Data Management for Agentic Memory: Foundations, Systems, and Challenges

Abstract

Recent advances in large language models (LLMs) have led to the emergence of autonomous agents as a transformative paradigm for building intelligent AI systems, integrating reasoning, planning, tool use, and interaction capabilities to tackle complex, open-ended tasks. Despite their growing sophistication, most agents remain stateless, lacking the ability to retain, organize, and reuse past experiences, which limits their personalization, long-term adaptation, and lifelong learning. To address this limitation, agentic memory has emerged as a critical area of research, equipping agents with mechanisms to store, retrieve, update, and exploit information from prior interactions, reasoning trajectories, tool executions, and environmental feedback. From a data management perspective, agentic memory shares similarities with database systems but also presents opportunities for integration, enhancing query functionality, bridging structured and unstructured data, and optimizing execution efficiency through historical insights. This synergy between agentic memory and databases amplifies the capabilities of autonomous agents and redefines the role of databases in adaptive, intelligent data systems. In this tutorial, we systematically review agentic memory systems from a data management perspective. We introduce the workflow and architecture of agentic memory systems. We summarize their operators, storage, and optimization techniques. We also highlight open research challenges, aiming to inspire further innovation and progress in this exciting field.