Redis introduces the Context Engine, a new layer that provides AI agents in enterprise environments with reliable memory. The company aims to address the \u201ccontext problem\u201d with this solution: a lack of memory that causes autonomous systems to hallucinate, stall, or produce incorrect results. <\/p>\n
The Context Engine consists of three components. The Redis Context Retriever, currently in preview, allows developers to build a semantic model of business data. This enables agents to map how customers are related to opportunities or support tickets. Instead of relying on unpredictable text-to-SQL queries, the Retriever automatically generates tools based on the open-source Model Context Protocol.<\/p>\n
The second component, Redis Agent Memory, offers a two-tier memory system. Short-term interaction history is managed alongside a persistent long-term cache for preferences and previous sessions. Both are available in preview today.<\/p>\n
The third, Redis Data Integration, is generally available today. It continuously synchronizes business data from relational databases and data warehouses, ensuring agents always work with up-to-date information.<\/p>\n
Redis\u2019s in-memory datastore is already present in 43 percent of all enterprise AI agent stacks. The company also recently surpassed the $300 million mark in annualized recurring revenue.<\/p>\n
With the Context Engine, Redis positions itself as a dedicated infrastructure layer between the agents and the business data.<\/p>\n