Classify actions by consequence
List the agent’s possible actions and score financial impact, reversibility, customer exposure, data sensitivity, legal or policy concern, and confidence. Reading a dashboard is not equivalent to changing an advertising budget or sending a customer message.
Use risk tiers to decide which actions can run automatically, which require sampled review, and which always require explicit approval.
Create a clear approval contract
Present the proposed action, purpose, key evidence, assumptions, expected impact, alternatives, and rollback method. Ask the reviewer for a bounded decision such as approve, revise, reject, or escalate.
Avoid checkpoints that require the human to redo the entire task. If reviewers cannot understand the evidence quickly, improve the agent output and interface.
Handle uncertainty and exceptions
Set confidence or evidence thresholds that trigger a pause. Define escalation owners for missing access, conflicting data, sensitive content, unexpected cost, or tool failures.
Preserve the state so work can resume after clarification. Timeouts and fallbacks should fail safely rather than silently continuing with weaker assumptions.
Measure the review system
Track approval rate, revision rate, reviewer time, false approvals, false escalations, time to resolution, downstream errors, and recurring correction themes. Segment by workflow and action type.
Turn repeated corrections into better instructions, tool constraints, validation, or training examples. The human loop should make the system more reliable over time.