Build Multi‑Agent Systems with n8n: Orchestrating Reliable AI Workflows

Multi-agent AI systems are becoming a critical capability as organizations move from experimenting with LLM workflows to running them in production. When a task is too complex for one agent, multiple specialized agents working together can get the job done.

In this course, instructor Tobias Zwingmann teaches you how to build multi-agent systems in n8n. Starting from a real project on day one, learn to design an orchestrator agent that delegates to specialized sub-agents, wire up shared memory and tools, and implement sequential and parallel coordination patterns. Explore the essential theory along the way: why multi-agent architectures exist, when to use them, and how to avoid pitfalls like context loss and runaway loops. Then, add production-grade features such as human-in-the-loop approval and fallback handling. Through screencap demos, node-by-node builds, and text-based challenges, you’ll build a complete deep research system on the n8n canvas.

Learning objectives
Design and build a multi-agent research system using search, critique, and orchestrator agents.
Run sequential and autonomous agent loops, applying split/merge, conditional routing, and safe stopping criteria.
Configure memory (buffer and vector store) and use it to generate a complete research report.
Deploy and monitor the workflow using n8n’s execution history, activation, and oversight nodes.