What Is AI Agent Memory

I build memory into every production agent because stateless agents frustrate users and miss opportunities. When a customer contacts your support agent for the second time, the agent should already know who they are, what they bought, and what happened in the last conversation. That context transforms a transactional interaction into a relationship.

The implementation ranges from simple to sophisticated. At the simple end: store conversation summaries in a database and retrieve them when the same customer returns. At the sophisticated end: vector-based semantic memory that lets the agent recall relevant past events based on context similarity, not just customer ID.

The challenge is memory management. Storing everything is expensive and degrades retrieval quality. Storing nothing means the agent can't learn. The sweet spot is selective memory — store the information that would be valuable if recalled, forget the rest. Customer preferences, issue resolutions, correction feedback, and important context are worth storing. Routine greetings and status checks are not.

Short-term memory (also called working memory) holds the context of the current conversation or task. This is the most basic form — the messages exchanged so far in the current session. Most agent frameworks manage this automatically through conversation history.

Long-term memory persists across sessions. It stores facts, preferences, and knowledge that the agent needs to reference later — customer preferences, past interaction summaries, business rules, and learned patterns. This typically lives in a vector database or traditional database.

Episodic memory captures specific past events: 'The last time this customer contacted us, they were frustrated about delivery times.' It's different from general knowledge — it's memory of what happened and how it was handled. Episodic memory is what makes agents feel like they know you.