A context layer is the subsystem in an AI agent stack that manages what an agent knows at any given moment — handling memory, session state, retrieval, conflict resolution, and token budgeting. Unlike RAG (which handles single-turn retrieval) or a semantic layer (which defines metrics), a context layer sits above both and orchestrates them for long-horizon agentic tasks. Five key failure modes it prevents are context poisoning, distraction, confusion, clash, and rot. Redis Iris is presented as a managed context engine that combines vector search, agent memory (short- and long-term), semantic caching, operational data access via change data capture, feature serving, and pub/sub coordination — all running on Redis's in-memory architecture to meet the low-latency demands of production AI agents.
Table of contents
What a context layer isFive context failures a context layer preventsBuild fast, accurate AI apps that scaleContext layer vs. RAG vs. semantic layer: key differencesThe building blocks of a real-time context layerGive your AI apps real-time contextRedis Iris serves agent context in millisecondsThe context layer is the agent's operating systemSort: