AI can produce code faster than most teams can safely review it. That changes the bottleneck. The hard part is no longer getting a change written, it is proving the change is correct, safe, and ready to ship.

In this fast-paced, demo-driven session, I’ll take one legacy workflow through a practical safety pipeline. We’ll capture its current behavior, create characterization tests, and use GitHub Copilot to make small, targeted refactors instead of one risky rewrite. After each change, we’ll compile, test, inspect the diff, and create a rollback-friendly checkpoint.

Then we’ll move beyond code-level tests. You’ll see GitHub Copilot and Playwright exercise the real user journey, capture traces and screenshots, and uncover regressions that unit tests can miss. We’ll finish with a GitHub Copilot-assisted pull request review that challenges the change for defects, security risks, edge cases, and missing coverage.

This is not a session about trusting AI-generated code. It is a repeatable way to demand evidence from it, while keeping a human responsible for the final release decision.

Takeaways
- Constrain AI changes to small, reviewable diffs.
- Prove behavior with tests and Playwright.
- Keep humans responsible for the final merge.