Context engineering is the system-level discipline of architecting the dynamic information environment for AI models. Unlike prompt engineering, which focuses on phrasing specific instructions, context engineering programmatically assembles the model's "working memory" using retrieved data, tool outputs, and conversation history. It employs strategies like selection, compression, and ordering to manage token limits and prevent "context rot." By orchestrating how information is filtered and presented at runtime, context engineering ensures LLMs remain grounded and reliable for complex, long-horizon tasks, effectively serving as the operating system for agentic AI.