Course overview
Multi-agent programming is reshaping how organizations automate complex work by coordinating teams of specialized AI agents that can plan, reason, use tools, and hand off tasks to one another. This course provides a hands-on introduction to designing and deploying multi-agent systems using n8n, the popular open-source workflow automation platform and its built-in, LangChain-powered AI Agent nodes. Participants will learn how to build agents that connect to language models, memory, tools, and data sources, then orchestrate several agents together to automate real-world business processes.
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Designed for developers, automation specialists, and AI professionals, this course equips learners to build reliable multi-agent workflows on a single n8n canvas: scoping each agent to a clear responsibility, wiring up an orchestrator that delegates to specialist agents, validating and structuring agent outputs, and exposing or consuming tools through the Model Context Protocol (MCP). By the end, participants will understand how to apply multi-agent automation to practical use cases—research, content production, data processing, and customer-facing workflows—while addressing key reliability, ethical, and security considerations.
Note: As n8n and the broader agentic AI ecosystem evolve, the course content will be updated periodically to reflect new nodes, features, and best practices.
Prerequisites
Basic knowledge of Generative AI and large language models is required. Familiarity with APIs and basic automation concepts is recommended, but not required.
Course outline
Understanding agentic automation and its role in modern workflows
How AI agents differ from traditional, linear automations
Real-world examples of multi-agent systems in business workflows
Getting Started with n8n for Agentic Workflows
Orientation to the n8n canvas: nodes, triggers, and executions
How n8n builds AI on LangChain with root and sub-nodes
Connecting your first language model and credentials
Building Your First AI Agent with the AI Agent Node
Anatomy of the AI Agent node: chat model, prompt, and tools
Triggering agents with the Chat Trigger and workflow triggers
Adding memory for multi-turn context and conversation history
Giving Agents Tools and Structured Outputs
Connecting tools: HTTP Request, Code, Calculator, and integrations
Writing clear tool descriptions so agents choose the right tool
Enforcing reliable outputs with the Structured Output Parser
Grounding Agents with RAG and Vector Stores
Why retrieval matters: reducing hallucinations with private data
Building an ingestion pipeline: loaders, splitters, and embeddings
Using vector stores as a retriever tool for AI agents
Designing Multi-Agent Systems in n8n
The orchestrator pattern: a manager agent that delegates work
Using the AI Agent Tool node to call agents as tools
Worked example: orchestrating a research agent and a writer agent
Connecting Agents with the Model Context Protocol (MCP)
What MCP is and how it standardizes tool discovery and calls
Exposing n8n workflows as tools with the MCP Server Trigger
Consuming external tools with the MCP Client Tool node
Reliability, Reusability, and Scaling
Wrapping repeatable steps in reusable sub-workflows
Sequential vs. parallel execution across multiple agents
Error handling, retries, and verification checks for dependable runs
Deployment, Monitoring, and Responsible AI
Deploying workflows on n8n Cloud or self-hosted environments
Monitoring latency, errors, and cost while auditing agent decisions
Bias, accountability, data privacy, and security best practices
