code-review

Automated code review for AI-generated code applies a layered pipeline to pull requests, running linting, static analysis, and AI-assisted checks to catch hallucinated APIs and unsafe dependencies before merging.

A layered code review pipeline sequentially applies automated checks, AI-assisted first-pass reviews, and human design evaluations to catch issues in complex codebases. You need it when merging AI-generated code requires safety, maintainability, and advocacy checks.

Static analysis and linting serve as the first automated layer to catch formatting and syntax issues, while a subsequent AI-specific failure pattern review layer targets hallucinated APIs, DRY violations, and unsafe dependencies in AI-generated code.

This code review pipeline includes security scanning in its automated checks layer and dedicates a final advocacy-specific review layer to evaluate data privacy, safety, and boundary considerations for the submitted code.

The best way to check Ousterhout red flags is through the human design quality review layer, which specifically evaluates maintainability concerns and design red flags after automated and AI-assisted checks pass.

AI-assisted code review needs a separate failure pattern layer because AI-generated code frequently introduces subtle issues like DRY violations, hallucinated APIs, and unsafe dependencies that standard static analysis and linting often miss.