Tutorials teach you how to build an AI agent that works when the API is up. They don't teach you what happens when the LLM hallucinates, the vector database times out, or the user asks something completely off-topic. When I build RAG (Retrieval-Augmented Generation) support bots, the "happy path" is only 20% of the build. The other 80% is edge-case handling. Here is the exact routing logic I use in n8n to keep AI systems from breaking silently in production: 🛡️ 1. The Guardrail Node: Before the LLM even generates an answer, I check the similarity score from the vector database. If it’s below 0.8, the system is instructed not to guess. It immediately routes to a "I don't know" fallback. Zero hallucinations. 🔄 2. The JSON Validator Loop: If I need the LLM to output structured data (like an order ID), I use a JSON validator node. If the LLM forgets a bracket or messes up the format, the workflow catches it, loops back, and feeds it a self-correction prompt. 👤 3. The Graceful Degradation (Human Handoff): If the system fails 2 times in a row, it doesn't crash. It pauses the workflow, packages the user's entire chat history, and sends a Slack/Zendesk alert to the human ops team with the exact payload that failed. Automation isn't just about making things run fast. It's about making sure they fail loudly and recover gracefully. What is your go-to fallback strategy when LLM APIs act up in production? Let me know below 👇 #n8n #AIengineering #RAG #automation