The rise of large language models and autonomous agents has turned some familiar software-design processes upside down. In this talk we’ll examine the idea that instead of a single AI assistant, you could have multiple agents debating architecture - challenging one another, surfacing assumptions, exposing trade-offs, and helping drive better decisions. Building on the ideas presented in “MAD About Software Design: When AI Debates”

We’ll cover three major areas:
1. Why this matters: Scaling software development is more than scaling the actual coding itself. We already look at areas of writing specifications, testing, coding and deploying with AI-driven agents. Software design is inherently a human task. But can we also scale that with AI?

2. How the “multi-agent debate” model works: Agents with different roles (e.g., “performance champion”, “security skeptic”, “maintenance guardian”) argue and negotiate design proposals. We’ll walk through what such a system can look like, its pitfalls, and how it differs from just “AI writes code for you”.

3. Lessons learned from real-world evaluation & implementation: Abstract ideas are fun, but it’s not enough for real-world impact. We’ll talk about implementation, experiments, failures, and learnings.

4. Practical guidance for your team: We’ll talk about practical implementation, and where this pattern and relevant tooling fits in the larger scheme of SDLC. What to look out for, and where the humans fit in all of this.