AI agent context engine FAQ: RAG, memory & caching | Redis Iris

What this post added

This post is an FAQ that elaborates on the practical application of Redis Iris for AI agents. It details strategies for managing context through chunk-based RAG, agentic RAG, and MCP-style tools. It emphasizes the importance of real-time context engines for providing fresh, relevant data to agents, addressing limitations of basic RAG, and the role of agent memory and semantic caching in improving personalization, speed, and cost-efficiency. Redis Iris is positioned as a solution to wire these components together, offering Context Retriever for business data, Redis Search for multi-modal retrieval, and Agent Memory/LangCache for persistent and cost-effective context.