Context engineering is an essential discipline for enhancing the performance of AI agents, focusing on effectively managing the limited context window of language models (LLMs) by employing strategies such as writing, selecting, compressing, and isolating context. This approach is crucial for agents that interleave LLM calls with tool usage, often in tasks that require long-running interactions and significant memory management. Effective context engineering can address issues like context poisoning, distraction, confusion, and clash by ensuring that only relevant information is retained or recalled. Tools like LangGraph and LangSmith offer frameworks to support these strategies, providing features like state management, sandboxing, and multi-agent architectures to optimize context usage and improve agent performance. By mastering context engineering, developers can create AI systems that are more efficient, scalable, and capable of handling complex tasks through judicious use of memory, tool integration, and feedback loops.