AI can generate code in seconds. We can create features, tests, and pull requests faster than ever. But if code is now easy to produce, what is the real bottleneck?

Many teams are generating more code than ever with tools like Copilot, Claude Code, Cursor, and other AI agents. The hard part is no longer writing the code. The hard part is knowing if the code is actually good. Is it secure, performant, and maintainable? Does it fit the architecture of the system? AI can generate a lot of output, but without the right context and validation it can also introduce risk.

In this session we’ll look at how teams can shift their focus from generation to validation. We’ll talk about testing, good architecture, and simple validation patterns teams can use today. You’ll leave with practical ideas for making sure the code we generate is actually ready for production.