You tried the latest coding model on your repo and got low-quality changes you’d never merge. The problem is often not the model, it’s the codebase context.

Not every repository is friendly to agentic engineers, just like not every repository is friendly to humans. This session covers practical context engineering techniques to make AI output more reliable: clearer module boundaries, better naming, task-oriented documentation, decision records, and machine-readable conventions that reduce ambiguity.

We’ll walk through before/after examples showing how small structural changes improve edit quality, reduce unsafe cross-cutting diffs, and make code review faster. By the end, you’ll leave with a concrete checklist to audit your own codebase and incrementally optimize it for high-quality AI collaboration.