Context Engineering (CE) is the systematic process designed to bridge the cognitive gap between human intent and machine understanding by optimizing context collection, storage, management, and usage. We explore CE’s history, tracing its evolution over 20 years from the "primitive computation" of Era 1.0 to the current "agent-centric intelligence" of Era 2.0, driven by large language models (LLMs). Discover how engineers reduce high-entropy human contexts into low-entropy machine representations, aiming for a future where AI achieves human-level or even superhuman context assimilation.