AI made code generation cheaper. It made software accountability more important. AI Code Governance gives engineering leaders a practical operating system for controlling how AI-assisted code is specified, generated, reviewed, tested, traced, approved, and released. Learn how to preserve provenance, build task-to-code evidence chains, review agent-generated pull requests, strengthen test quality and CI/CD guardrails, govern MCP and tool permissions, manage security and licensing risk, define proportional approval policies, and measure whether AI adoption is actually improving delivery. Use the included checklists, templates, matrices, maturity model, and production playbooks to move from faster code generation to reliable software delivery.