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

In this session, Nnenna Ndukwe, Developer Relations Lead at Qodo, walks through what it actually takes to govern AI-generated code at scale, from defining standards to enforcing them consistently across teams, repos, and tools.

Topics covered:

Why AI-generated code is a quality problem right now. AI has accelerated code output, but review and validation processes haven’t kept pace. In a live poll, 71% of attendees said they aren’t measuring the impact of AI on code quality. The incidents making headlines aren’t future risks. They’re happening now, and most teams don’t have the systems in place to catch them.

The context engineering problem at scale. Agents.md, internal docs, engineering standards, review criteria: it’s all context. Managing that context works for a solo developer. For distributed teams across multiple repos, it fragments fast, leading to inconsistent standards, architectural drift, and reviews that miss what matters.

Rules as dynamic, self-managing standards. Static configs and manual rule management don’t scale with team growth or AI-assisted development. Qodo’s Rules System auto-discovers patterns from your codebase and PR history, surfaces rule suggestions, tracks adoption and violations, and evolves as your standards do. Less overhead. More enforcement.

Closing the loop between standards and review. When rules feed the review and the review feeds the rules, quality compounds over time. The result: faster reviews, higher-signal findings, and code that’s actually production-ready, not just AI-generated.