An agent is only as good as the context it can draw on, and memory is what lets that context last beyond a single response. In this hands-on session, you will build an agent and give it memory backed by Lakebase, then deploy it as an app on Databricks. You'll add short-term memory so the agent follows a conversation across turns, and long-term memory so it recognizes a returning user in a session days later. Then you'll trace how it works with MLflow and deploy it as an app that remembers its users.