Semantic memory search for AI agents

What this post added

This post elaborates on the 'Context Engineering for AI' thread by focusing specifically on semantic memory search as a mechanism for AI agents to achieve durable recall. It details the architecture of semantic memory search (storage of facts as vector embeddings and retrieval via similarity search), explains why AI agents forget without an external memory layer (context window limits, cost, accuracy degradation), and contrasts semantic search with keyword search. It also emphasizes the critical need for fast and fresh memory retrieval, discussing latency considerations for interactive interfaces and voice agents, and the problem of data staleness in memory. The post highlights Redis Iris and Redis Agent Memory as solutions for providing low-latency, real-time context and memory for AI agents, including support for hybrid search and a two-tier design for session memory.