AI Workflow Automation with n8n

Overview

Build Production-Ready AI Agents

Course Positioning
This course teaches how to design, build, deploy, and scale production-ready AI automation systems using n8n, OpenAI, Anthropic, databases, APIs, and modern deployment practices. The emphasis throughout is on transferable architecture and implementation patterns — the concepts (agents, orchestration, tool-calling, error handling, deployment) apply beyond n8n to any automation/orchestration tool, since n8n here is the vehicle for learning durable systems-design skills rather than the end goal itself.

Learning Outcomes
• Build production-ready AI workflows
• Integrate APIs, databases, and LLMs
• Deploy and scale n8n
• Implement enterprise workflow patterns
• Develop real-world AI automation projects

Curriculum

3 Sections
23 Lessons
20 Hours

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Module 1 — AI Automation Fundamentals
This module lays the conceptual groundwork before any tool-specific work begins. The goal is to build correct mental models: what "AI automation" actually means, how agents differ from deterministic workflows, and the core primitives (APIs, webhooks, JSON, MCP, RAG, memory) that every later module builds on.
9
Module 2 — n8n Foundati
This module moves from concepts into the tool itself: getting n8n running, understanding its execution model, and building the first real (non-AI) workflow end-to-end.
7
Module 3 — APIs & Integrations
This module goes deep on the single most-used node category in n8n — connecting to arbitrary external services via HTTP — since almost every real workflow eventually needs to talk to something n8n doesn't have a pre-built node for.
7