Code Generate → Human Review → Quality Gate → Deploy
Create a structured review process for AI-generated code that requires human approval and quality checks before deployment.
Workflow Steps
GitHub Actions
Detect AI-generated code
Set up workflow that analyzes commit patterns and code characteristics to identify likely AI-generated code submissions using comment patterns and code style analysis.
GitHub
Require enhanced review
Automatically apply 'AI-generated' labels and require additional reviewers for pull requests containing AI code, enforcing stricter approval rules.
SonarQube
Run extended quality checks
Execute comprehensive code analysis including security scans, complexity metrics, and test coverage requirements specifically for AI-flagged code.
Linear
Track technical debt
Create tracking tickets for any identified issues, linking them to the original PR and assigning appropriate priority levels for future cleanup.
GitHub Actions
Gate deployment
Block automatic deployments until all quality checks pass and required human approvals are obtained, ensuring code meets standards before going live.
Workflow Flow
Step 1
GitHub Actions
Detect AI-generated code
Step 2
GitHub
Require enhanced review
Step 3
SonarQube
Run extended quality checks
Step 4
Linear
Track technical debt
Step 5
GitHub Actions
Gate deployment
Why This Works
Creates systematic quality gates that catch issues early while still allowing teams to benefit from AI assistance, balancing speed with reliability.
Best For
Development teams using AI coding assistants who need to maintain code quality and reduce the risk of expensive rewrites
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