Collate 2.0 Demo Webinar Series: Session 2- Collate for Builders

​Stop rebuilding context for every new agent

​If you find yourself manually reconstructing and piping context for every new agent, this session will show you a better way. Without specialized knowledge, generic data agents only know what their prompt tells them, so they won’t have the full context to respond to requests accurately. They have no way to know that someone already caught an error in how a metric is defined last week, or that the person asking is only allowed to see results at an aggregate level. As a result, you get a confident answer without really knowing whether it is right or wrong.

​In this session, we'll show how Collate helps builders leverage trusted context to keep agents accurate and governed no matter where those agents run. Collate is built on OpenMetadata, the Open Context Layer for AI agents, so everything you build here stays open and interoperable with your other AI and data systems. We'll build an agent that monitors business data continuously and escalates a variance the moment it appears and show how to build it in a few different ways.

​What we'll cover:

• ​Retrieve — pull an asset's complete context including schema, transformation logic, and lineage, through the Context API and MCP server, with role-based access controls

• ​Build — create customized data agents in AI Studio, the Collate UI-based, no-code agent builder

• ​Extend — build standalone agents with the AI SDK (Python, Java, TypeScript), carrying the same governed context and corrections for agents running outside Collate

• ​Then we'll show a correction gets logged as a “memory,” so a mistake caught once doesn't have to be caught again by every agent reading the same data.