How does AI agent memory work?

AI agent memory works through four types that build on each other, so the agent gives responses shaped by persistent knowledge and accumulated experience instead of forgetting everything when the session ends. Working memory is the context window for the current session, fast but temporary. Semantic memory is stored facts, rules, and documentation. Procedural memory is the skills that tell it how to do things. Episodic memory is a record of what happened in past interactions. Take a support team with a ticket queue. Semantic memory gives it the playbook and policies to answer consistently, while episodic memory lets it notice that billing tickets often need escalation and flag them earlier.

What are the types of AI agent memory?

There are four types, each doing a different job. Working memory is the context window, everything the agent can see right now in this session, which disappears when the session ends. Semantic memory is facts, rules, and documentation the agent loads at the start of each session, often just Markdown files in a folder. Procedural memory is the agent's skills, the step-by-step instructions for how to do things. Episodic memory is its distilled record of past interactions and what it learned. A support team's ticket bot might lean on semantic memory for the policies it loads each session and episodic memory for the lessons it picks up from past tickets. Not every agent needs all four, so match the types to the job rather than building everything.

Do AI agents remember previous conversations?

They can, through episodic memory, which is the agent's record of what happened in past interactions and what worked. The naive version saves every conversation and searches through it, but better systems distil and compress the experience, for example noting "the auth module issue was in the middleware layer" rather than storing a full 45 minute transcript. This is what separates an agent from a chatbot. On a support desk, a chatbot answers a ticket and forgets, while an agent improves over time because it remembers what billing tickets usually need.

What is the difference between short-term and long-term memory in AI agents?

Short-term memory is the agent's working memory: the context window, fast and immediate like RAM on a computer but temporary and size-limited, so it is gone when the session ends. Long-term memory covers the three types that persist across sessions: semantic memory for facts and documentation, procedural memory for skills, and episodic memory for lessons from past interactions. A simple routing bot might only need short-term working memory, while a coding agent needs all four because it carries project knowledge across sessions.

How AI agent memory works

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How AI agent memory works: the four types of memory, working, semantic, procedural and episodic, that let an agent learn over time instead of forgetting each session.

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