Powering Agents with Search: Creating Interactive Vector Pipelines with SQL

How do you keep search indexes and vector databases fresh enough for an agent to act on?
Search has become the predominant way agents see the world. But keeping vectors and attributes up to date is complex, forcing teams to choose between stale documents, expensive batch updates, or complex pipelines.
This talk will show how to architect a search pipeline in SQL that keeps your search index up to date and trustworthy enough for agents to rely upon. You'll learn how to transform your operational data into documents and semantic objects, patch your search index with only changed attributes and embeddings, and enrich results with fresh context to give agents the full picture.
Attend if you're deploying AI agents to production and want to give them fresher, more complete context, and tighten their read-act-react loop.