Connect AI agents to data sources with Redis

AI agents require runtime access to external data and capabilities through approaches such as retrieval-augmented generation, function calling, the Model Context Protocol (MCP), and custom API connectors, often used together in production systems. Their effectiveness depends not only on connecting to data but on retrieving relevant, well-ranked, current, and authorized information within latency constraints, as poor retrieval, stale indexes, fragmented enterprise systems, excessive tool choices, and weak permission controls can lead to inaccurate or unsafe results. The piece argues that enterprise deployments need a context layer to manage data freshness, governance, memory, retrieval, and performance across multi-step agent workflows. It presents Redis Iris as a managed real-time context engine built on Redis that provides governed MCP-based structured data access, hybrid retrieval, semantic response caching, session and long-term agent memory, and near-real-time synchronization with relational databases, while leaving orchestration, policy decisions, ranking, and final prompt assembly to the application.

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