Connect AI Agents to Data Sources with Redis

What this post added

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.