We Built an Alternative to Vector RAG for AI Agent Memory

# Summary The article explains how AI agents are shifting from traditional vector-based Retrieval-Augmented Generation (RAG)—which pre-retrieves document chunks before generating answers—to agentic RAG, where agents dynamically decide when and what to retrieve as part of their reasoning process. Traditional vector RAG has limitations including destroying document structure through chunking and measuring semantic similarity rather than task-relevant information, making it suboptimal for complex agent workflows that require multi-step reasoning and tool use.