Agents / NAVIN RESEARCH
Human Approval Gates for High-Risk AI Agent Actions
Place approval at the last responsible moment and show people exactly what the agent intends to do.
Place approval at the last responsible moment and show people exactly what the agent intends to do.
Short answer: Require approval for irreversible, externally visible, expensive, or privilege-changing actions. Generate a preview from validated structured arguments, freeze those arguments while approval is pending, and execute exactly the approved operation.
Implementation steps
- Classify tools by risk and define policy outside the prompt.
- Show target, scope, cost, data exposure, and rollback options in the approval view.
- Bind approval to a hash of the normalized request.
- Expire approvals and require re-approval after any material change.
Validation checklist
- Change an argument after preview and ensure execution is blocked.
- Test cancellation, timeout, and duplicate-submit behavior.
- Audit who approved what without recording excess sensitive content.
Common mistakes
- A generic 'Are you sure?' dialog.
- Approval before the agent has concrete arguments.
- Letting the model decide whether its own action requires review.
Practical decision
Ship the smallest design that passes the checks above. Record the model, prompt, tool, dataset, and runtime versions used in testing so later changes can be compared rather than guessed. Recheck the linked documentation before relying on provider-specific limits, model names, prices, or preview features; those details change more quickly than the engineering principles in this note.
