When it comes to n8n, the question on everyone’s mind is: does it finally support real multi-agents with the new Agent Tool? The short answer is: not exactly. But it’s getting close.
The Agent Tool is a significant step towards making n8n a more comprehensive automation tool. With it, you can orchestrate agents inside a single workflow, achieving solid results in production. The key is understanding the tool-calling loop and designing the flow well.
The n8n AI Agent works like a Tools Agent, reasoning in iterations, choosing which tool to call, passing the minimum parameters, observing the output, and planning the next step. This allows you to mount other agents as tools inside the same workflow and adds native controls like System Message, Max Iterations, Return intermediate steps, and Batch processing.
So, what’s the difference between an orchestrator and an agent? An orchestrator decides and coordinates, owning the data flow and sending each specialist the minimum useful context. The execution plan lives outside the prompt and advances as a checklist. Sequential or parallel decisions are made based on dependencies, cost, and latency.
In my experience, using an orchestrator as the root agent and mounting specialists via AI Agent as Tool has led to significant improvements. I’ve seen session tokens drop by 38% and latency fall by 27%. Context limit cutoffs have decreased from 4.1% to 0.6%, and correct tool use has risen from 72% to 92%.
But what works and what doesn’t? Parallelism with Agent as Tool can be inconsistent, and deep nesting can become fragile. When I need robust parallelism, I combine batches and parallel sub-workflows, keeping the orchestrator light.
So, when should you use each approach? AI Agent as Tool in a single workflow is ideal for speed, low context friction, and native controls. Sub-workflows with an AI Agent inside are better suited for reuse, versioning, and isolation of memory or CPU.
n8n may not be a perfect multi-agent framework yet, but the Agent Tool is a significant step in the right direction. By understanding the tool-calling loop, persisting the plan, minimizing context per call, and choosing wisely between sequential and parallel, you can unlock the full potential of n8n.
How well is parallel working in your stack, and how deep can you nest before it turns fragile?