Context Engineering is the systematic discipline of designing, structuring, and managing the information fed to Large Language Models (LLMs) and AI agents to optimize their performance, accuracy, and efficiency. Unlike traditional prompt engineering, which focuses on crafting individual queries, context engineering operates at a systems level—managing the full data environment including memory, external knowledge, tool definitions, conversation history, and environmental signals. As models scale and context windows expand beyond millions of tokens, the challenge shifts from "what can we fit?" to "what should we include and how should we organize it?" Context engineering addresses this by applying principles of information architecture, relevance ranking, compression, and dynamic adaptation to ensure AI systems receive the right information at the right time without overwhelming their attention budget or incurring excessive costs.