Hi everyone. I’m learning about AI agents at the same time as I’m learning about N8N. I already have two agents created that are chatbots, and each one uses some tools, and there are a few more on the roadmap, and I realized that wasn’t the best way to do it. So I’m researching how to make a multi-tasking agent. Initially, I was going to make a single agent connected to several tools that would be sub-workflows of the tools I already have created.
I asked for a suggestion on the Google forum and a Gemini expert suggested making a routing agent, which directs the user’s message to the next agent.
I liked the idea, but I came across this topic here on the forum and found the approach used very interesting:
Considering that in the future my agent may have many functionalities (RAG, payments, simple questions, opening tickets, scheduling meetings, etc.), what would be a good path to follow?
For a growing set of tasks it’s usually better not to put every tool and capability into one big generalist agent. A single agent often becomes unreliable and unpredictable when it’s asked to handle many different jobs.
A better approach as your needs grow (RAG, tickets, payments, scheduling, etc) is to break the logic into multiple focused agents that each do one job well. There are two simple patterns that work in n8n:
1. Routing pattern
Have a lightweight classifier first, then use a Switch node to send the request to a sub-workflow with a specialist agent for that category (e.g., support, scheduling, billing). This keeps responsibilities clear and makes the system more predictable.
2. Orchestrator agent pattern
Have one “manager” AI agent whose job is to decide which sub-agent to call, and then to hand off work to those sub-agents (each in their own workflow). That keeps the core decisioning separate from the task execution.
Both approaches avoid overloading a single agent with everything, and they make it easier to debug and scale as your system grows.
Hi @Anshul_Namdev , thank you for your willingness to help.
Okay, the routing approach is the same one suggested to me and I’m tending to follow it, but for learning purposes, I couldn’t see the difference between the two patterns you indicated. Are they really very similar or did I miss something?
@Facchioli They are similar in that both patterns help you scale beyond one agent, but the core difference is who makes the routing decision.
Routing pattern
You use a classifier (or simple logic) to decide the category of the request and then a Switch node sends it to the right sub-workflow with a specialist agent. Each agent runs independently and the workflow structure drives which one gets called. This is straightforward and predictable.
Orchestrator agent pattern
You have one main AI agent whose job is to decide at runtime which specialist agents to call by using AI Agent Tool nodes to invoke them. Here the AI itself does the task dispatching instead of the workflow structure. This lets the agent make more dynamic decisions inside its logic.
