To summarize, agents are (essentially) LLMs in a loop: take actions, think, plan, etc, then repeat. Currently, from what I've seen, current LLMs "diverge" when

To summarize, agents are (essentially) LLMs in a loop: take actions, think, plan, etc, then repeat. Currently, from what I've seen, current LLMs "diverge" when put into a loop. They seem to reason acceptably in small chunks, but when you string the chunks together, they go off the rails and don't recover. Can you slap another layer of LLM on top to explicitly recover? People have tried this, it seems like nobody has figured out the error correction needed to get it to converge well. My personal opinion is that this is the measure of whether we have AGI or not. When LLM-in-a-loop converges, self-corrects, etc, then we're there. It's likely all current agent code out there is just fine, and when you plug in a smart enough LLM it'll just work.