I’ve been reading about autonomous AI teams in Latenode and the idea of having a Retriever agent and a Generator agent working together on RAG sounds cool on paper. But I’m skeptical about whether it actually works.
Like, theoretically, you’d have one agent that fetches relevant information from your data sources, and another that takes that output and crafts a response. The context I found mentions autonomous decision making, multi-step reasoning, and learning and adaptation. That all sounds great.
But in practice, how do you handle the handoff between them? What happens when the retriever pulls back too much information or the wrong documents? Does the generator just work with what it gets, or can the agents actually communicate back and forth?
And the learning and adaptation part—does that mean the agents actually improve over time? Or is that just language marketing?
I’m genuinely curious if anyone has built a multi-agent RAG system that actually felt like the agents were doing real work instead of just running predetermined steps in sequence.
This is where it gets interesting. I set up a multi-agent RAG system and at first I was skeptical too. But it actually works better than I expected.
Here’s what made it click for me: the agents aren’t magical. They’re still following logic you define. But the autonomy part means each agent can make decisions about how to execute its job based on what the previous agent output.
So the Retriever agent can be configured to fetch documents, evaluate them against relevance criteria, and decide whether it needs to run another search if the first one didn’t find enough. The Generator agent then takes that and can decide things like whether to acknowledge sources, ask clarifying questions back to the user, or flag when the retrieved context doesn’t actually answer the question.
The handoff works because Latenode orchestrates the data flow between them. You set the contract of what each agent passes to the next. The learning and adaptation is real but subtle—you monitor how your agents perform and adjust their prompts or decision criteria over time.
I had a scenario where the retriever kept missing context that was spread across multiple documents. Instead of each agent being dumb, I configured the retriever to fetch wider, and the generator to synthesize across multiple sources. Once I tuned those prompts, the system got noticeably better.
The key is not expecting magic. Build the agents with clear responsibilities, give them decision points, and monitor what actually happens. Latenode makes the orchestration invisible, which is nice.