Building Search for Agents with Lakebase

Traditional search breaks under the bursty, concurrent demands of multi-step agents. In this session, learn how to build agent-native search on Postgres that extends pgvector with advanced indexing and first-class BM25, with vector and full-text indexes living right alongside your operational data. The payoff: no brittle app-level joins, just single-statement SQL that fuses vector rankings with structured filters. Watch it scale to zero when idle, burst to billion-vector scale with no performance hit, and branch instantly for sandboxing and A/B testing, all without rebuilding a single index. Then get a first look at what we are building to make Lakebase the foundation for the next generation of AI agents.