The issue I see is that this is pretty much the final boss for AI systems. Not because the tasks to do are inherently too difficult or whatever, but the integration of data and quality of that data is so variable that you just can't get something done reliably.

Compare this to codebase AI, where much of the data you need lies in your codebase or repo. Even then, most of these coding tools aren't even close to automating meaningful coding tasks in practice, and while that doesn't mean they can't in the future, it's a long ways off!

Now in the ops world, there's little to no guarantee that you'll have relevant diagnostic data coming out of a system that you need to diagnose it. That weird way you're using kafka right now? The reason for it is told via oral tradition on the team. Runbooks? Oh, those things that we don't bother looking at since they're out of date? ...and so on.

The challenge here is in effective collection of quality data and context, not the AI models, and that's precisely what's so hard about operations engineering in the first place.