How Memory Helps AI Agents Keep the Context of a Conversation
Habr AI has published a clear breakdown of AI agent memory, from the limits of the context window to external storage. The article explains why a long conversation degrades a model's answers and how episodic, semantic and procedural memory differ. It also covers how to combine them in a single prompt through summarization, vector search and separate instructions for personal assistants, support scenarios and teams.
AI-processed from Habr AI; edited by Hamidun News
A breakdown of how memory helps AI agents keep the context of a conversation, and why you cannot build a useful assistant that lasts longer than a single conversation without it.
Why do AI agents need memory to keep context?
An LLM does not remember past sessions on its own. With every new request, the model receives everything afresh: the system prompt, the chat history and the results of tools, all inside a limited context window.
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