Context Engineering for AI Agents: A Practical Guide

Your AI agent works perfectly in demos. Then you deploy it to production, hand it a real workflow with 30 conversation turns, 15 tool definitions, and a pile of retrieved documents -- and it starts hallucinating, ignoring instructions, and picking the wrong tools. The model didn't get dumber. Your context engineering failed. Context engineering is the practice of curating everything an LLM sees before it responds -- not just the prompt, but the system instructions, tool definitions, conversation history, retrieved documents, and previous step results. While prompt engineering focuses on crafting the right instruction, context engineering decides what information makes it into the context window and what gets left out. This matters because in production agent systems, the context window is prime real estate. Every token competes for attention, and the wrong mix of information degrades performance faster than a weaker model would. This guide gives you four concrete strategies to engineer