Whitepaper Companion Podcast - Context Engineering: Sessions & Memory

This co-hosted podcast episode, titled "Deep Dive," explores the critical elements required to equip large language models (LLMs) with memory for building intelligent AI agents. The discussion revolves around three core ideas: context engineering, sessions, and memory. Context engineering dynamically manages information within the LLM's context window, addressing the stateless nature of LLMs. Sessions serve as containers for individual conversations, tracking history and working memory. Memory provides long-term knowledge consolidation across multiple sessions, enabling personalization. The hosts delve into the challenges of context rot, session management, memory generation, and retrieval strategies, emphasizing the importance of asynchronous processing and data hygiene for building adaptive AI experiences.