Building an Agentic Chatbot with Durable Execution

How I built a production-ready AI assistant that decides when to search the web, process documents, and run multi-minute research tasks without losing progress if things go wrong. Most "AI chatbot" tutorials stop at the same place: wrap an LLM, stream tokens, done. That's a prototype. Production is a different beast entirely. Over the past three years building AI-native applications, I've shipped chatbots that need to do more than answer questions. They need to act : search the web for current information, process uploaded documents, run multi-step research that takes minutes, and deliver results even if the user closes the browser. This article walks through the architecture I landed on after multiple production deployments. The key insight: agentic chat is a distributed systems problem , not just an AI problem. The Architecture Here's the simplified flow: User message → Elysia API (auth + validation) → Vercel AI SDK (streaming + tool calling) → Claude decides: respond directly, or us