Introduction

AI agents powered by large language models (LLMs) hold great promise for revolutionizing how knowledge workers do their job, but often fall short in one crucial area: memory. They may answer accurately in the moment, only to “forget” vital context in the next interaction. Retrieval-Augmented Generation (RAG) emerged as a popular workaround—pairing LLMs with external knowledge stored in vector databases—but standard RAG pipelines frequently struggle with accuracy and continuity.