Context Engineering Patterns for AI Agents: Strategies and LangGraph Implementation

Context engineering is now recognized as the most critical discipline in building effective AI agents. Unlike prompt engineering, which focuses on crafting single instructions, context engineering is about dynamically assembling and managing all the information, tools, and state an agent needs to perform well at each step. This includes system prompts, user input, chat history, long-term memory, retrieved knowledge, tool definitions, and more. The LangChain team reviews four core patterns—write, select, compress, and isolate—for structuring and optimizing context, and explains how LangGraph enables developers to implement these strategies for robust, scalable agent workflows. By mastering context engineering, teams can avoid pitfalls like context poisoning, distraction, and confusion, ensuring agents remain accurate, efficient, and aligned with user intent