Build AI Agents with N8n Course

Lesson 1 • Setting Up Your Development Environment

Guides students through account creation, API key management, and environment configuration. Produces a ready workspace so hands-on exercises can begin immediately.

Lesson 2 • What Are AI Agents

Defines AI agents, their components, and how they reason and act autonomously. Grounds the entire course by distinguishing agents from simple scripts or rule-based bots.

Lesson 3 • Core N8n Concepts and Terminology

Covers workflows, triggers, nodes, and data flow within N8n. Ensures students share a common vocabulary used throughout all subsequent chapters.

Lesson 1 • Connecting LLM Providers to N8n

Covers credential setup and API integration for leading LLM providers. Establishes the connection layer that all AI agent workflows depend on.

Lesson 2 • Error Handling for LLM Nodes

Addresses rate limits, token overflows, and malformed responses from LLM APIs. Builds resilient workflows that recover gracefully from provider failures.

Lesson 3 • Prompt Engineering Fundamentals

Teaches structured prompt design, role assignment, and instruction clarity. Directly improves the quality and consistency of LLM outputs used in agent workflows.

Lesson 1 • Anatomy of the N8n AI Agent Node

Breaks down every configuration field of the AI Agent node and its sub-nodes. Gives students precise control over agent behavior before adding complexity.

Lesson 2 • Testing and Debugging Agent Workflows

Introduces execution inspection, pin data, and step-by-step debugging techniques. Ensures students can identify and fix issues before deploying any agent.

Lesson 3 • Triggering Agents from Multiple Sources

Shows how to activate agents via webhooks, schedules, chat, and form triggers. Expands the range of real-world scenarios students can automate.

Lesson 1 • Connecting External APIs as Tools

Demonstrates wrapping HTTP requests and third-party service nodes as agent tools. Unlocks any external service as an agent capability through N8n's HTTP node.

Lesson 2 • Built-in N8n Tools for Agents

Covers N8n's native tool nodes including web search, calculator, and code execution. Provides ready-made capabilities students can attach without custom coding.

Lesson 3 • Tool Output Handling and Chaining

Covers how agents process tool results and decide on follow-up actions. Enables multi-step reasoning where one tool's output feeds the next decision.

Lesson 4 • Understanding the Tool-Use Pattern

Explains how agents decide when and how to invoke tools during reasoning. Establishes the mental model needed before wiring any specific tool.

Lesson 1 • Long-Term Memory with Vector Stores

Covers embedding generation, vector store setup, and retrieval-augmented memory. Allows agents to recall information from past sessions or large knowledge bases.

Lesson 2 • Configuring Short-Term Conversation Memory

Walks through attaching and tuning buffer and window memory nodes to an agent. Enables agents to maintain coherent multi-turn conversations within a session.

Lesson 3 • Context Window Optimization

Teaches token budgeting, context pruning, and summarization to stay within LLM limits. Prevents context overflow errors that silently degrade agent performance.

Lesson 1 • Retrieval and Answer Generation

Builds the query-time workflow that retrieves relevant chunks and passes them to the LLM. Completes the RAG loop and connects retrieval output to agent responses.

Lesson 2 • Document Ingestion and Chunking

Covers loading PDFs, web pages, and structured files into N8n for processing. Prepares raw content for embedding by applying appropriate chunking strategies.

Lesson 3 • Embedding and Indexing Documents

Demonstrates generating embeddings and upserting them into a vector store via N8n. Creates the searchable index that the retrieval step queries at runtime.

Lesson 4 • Evaluating and Improving RAG Quality

Introduces faithfulness, relevance, and recall metrics for assessing RAG pipelines. Guides iterative improvement of chunking, retrieval, and prompt strategies.

Lesson 1 • Inter-Agent Communication in N8n

Shows how agents pass messages using workflow calls, webhooks, and shared data stores. Enables reliable coordination without tight coupling between agents.

Lesson 2 • Building an Orchestrator Agent

Constructs a central agent that decomposes tasks and delegates to sub-agents. Demonstrates how the orchestrator manages state and collects sub-agent results.

Lesson 3 • Specialized Sub-Agent Design

Covers building focused agents with narrow scopes and well-defined interfaces. Ensures each sub-agent is independently testable and reusable across workflows.

Lesson 4 • Multi-Agent Design Patterns

Surveys orchestrator-worker, peer-to-peer, and hierarchical agent architectures. Equips students to choose the right pattern before writing a single node.

Lesson 1 • Continuous Improvement and Versioning

Establishes workflow versioning, rollback procedures, and iterative improvement cycles. Keeps production agents reliable while enabling ongoing enhancements.

Lesson 2 • Performance Optimization and Scaling

Teaches queue mode, worker scaling, and execution concurrency tuning in N8n. Ensures agents handle high-volume workloads without degradation or timeouts.

Lesson 3 • Security and Access Control

Addresses credential encryption, role-based access, and webhook authentication. Protects sensitive data and prevents unauthorized agent execution in production.

Lesson 4 • Production Deployment Strategies

Covers self-hosted, cloud, and containerized deployment options for N8n agents. Helps students select the deployment model that fits their infrastructure and budget.