Agent safety
Human-in-the-loop AI is a design decision, not a temporary compromise.
Where approvals, review queues, escalation, and policy checks belong in production AI workflows.
October 12, 2026 · 6 min read
The short version
Put people at decision points where consequences are high, context is incomplete, or policy requires accountable judgment.
01
Not every action deserves the same autonomy.
Reading a public document, drafting a reply, changing a customer record, and issuing a refund have different consequences. A mature system assigns controls according to impact instead of applying one global confidence score.
Human review is most effective when the system pauses before the consequential action—not after an error has already propagated across connected tools.
02
Design a review that respects human attention.
A reviewer should see the proposed action, relevant evidence, policy rule, uncertainty, and available alternatives. Asking someone to reread the full case removes much of the benefit. Approvals should be quick for routine cases and provide enough context for genuine exceptions.
- —Require approval for financial, legal, external communication, and destructive actions.
- —Use explicit escalation when required information is missing or conflicting.
- —Record who approved what, with the evidence visible at the time.
- —Feed corrected decisions back into evaluations before changing autonomy levels.
03
Earn autonomy with evidence.
Start with drafts or recommendations, measure agreement and correction patterns, then automate narrow categories that demonstrate stable performance. Keep a fallback path when tools fail or the request moves outside the tested distribution.
The target is not zero human involvement. It is placing scarce human judgment exactly where it changes the outcome.
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