AI-assisted code review analyzes code changes and suggests improvements, catching issues human reviewers might miss. Capabilities: Style consistency, bug detection, security vulnerabilities, performance issues, documentation gaps, code smell detection, best practice enforcement. Integration: GitHub PR comments, GitLab merge request bots, IDE plugins, CI/CD pipeline integration. Workflow: Developer opens PR, AI analyzer runs, comments posted with suggestions, developer addresses or dismisses. Tools: CodeRabbit, Sourcery, Amazon CodeGuru, DeepCode, PR-Agent, custom LLM integrations. Review aspects: Correctness, readability, maintainability, security, test coverage, documentation. LLM-based review: Understands context and intent, can explain suggestions, handles novel patterns. Limitations: May miss domain-specific issues, cannot fully replace human judgment on design decisions, false positives. Complementing human review: AI handles mechanical checks, humans focus on architecture and design. Speeds up review cycle. Customization: Configure rules per codebase, train on team conventions, adjust verbosity. Use as first pass before human review.