Why AI agents need separate working and episodic memory layers

AI agents are stateless, so anything they remember between sessions must be stored outside the model or placed in its context window. Simply enlarging context windows does not solve the problem, since research from Anthropic and Chroma shows recall and performance degrade as input length grows. Working memory is short-lived and must be managed through trimming or summarization, each with its own trade-offs. Episodic memory lives in external stores and is retrieved on demand, with the choice of store depending on how the agent needs to recall information. Approaches like the Berkeley paging model and consolidation of stored episodes into summaries help manage what moves between layers.