Beyond Code Review: Building a Governance Layer for AI-Generated Code

AI has removed the ceiling on how fast teams can write code. It hasn’t removed the consequences of shipping code that breaks things.

The question every engineering leader is facing right now isn’t whether to build with AI, it’s whether they can trust what AI builds. And the honest answer, for most teams today, is: not completely.

Join us for a practical session on what it actually takes to establish trust and governance over AI-generated code in production without slowing down the teams producing it.

What You’ll Learn:

• Defining trustworthy code in the AI era: What quality means when AI is writing the majority of your output across maintainability, reliability, compliance, testability, and security, and why yesterday’s standards aren’t sufficient.
• From tribal knowledge to enforced standards: How to codify the implicit standards living in your best engineers’ heads into a governance system that enforces them automatically, across every repo and every team.
• Governance as a velocity enabler: How automated quality gates free senior engineers from syntax and pattern reviews so human judgment is applied where it actually matters: architecture, design, and business logic.
• Context-aware enforcement: How deep codebase context and codified organizational standards work together, so AI-driven review feels like a senior engineer who knows your codebase, not a generic linter.
• Continuous standards evolution: How a living governance system learns from developer behavior and review decisions so your standards stay current as your codebase and team evolve.