AI agent governance
Map human approval boundaries in an AI agent workflow
Identify which agent actions remain read-only, which can run automatically, and which must stop for human approval.
Target outcome
An action-level map of evidence, permissions, reversibility, impact, approver, timeout, and recovery.
Work through the evidence
before the final claim.
- 01
List actions, not capabilities
“Uses email” is too broad. Separate reading a mailbox, drafting a reply, sending one reply, changing routing, and bulk outreach because their authority and impact differ.
- 02
Classify consequence and reversibility
Record whether an action spends money, changes public or customer state, discloses data, creates a legal promise, or is difficult to reverse.
- 03
Name the evidence required
Define what the agent must know, where that fact comes from, how fresh it must be, and what contradiction forces a stop.
- 04
Design the stop and recovery path
Specify the approver, exact requested decision, timeout behavior, audit record, retry rule, and what unrelated work may continue.
Example input
A concrete first pass
An agent can draft and send supplier follow-ups, update the CRM, and promise a delivery date.
Reviewable output
Drafting allowed; sending requires recipient and policy checks; CRM updates require source evidence; delivery promises require an accountable human.
The boundary follows the real action and consequence instead of treating the whole agent as simply autonomous or manual.
Important boundaries
What this workflow does not prove
- A map does not grant technical permissions.
- Legal, privacy, security, employment, and sector-specific review may still be required.
- Runtime enforcement must be tested independently.
AI operations teams · $99 once
Use AgentBoundary Approval Map for the structured first pass.
Map an agent workflow into clear approval boundaries so humans can see where action must stop, escalate, or remain read-only.