I've written about some hierarchical manager system with some friends when exploring how to use AI for larger set tasks. While the easy answer is simply using something with much larger context - `Claude` is amazing with an API key if you're on the waitlist - we definitely followed the same idea of splitting up the context into individual groups.

We had some success actually with layering another AI into the mix - having one AI look at a summary version of the context as a whole, and decide which pieces of context to assign to each manager. This of course requires a sidestep into another database of some kind to store the "master context" (AKA the full conversation, so you likely already have it in some form of storage), and of course a lot more calls to the AI which overall increases latency quite a bit.

1. Use an AI to provide a short summary of each piece of logical context and map it by access ID 2. Use another AI to determine which pieces contain the most useful additional context to the piece of the task being evaluated 3. Build the context from the generated ID list and pass to individual task manager AI