AI is accelerating how code gets written, but it’s also widening the gap between specs and production-ready implementation. The result is both velocity and hidden risks. This talk reframes code quality as a living lifecycle instead of a static checkpoint.

We’ll explore how a “code review lifecycle” approach can transform pull requests into continuous feedback loops that evolve with your team’s standards, architecture, and best practices. You’ll learn how to close the “last mile” gap in AI-generated code, embed quality checks across the SDLC, and turn review findings into one-click fixes.

By the end, you’ll have a practical playbook for making code review the backbone of AI-native development to make sure speed and quality move forward together.