I’ve been wrestling with this for a while now. We built a customer support workflow that uses RAG to pull from our docs, but the problem is obvious—docs get updated, and suddenly the system is pulling outdated information. It’s frustrating because the retrieval part works great until the knowledge base shifts.
I found out that the real issue isn’t just about reindexing. It’s about having the system pull live data during execution instead of relying on static snapshots. Someone mentioned that Latenode’s AI Copilot can generate workflows that handle real-time data retrieval, which means the retriever actually grabs current information when a query comes in, not from some stale index.
The other thing I’m curious about is whether you can build a workflow where multiple AI agents stay coordinated—like one agent that handles retrieval and another that verifies freshness. Has anyone actually set this up? I’m wondering if the overhead is worth the accuracy gain, or if there’s a simpler pattern I’m missing.
The way to solve this is to build your retrieval step to execute in real time, not batch. You set up your workflow so the retriever pulls current docs every time a query hits, and the generator works with fresh context immediately.
With Latenode, you can chain an AI node that retrieves from your knowledge base with another AI node that generates responses right after. The AI Copilot can generate both steps at once when you describe what you want in plain English. The key is making the retrieval part execute live, not relying on a static index.
For coordination between multiple retrieval and generation steps, you can use Autonomous AI Teams to handle agent orchestration. Each agent stays focused on its role—one retrieves, one validates freshness, one generates—and they work together in sequence.