Sponsored by: Insight| Beyond the Sandbox: Using Lakebase Instant Branching for High-Stakes AI Agent Deployment

As AI agents evolve from chat-based assistants into autonomous systems, the cost of data errors and unintended actions grows. How do you let an agent update pricing or modify records without exposing production systems safely? Enter Lakebase. In this session, we demonstrate a safety‑first architecture using Lakebase’s serverless Postgres and Instant Branching capabilities. You’ll see a live demo of an Autonomous Pricing Agent responding to real-time market signals. Instead of writing directly to production, the agent triggers an Instant Branch—a zero‑copy, isolated clone of the operational database created in seconds. Here, we run automated pre‑flight validations, business rules, and AI/BI impact analysis before selectively merging approved changes back to the primary database. Attendees will learn how to implement Git‑for‑Data style workflows, manage agent state and memory using pgvector, and continuously sync changes using Lakehouse Sync for auditability and downstream analytics.