Your agent treats every conversation like a first date. It should not.

You ask your agent the same thing every Monday. It has no idea. You correct it and it makes the same mistake next time. You told it your preferences three conversations ago and it forgot everything. Most agents have no memory beyond the current chat window. Every session starts from zero.

In this session you learn how to build agents that actually learn from the people using them. You start with short-term memory so the agent stays coherent within a conversation. Then long-term memory so it remembers what happened days or weeks ago using vector stores over past conversations. Then user preference tracking so the agent learns how you like things done and adapts its tone, format, and decisions over time. Then feedback loops where the agent takes your corrections and actually uses them to improve future responses instead of ignoring them. You see how to store all of this safely, how to let users see and delete what the agent remembers, and how to handle the privacy side so personalization does not become surveillance. You leave with an agent that gets better the more you use it and respects your data while doing it.