Agent Memory

Full definition

Three common layers: (1) Working memory — the current context window itself; (2) Episodic memory — past conversations or events stored verbatim and retrieved by similarity (vector store of summaries); (3) Semantic / profile memory — distilled facts about a user or domain, stored as key-value or structured data. Frameworks: LangGraph memory, MemGPT (Packer et al., 2023), Letta, Zep. Memory write/read policy is the hard part — naive "store everything" memory rapidly degrades retrieval quality.

Why it matters

Memory turns one-shot chatbots into stateful assistants. It is also the leading source of privacy and security risk in agent products — what gets written to memory is often outside the user's mental model and can leak across sessions.

Example

A sales-coaching agent remembers each rep's pipeline accounts, previous objections handled, and skill-gap notes; after 8 weeks the agent's recommendations measurably outperform a memoryless baseline by 22% on close rate.

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Related terms

• AI Agent / Agentic AI
  An AI agent is an LLM-driven system that, given a goal, plans a sequence of steps, calls tools or APIs, observes the results, and iterates until the goal is reached — going beyond single-turn chat to multi-step autonomous action.
• Context Window
  A context window is the maximum amount of text — measured in tokens — a language model can read and reason over in one inference call, equivalent to the model's working memory for that turn.
• Retrieval-Augmented Generation (RAG)
  Retrieval-Augmented Generation (RAG) is a technique where a language model retrieves relevant documents from an external knowledge base before generating a response, which reduces hallucination and enables citation of sources.
• Multi-Agent System
  A multi-agent system orchestrates multiple specialized AI agents — often LLM-powered, with distinct roles, tools, and prompts — that communicate, coordinate, and divide labor to solve a problem larger or more diverse than any single agent could handle reliably.

More deployment & operations terms

• Planner-Executor
  Planner-executor is an agent pattern that separates a high-level planning model — which produces a multi-step plan up front — from a tool-calling executor that runs each step, decoupling strategic reasoning from tactical action.
• Tool Router
  A tool router is a layer in an agent system that selects which tool (or subset of tools) to expose to the model for a given query, instead of putting all tool definitions in every prompt.
• Model Context Protocol (MCP)
  The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in 2024, that lets AI applications connect to external tools, data sources, and services through a common interface — analogous to the Language Server Protocol for editors.