customer behaviour rather than treating the launch configuration as permanent.
A visible edge builds trust because people know where the system works, where it asks and who remains responsible for the result.
Put it into practice.
- Separate read, prepare, decide and act permissions for the proposed agent.
- Require approval for high-consequence or difficult-to-reverse actions.
- Log completed work and escalations so the boundary can improve over time.