Modern AI systems are incredibly capable and confidently wrong.

In this talk, we explore a new architectural pattern: AI systems that argue with themselves. By orchestrating multiple AI agents with opposing perspectives, we can uncover hidden bugs, reduce hallucinations, and dramatically improve output quality without adding human reviewers to the loop.

I’ll demonstrate how to design and implement a self-debating AI system using real-world examples: debugging code, validating architectural decisions, and stress-testing product requirements. We’ll explore when AI disagreement is useful, when it fails, and how to measure improvement beyond “it feels better.”