The problem is the comments only make sense if you knew the original prompt in the first place. It'd be one thing if AI left a bunch of breadcrumbs reminding it

The problem is the comments only make sense if you knew the original prompt in the first place. It'd be one thing if AI left a bunch of breadcrumbs reminding itself not do do Y, but when it makes a comment that only makes sense if you know about the original requirement not to do Y, to me it's more likely to trip up future agents than it is to help them. A recent example I saw was an agent leaving a comment "// return an error here instead of panicking, as a panic will abort the process". Because likely the original human in the loop caught the AI putting a panic in there and told them not to, and then the comment to not do panics was placed in there. But to a future agent, it'll see that and think "ok, this comment must be here because we usually do use panics instead of returning errors, this place must be an exception", and now its context window is primed to think of using panics first. It's pretty well-documented at this point that spending a lot of tokens explaining what not to do can actually increase the likelihood of an LLM doing that thing, especially when its context window is getting full. It's like if you go to the grocery store and see a sign saying "Vegan tomatoes". It sounds fine until you think "wait, aren't all tomatoes vegan?" and now you start doubting yourself and start imagining what a non-vegan tomato would be. Another semi-related issue is the tendency for LLM's to make up their own dumb little short-hand words for things that it has been talking about over and over in the context window. "The lock that prevents a user from being deleted while another thread is updating it" becomes a "user-fence", and now there's comments saying "// this function returns a user-fence", and I have absolutely no idea what that's supposed to even mean.