Effective memory management for AI agents combines principles from traditional data storage with human cognitive mechanics, utilizing techniques like semantic caching and vector embeddings to enhance performance. The article discusses frameworks such as Redis and LangGraph that facilitate scalable memory systems, while emphasizing the importance of selecting the right LLMs to optimize cost and reliability in multi-turn interactions. Strategies for building adaptable AI agents are explored, providing insights into the future of intelligent systems.