That matches what I'm curious about. Where an LLM is doing the bulk of information discovery and tool calling directly. Most simpler RAGs have an LLM on the frontend mostly just doing simpler query clean up, subqueries and taxonomy, then again later to rerank and parse the data. So I'd imagine the prompting and guardrails part is much more complicated in an agent loop approach, since it's more powerful and open ended.