From Chatbots to AI-Native Apps: Building Agentic Memory with Lakebase
DataIntermediate
AgentsAIArchitectureDataDatabases
The shift from simple chatbots to autonomous agents requires more than just better prompts; it requires Agentic Memory. While standard RAG provides a snapshot of data, true AI-native apps need a persistent, evolving state to reason effectively over time.
In this session, we explore how to build this memory layer using Lakebase. We’ll cover:
– Architectural Shifts: Moving from stateless chats to stateful, goal-oriented agents.
– Memory Management: Leveraging Lakebase and Lakehouse to unify structured data and unstructured context for long-term recall.
– Optimization: Practical strategies for memory condensation and reducing context drift.
Attendees will move beyond chat applications and learn how to build autonomous systems that actually remember.
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