AI assistance is increasing the pace and volume of code changes. That creates a practical quality problem: code can follow common industry patterns and still miss the standards your organization relies on, such as architecture boundaries, testing expectations, observability, security practices, or product conventions. This session looks at how to make those standards visible and usable across the development workflow. The goal is not to replace engineering judgment with a score or a bot. It is to decide which standards should be automated, which should be reviewed by humans, and which parts of quality depend on the team’s taste and context. You will leave with a practical model for combining Copilot, Copilot code review, and GitHub Code Quality so AI-generated and human-written code are held to the same expectations: easier to maintain, easier to trust, and more consistent with how your organization actually builds software.