Direct answer
How should teams review AI agent output?
Teams should review AI agent output against the original brief and acceptance criteria, inspect supporting and conflicting evidence, validate high-impact claims or changes in the real environment, check uncertainty and affected scope, and record whether the output was approved, rejected, or revised. A completed run is not the same as accepted work.
Reviewed by Raltic Research · Updated August 15, 2026
The short answer first, followed by the operating details and next decision.
What should every review check?
Check task fit, evidence, correctness, uncertainty, impact, and ownership.
The exact validation depends on the domain, but the reviewer should always be able to connect the result back to the requested outcome and understand what remains unproven.
- 01Does it answer the brief?
- 02Are important claims supported?
- 03What evidence conflicts or is missing?
- 04Were tests or validations run?
- 05Who owns the next action?
How does review differ by workflow?
Code needs diffs and tests; research needs sources and freshness; customer-facing work needs claim and authorization review.
Use a domain-specific evidence packet rather than a generic confidence score. High-impact work should be verified in the system where the action will occur.
What should the team retain?
Retain the output version reviewed, evidence, feedback, reviewer, decision, and resulting task or action.
This record supports audits, onboarding, regression analysis, and better future briefs.
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