I was previously working at https://autonomy.computer, and building out a platform for autonomous products (i.e., agents) there. I started to observe a similar opportunity. We had an actor-based approach to concurrency that meant it was super cheap performance-wise to spin up a new agent. _That_ in turn meant a lot of problems could suddenly become embarrassingly parallel, and that rather than pre-computing/caching a bunch of stuff into a RAG system you could process whatever you needed in a just-in-time approach. List all the documents you've got, spawn a few thousand agents and give each a single document to process, aggregate/filter the relevant answers when they come back.

Obviously that's not the optimal approach for every use case, but there's a lot where IMO it was better. In particular I was hoping to spend more time exploring it in an enterprise context where you've got complicated sharing and permission models to take into consideration. If you have agents simply passing through the permission of the user executing the search whatever you get back is automatically constrained to only the things they had access to in that moment. As opposed to other approaches where you're storing a representation of data in one place, and then trying to work out the intersection of permissions from one of more other systems, and sanitise the results on the way out. Always seemed messy and fraught with problems and the risk of leaking something you shouldn't.