Agent Memory
Agent memory is the set of mechanisms that let an AI agent keep information beyond a single model call and reuse it later in the same task or in future sessions. Short-term memory is the working state carried between steps, while long-term memory lives outside the model, in a database, file or vector store, and is retrieved when relevant; long-term stores are often split into semantic memory (facts), episodic memory (past actions and their outcomes) and procedural memory (instructions and skills). The context window is only what the model sees in one call, and memory is the layer that decides what gets written, kept, updated and loaded into that window. In agent systems, memory appears as user profiles in assistants, as notes or to-do files in long-running coding agents, and as stored trajectories that help an agent avoid repeating a failed approach.
Most production problems with memory concern writing: deciding what is worth keeping, resolving conflicts with older entries and expiring facts that are no longer true. Memory also widens the data protection surface, because personal data persists between sessions, so retention and deletion rules belong in the design from the start.
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