AI agents can reason, call tools, and perform complex tasks. However, when they start a new conversation, they behave as if they’ve never met the user. Give them too much history and they get distracted, expensive, and inconsistent. Give them too little and they forget user preferences or struggle with longer tasks. What is the right way to give an agent memory?

A vector database can be added, but that alone is not enough to solve the problem. It is also necessary to decide what the agent should remember, when it should save something, and how it should find that information later. Oh, and don’t forget that old memories can become stale - they need to be changed or removed at the right time.

Overall, building memory systems for AI agents is a data problem. This talk presents a practical way to think about the problem. The speaker will look at working, short-term, semantic, episodic, and procedural memory. Then, the talk will follow a memory through the system from creation to retrieval and eventual deletion. A short demo will compare exact, semantic, and hybrid retrieval and show how the agent behaves with and without the right context.
The goal is simple - to help attendees build agents that remember useful information without filling their context with irrelevant or outdated details.