Human Review of AI Outputs
Human review is not optional or a "nice to have": it is the condition that makes the agent's work trustworthy for production. Without human review, AI's greater generation speed becomes a greater speed of accumulating hidden technical debt.
What Is Reviewed
Human review of AI output covers four dimensions according to the reinforced DoD:
- Static analysis: the code passes linters and automated analysis tools without critical errors. This part can be assisted by automated tools.
- No duplication: the agent has not introduced duplicate code. The recommended threshold is that duplication of AI-generated code should not exceed 5% of the module's total.
- Security: no obvious vulnerabilities have been introduced, such as injections, exposure of secrets, insufficient input validation, or insecure data handling.
- Business logic: the part that only a human can do. Does the code do what the spec asked for? Are edge cases covered? Is the implementation consistent with the architecture of the system as a whole?
What human review is NOT
Human review is not the same as running the tests and approving if they pass. Tests validate known behavior; human review validates:
- Consistency with the system: whether the agent introduced logic that works locally but breaks invariants of the wider system.
- Maintainability: whether the code is understandable and modifiable by the team, or whether it is a functional maze.
- The cases the tests don't cover: behaviors in production that the test environment doesn't simulate.
Automation bias is the specific risk of this practice: the tendency to trust the output of an automated system more than is warranted. Effective human review requires reviewers to exercise their own judgement, not merely validate what already looks correct.
Reference ratio
Scrum Manager's CheatSheetScrumIA guide sets a human-review-time-per-AI-task ratio of 1:3 to 1:5 as a health indicator. If reviewing an AI-generated task consistently takes more than a third of the time it took to generate, it is a sign that either the specs are not precise enough or the bottleneck lies in the team's review capacity.
AI Supervising AI as a Complement
Automated review by another agent can cover some of the mechanical checks (static analysis, duplication patterns, and known vulnerabilities), freeing the human reviewer to focus on business logic and system coherence. This mechanism complements human review; it does not replace it.
Common mistake
Delegating the review to "if it compiles and the tests pass, it's fine." AI produces code that compiles and often passes the tests but contains subtle errors in the business logic, hidden duplication, or security vulnerabilities that only human context can detect. Effective review requires someone to read the code, understand what it does, and validate that it does what it should, not just that the CI system accepts it.
Resources
🏦 Scrum en equipos con IA [Scrum in Teams with AI; in Spanish]Skill Arena · Scrum Manager
📊 Guía didáctica Scrum en equipos con IA [Teaching Guide: Scrum in Teams with AI; in Spanish]Resources · Scrum Manager
See also
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