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

This post clarifies the evolving landscape of AI agent context engineering, emphasizing that basic RAG is insufficient for production use cases. It introduces the concept of a real-time context engine to manage fresh data, relevant memory, and operational signals. The post details the layering of chunk-based RAG for documents, MCP-style tools for structured data, and agentic RAG for orchestration. It highlights Redis Iris's role in providing Context Retriever for auto-generating structured data access tools, Redis Search for hybrid retrieval, and Agent Memory/LangCache for persistent and cost-effective context reuse. The FAQ addresses common questions about agent memory, semantic caching, and the challenges of dynamic context retrieval for customer-specific answers, positioning Redis Iris as a solution for these complexities.