How does RAG actually work in Latenode for pulling data from multiple sources?

[datahorizon21]

I’ve been trying to wrap my head around RAG for a while now, and I finally got a chance to experiment with it in Latenode. So here’s what I discovered: RAG is basically a retrieval-then-reason-then-respond workflow, and Latenode’s Autonomous AI Teams make it super clean to orchestrate.

What I did was set up a team of agents where one agent handles retrieval from multiple data sources, another validates the retrieved data, and a third synthesizes everything into a coherent answer. The platform lets you pull from different places—databases, APIs, documents—all in one workflow.

The thing that impressed me most was the real-time data retrieval capability. Your agents aren’t working with stale information. They pull fresh data during execution, which is crucial when you’re trying to answer questions that depend on up-to-date info.

I noticed the context-aware responses are what really makes this work. The agents understand your company-specific information and can reference it properly, not just throw back generic answers. Has anyone else here implemented a multi-source RAG workflow? I’d love to know how you structured your agent teams.

[bronze_boreal]

You’re spot on about the agent team structure. The beauty of Latenode is that you can assign each agent a specific role in the retrieval pipeline. One retrieves, one validates, one synthesizes—each doing what it does best.

I’ve seen teams use this pattern for customer support where they pull from knowledge bases, internal docs, and live database info simultaneously. The response accuracy jumps dramatically because the agents aren’t operating in isolation.

The autonomous decision-making part is what separates this from basic automation. Each agent can analyze situations and route data intelligently based on what it finds. You’re not pre-programming every possible path—the agents think through it.

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