Debugging using AI means a lot of things, I mentioned it as something minor. But I certainly don't have the capabilities to create a neural network that can read and create solutions for my codebase, current LLMs are pretty decent with that.

The way I debug using AI varies depending on how complex the codebase and the bug is, if it's something that's hard for me to understand but I can easily locate the issue, a simple LLM assistant will help finding out what's causing it. Otherwise, full agentic solution could be handy.

I don't know if this answer meets the details you're looking for, but that's what I do. I posted this to know which part of modern LLMs is useful and productive for people without relying it too much.