Connect AI Agents to Data Sources with Redis

This post elaborates on the challenges of connecting AI agents to data sources, focusing on integration sprawl, stale data, and permissions/data governance. It emphasizes the need for a context engine to manage data freshness and speed on the agent's hot path, introducing Redis Iris as a solution for providing real-time context. The post also discusses various patterns for connecting agents to data, including RAG, tool/function calling, MCP, and custom API connectors, and highlights the importance of data quality and retrieval strategies for agent output quality.