The Production Agent Gap: Why Your AI Agent Tutorial Won't Survive Real Users

Every AI agent tutorial follows the same script: Import LangChain Define some tools Call the LLM in a loop Ship it! And it works. In the demo. In the notebook. In the conference talk. Then you deploy it and everything breaks. The tool call times out but there's no retry logic, so the agent hallucinates its way through. A user sends a carefully crafted prompt and your agent emails your entire customer database to evil@hacker.com . The context window fills up and the agent forgets what it was doing. Your API bill hits $500 because a single session got stuck in an infinite loop. This is the production agent gap. The distance between a working demo and a reliable system. I've spent the last year building AI agents professionally, and I've documented everything I've learned about closing that gap into a comprehensive guide: Ship Production AI Agents . Here's a preview of what's inside - the patterns that separate production agents from tutorial agents. The Naive Agent vs. The Production Age