Context Engineering for AI: What It Is & How to Build It

What this post added

Introduces and defines 'context engineering' for AI systems, differentiating it from prompt engineering. It details the challenges in AI agent reliability stemming from context management, categorizing failures into fragmentation, opacity, speed degradation, and non-accumulation. The post proposes four core operations for context engineering: write, select, compress, and isolate. It also outlines infrastructure needs, including support for multiple query modalities and real-time data freshness, and presents a five-stage maturity model for evaluating context layers. Finally, it explains how Redis, particularly through Redis Iris, can serve as a real-time context engine by integrating storage, retrieval, and messaging primitives.