AI-powered code review can catch issues before human reviewers. Architecture: PR trigger -> fetch diff -> static analysis (ESLint/Semgrep) + AI review -> post inline comments. GitHub Actions workflow: use pull_request trigger, fetch diff with git diff, pass to AI with structured JSON output prompt requesting critical/improvement/style categories with file+line+suggestion. Use Octokit to post inline review comments and create review summary. Security-focused prompts should check for injection vulnerabilities, auth flaws, sensitive data exposure, and cryptographic weaknesses with CWE IDs. Best practices: chunk large PRs, cache reviews for unchanged files, set confidence thresholds to reduce false positives.