Connecting an AI agent to a single data source is straightforward, but production workloads demand much more: handling hundreds of fragmented enterprise systems, keeping retrieved data fresh, enforcing data governance, and maintaining low latency across multi-step agent loops. The post explains four data-access patterns (RAG, tool/function calling, MCP, custom connectors), documents common retrieval failure modes, and introduces Redis Iris — a managed real-time context engine that bundles structured data access via auto-generated MCP tools, semantic caching (LangCache), two-tier agent memory, and live data sync into a single in-memory platform.
Table of contents
What does it mean to connect an AI agent to data?Why AI agent quality depends on the data it retrievesRedis Iris serves agent context in millisecondsWhy connecting an AI agent to data is the easy partWhat production agent workloads demand from your data layerBuild agents that remember, not agents that guessWhere a context engine fits between AI agents & their dataFresh context, every callGive your agents context, not just a connection35 Impressions