AI-assisted development doesn’t just change how we write code, it exposes how well (or poorly) our systems are structured.

In many teams, AI struggles not because the tools are weak, but because the architecture is unclear: implicit domain knowledge, blurred boundaries, and inconsistent patterns.

Having worked on introducing AI-assisted workflows in a 5,000+ engineer organization and later designing an AI-native engineering setup from scratch, I’ve seen a consistent pattern: AI effectiveness is directly proportional to architectural clarity.

In this session, we’ll explore:
- Why documentation is more important than ever
- Why bounded contexts matter more than ever
- How domain models influence AI output quality
- Making architectural constraints explicit
- Reducing ambiguity in large codebases
- Practical refactoring patterns to increase AI leverage

This talk is for architects who want to design systems that are not only maintainable by humans, but understandable and usable by AI.