AI agent memory: types, architectures, and enterprise considerations

AI agent memory enables continuity, personalization, and context-aware decisions by retaining and retrieving information from prior interactions, addressing the limitations of stateless agents that repeatedly request the same information. Short-term memory uses an LLM’s context window during a single session, while long-term memory persists across sessions through external stores such as vector databases, knowledge graphs, and relational databases; episodic memory captures past events, whereas semantic memory stores factual knowledge. Common architectures include in-context token memory, fine-tuned parametric memory, and flexible retrieval-based external memory, with most enterprise deployments combining external retrieval with session context. Effective implementation requires deliberate choices about what to store, metadata, summarization, retrieval filtering, retention, and measurement of recall, relevance, and staleness. Persistent memory also introduces substantial privacy, compliance, residency, deletion, latency, and cost considerations, making governance and forgetting strategies essential. The recommended enterprise approach is to begin with a narrowly scoped semantic-memory pilot, establish governance for persistent data, then add episodic user history while monitoring production performance and memory quality.

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