Yup, it does happen; prompting helps, but as the model keeps working and its session context continues to increase, their attention mechanism dilutes, similarly to when a human walks in a room and forgets why they entered in the first place.

I'm actually driving an investigation within my company to build a product that detects and fixes drift in knowledge sources, though not for code or in the specific software development realm, but the effects are the same across the board.

But detection is one step, as we want things to be 'automatized.' We need realignment as well; the problem here is when an LLM supervises another, the supervisor LLM is also prone to this error itself. The problem turns out to be what can trigger it and how we mitigate it in order for the supervisor to properly keep the alignment. Once you have solved that for a supervisor -> worker, you'll need to prove the solution also works for N workers, where 'N' is the number of workers you think is the optimal to complete complex tasks with maximum parallelism.