I’m working on something more ambitious now. We need to build a system where multiple AI agents work together on a RAG pipeline. One agent retrieves documents, another processes and summarizes them, and a third generates answers. All of this needs to happen reliably at scale without manual intervention.
The challenge I’m running into is that these steps are interconnected. If retrieval fails silently, downstream processing becomes garbage. If summarization is off, the final answer is compromised. And coordinating all of this across different AI models while keeping costs reasonable feels like juggling.
I’ve been reading about autonomous AI teams, where agents can hand off work to each other and make decisions based on what they encounter. That seems like it could solve for complexity, but I’m not sure how to actually set that up in a no-code environment.
Has anyone scaled RAG beyond a single retrieval-and-answer workflow? How did you handle the orchestration between multiple steps and multiple AI agents?
Orchestrating multiple agents is where things get interesting. The old way—writing custom code to manage handoffs and error handling—is painful. I’ve been there.
With Latenode, you define autonomous AI teams where each agent has a specific role. One retrieves, one processes, one generates. They communicate through the platform, so you don’t need to write integrations between them. Set rules for how they hand off work, what happens if something fails, and they run independently.
What makes this work at scale is the visual builder. You see the entire workflow—all the agents, all the handoffs, all the error paths—in one place. When something breaks, you know exactly where.
And the multi-agent capability is designed specifically for enterprise tasks. The CIA can be your retrieval agent, focused on speed and relevance. The analyst can process results. The communicator generates the final response. Each specialized, each optimized for one job.
The platform also handles the heavy lifting: model selection across 400+ options, execution monitoring, cost tracking per agent, all built in.
I built something similar for document processing at a financial firm. The coordination piece was harder than I expected. We needed retrieval to pull specific contract clauses, a second agent to check them against compliance rules, then a third to generate a summary for review.
The breakthrough was treating each agent as having a single responsibility with clear input and output contracts. Agent A must output exactly this format so Agent B knows what to expect. That standardization made error handling and debugging so much easier.
We also added checkpoints. After retrieval, we validate that we got actual documents. After processing, we validate the output format. These small checks prevented cascading failures. Without them, bad data from retrieval would snowball through the pipeline.
Monitoring is another thing I’d recommend building in from the start. We track metrics at each stage: retrieval accuracy, processing time, generation quality. When performance degrades, we know immediately which agent is the bottleneck. Without that visibility, you’re flying blind.
Enterprise RAG pipelines need proper error handling and fallback strategies. I implemented a tiered approach: primary retrieval from the main knowledge base, secondary retrieval from a backup source if the first fails, and a human escalation path if both fail. The orchestration layer manages all these branches. Using autonomous agents helped because each agent could make local decisions—retry, escalate, or try an alternative approach—without requiring centralized control. That resilience is critical when you’re processing thousands of queries daily.
Scaling RAG requires decoupling concerns. Your retrieval agent shouldn’t care about summarization; your summarizer shouldn’t worry about how the final answer gets formatted. I’ve seen teams fail because they built everything as one monolithic workflow. When you split it into specialized agents with clear interfaces, scaling becomes manageable. The hand-off between agents also gives you observation points. You can measure latency, accuracy, and cost at each stage independently. This visibility is invaluable for optimization.