Quality Gates

Part 4: Building a Multi-Agent AI Code Review Pipeline

← Previous Chapter: Part 3: The AI Bug Taxonomy | Series Hub | Next Chapter: Part 5: AI Code Security → Answer-first: A multi-agent PR review pipeline deploys 3 specialized LLM agents in parallel: (1) Security Agent (OWASP vulnerabilities), (2) Architecture Agent (DDD layer boundary compliance), and (3) Performance Agent (SQL queries, memory allocations).

Part 5: Agent Evals: Trajectory Validation & Automated Benchmarking

← Previous Chapter: Part 4: AgentOps | Series Hub | Next Chapter: Part 6: Human-in-the-Loop Gateways → Answer-first: Traditional single-turn evaluation metrics (BLEU, ROUGE) are useless for multi-step agents. Production eval pipelines evaluate Trajectory Efficiency (minimum tool steps to completion), State Invariant Compliance, and Negative Constraint Enforcement.

Part 3B: AI Code Review & Automated Quality Gates in CI/CD

← Previous Chapter: Part 3B: AI Automation for Internal Ops | Series Hub | Next Chapter: Part 4: Legacy Refactoring → Answer-first: Automated AI review gates enforce non-negotiable architectural standards (zero circular imports, mandatory unit test coverage for new endpoints, type-safety) before human reviewers even open the pull request.