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.

Scheduling for training will launch closer to the event. An email will be sent notifying you when scheduling is open in the session catalog.