I’ve been reading about autonomous AI teams on Latenode, and I’m trying to understand if the added complexity is worth it.
Basically, the idea is: one agent handles document retrieval, another handles response generation. They work independently, coordinate somehow, and supposedly this is better than having one AI model do both retrieval and generation.
On one hand, separation of concerns makes intuitive sense. Retrieval and generation are different problems. An agent optimized for finding relevant documents might be terrible at synthesizing them into coherent answers.
On the other hand, I’m looking at the added orchestration overhead. You need to manage communication between agents, handle failures independently for each part, debug across multiple decision-making points instead of one. That feels like it could get messy fast.
The docs mention that autonomous AI teams can implement RAG at scale with retrieval and generation teams. But what does “at scale” actually mean? Is this for massive enterprises processing thousands of documents daily? Or is this genuinely useful for a ten-person startup trying to automate their support workflow?
Has anyone actually built this with separate retrieval and generation agents? Does it actually perform better, or are you just paying complexity cost for marginal improvements?