Agent Memory vs Model Memory: Long-Term State Design (EXPLAINED)
Hard scenario on agent memory — episodic vs semantic memory, vector stores, summarization, and model parametric limits.
TL;DR — Quick Answer
Model memory is whatever fits in context plus static pretraining — ephemeral and lossy. Agent memory is external structured state: conversation summaries, vector episodic store, user profile facts, and tool result cache — retrieved selectively per turn. Design: short-term buffer in context, rolling summarization for mid-term, vector DB for long-term episodic retrieval, explicit user preference store with consent. Never rely on model to remember account details across sessions without persisted retrieval.
The Interview Question
Compare agent memory and model memory for long-term state in LLM applications. How do you design persistent memory without blowing context limits?
Deep Explanation
Model memory limits
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