Your Code Isn’t AI-Ready (Yet): Fixing the Complexity That Breaks AI Agents

AI agents don’t struggle with syntax. They struggle with missing intent, non-expressive code, and surprising dependencies. Historically, we were supposed to write code for human readers, code that fits our cognitive limits and supports collaboration. In reality, much of our industry has fallen short.

That comes back to bite us.

When AI agents enter the development loop, they amplify those same problems. Where a human developer will ask questions and seek clarification, an AI often proceeds without it, making its best guess from patterns in code that was never designed to be unambiguous.

Code that is hard for humans to understand becomes unreliable for AI.

In this talk, Adam Tornhill shows how to turn that around. You’ll learn the key principles behind AI-friendly code and apply practical AI-assisted refactoring patterns that make those principles concrete. The focus is not on generating more code, but on improving the code you already have so AI becomes reliable instead of risky. All recommendations are grounded in AI research and cognitive psychology.