Why Vector Stores Are Not Enough: Using Lakebase as a Durable Memory Layer for Autonomous Agents

We are told that Vector Databases are the 'Memory' for AI Agents. But they give you a probabilistic similarity score. In enterprise workflows, 'probably' isn't good enough. Agents need deterministic, transactional state.

We will use Lakebase as a memory layer for agents, enabling them to remember actions, lock resources, and handle concurrency.

We will build a 'Stateful Worker' agent live with:

• The 'Two-Phase Commit' Prompt: Forcing an LLM to lock a row in Lakebase before calling an external API, preventing hallucinated actions.
• Time Travel for Agents: Leveraging Lakebase Branching to create isolated 'simulation' environments where agents can test destructive actions (e.g., 'Delete User') in a sandbox before committing to production.
• Hybrid Recall: A pattern combining Vector Search for knowledge with Lakebase for state, proving you need both to build reliable agents.

Attendees will leave with a resuable architecture to build high-performing and autonomous agents in production.