A context engine is the platform layer that handles retrieval, memory, caching, and data freshness for AI agents in production. Most agent failures stem from context problems rather than model limitations — stale data, disconnected systems, or missing session state. A context engine unifies these concerns into one coordinated stack. The post outlines three core layers: hybrid retrieval and ranking, short/long-term memory and session state, and semantic caching with real-time data freshness. It also explains where traditional single-pass RAG breaks down at agent scale, citing four degradation modes (context poisoning, distraction, confusion, and clash). Redis Iris is presented as a unified context engine built on Redis, combining Redis Search, Agent Memory, LangCache, Data Integration, and Context Retriever into one runtime.
Table of contents
What is a context engine?How a context engine sits between your data & your agentsBuild agents that remember, not agents that guessThe three layers every production context engine should coverFresh context, every callWhy traditional RAG runs into limits at agent scaleWhat Redis adds to the context engine stackAgents are only as smart as the data they can reachContext is a platform problem, not a prompt problemSort: