How to decide which AI agent outputs skip human review
Production teams using features need a way to decide which results require and which can go through automatically. The work includes support replies, decisions, , routing, and triage. Possible decision methods include , , manual spot checks, policy rules, or another control process.
The main issue is whether teams measure error rates specifically for outputs that bypass review, or whether they mostly rely on dashboards and manual inspection. The practical question is whether AI agent release gates are based on evidence or informal judgment.
Key points
- Teams need criteria for deciding when AI outputs can skip .
- The examples include support replies, decisions, , routing, and triage.
- Possible controls include , , manual spot checks, and policy rules.
- The key metric is the error rate among outputs that bypass review.
- gates can reduce review cost while keeping risk visible.