How AI Agents Remember and Forget: The Engineering Behind Agent Memory

AI agents rely on memory systems to maintain context across interactions, as a stateless model without memory cannot pursue goals or avoid repeating mistakes. Unlike a model's context window — which acts as temporary working memory and resets with each request — real agent memory is stored externally and loaded selectively when needed. Researchers and developers typically categorize agent memory into four types: short-term working memory, long-term episodic memory, semantic memory, and procedural memory. A common production technique involves retaining recent conversation turns verbatim while summarizing older ones, keeping prompts concise without losing context. For cross-session recall, agents use vector embeddings stored in databases, enabling retrieval by semantic meaning rather than exact keywords, though this approach has limitations around recency and authority of information.