I’ve been thinking about this problem: customer questions are getting more complex, and a single AI model sometimes isn’t enough to give a really good answer. You need someone to fetch the information, someone else to synthesize it, maybe a third to validate it. That’s essentially what autonomous teams do.
I was skeptical that you could actually build multi-agent RAG workflows without writing code. But I spent time experimenting with Latenode’s autonomous AI teams feature, and it’s genuinely different from what I expected.
Here’s how it works in practice: I created two agents. Agent 1 is the retriever—it takes a customer question, hits the knowledge base, and pulls back relevant documents. Agent 2 is the responder—it takes those documents plus the original question and generates a comprehensive answer. Both agents have their own prompts, their own model assignments, even their own error handling. They communicate through the workflow.
What blew my mind was doing this entirely in the visual builder. No Python scripts, no orchestration code. Just dropping in agent nodes, configuring their behavior, and connecting them with data flows.
The real challenge I ran into was prompt tuning. Each agent needs its own optimized instructions. The retriever needs to be aggressive about finding relevant docs. The responder needs to know how to synthesize that context clearly.
Has anyone else built multi-agent RAG setups? How do you decide what each agent should do? And how do you tune their interaction without it becoming a nightmare?