N8N for AI Automation: How I Built Multi-Agent Workflows That Actually Work

TL;DR N8N isn't just a "Zapier alternative." I use it as the backbone for AI agent orchestration — multi-step workflows where AI models analyze data, make decisions, and produce structured outputs. Here's my practical guide based on building two production applications with N8N. Why N8N Over Code? Let me be direct: I can write Node.js. I can build Express APIs. I could orchestrate AI agents entirely in code using LangChain or CrewAI. But I choose N8N for certain workflows because: Visual debugging is unmatched. Click any node, see exactly what data went in and came out. When an AI agent produces garbage, you can inspect the prompt, the input data, and the response in seconds. In code, you'd be adding console.logs and redeploying. Fallback logic is trivial. My primary AI model (Groq) sometimes hits rate limits. In N8N, I add an IF node after the AI call — if error, route to Gemini. Done. In code, this is a try/catch with retry logic, environment variables, and error classification. Non-