Tracking models, agents and AI governance

Summary 
In this episode Suresh Srinivas and Sriharsha Chintalapani explore how metadata platforms are evolving from human-centric catalogs into the foundational context layer for AI and agentic systems. They discuss the origins and growth of OpenMetadata and Collate, why “context” is necessary but “semantics” is critical for precise AI outcomes, and how a schema-first, API-first, unified platform enables discovery, observability, and governance in one workflow. They share how AI agents can now automate documentation, classification, data quality testing, and enforcement of policies, and why aligning governance with user identity and intent is essential as agentic access scales. They also dig into scalability strategies, MCP-based agent workflows, AI governance (including model/agent tracking), and the emerging convergence of big data with ontologies to deliver machine-understandable meaning. 

Announcements 

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Your host is Tobias Macey and today I'm interviewing Suresh Srinivas and Sriharsha Chintalapani about how metadata catalogs provide the context clues necessary to give meaning to your data for AI systems

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you start by giving an overview of the roles that metadata catalogs are playing in the current state of the ecosystem?
• How has the OpenMetadata platform evolved over the past 4 years?
  • How has the focus on LLMs/generative AI changed the trajectory of services like OpenMetadata?
• The initial set of use cases for data catalogs was to facilitate discovery and documentation of data assets for human consumption. What are the structural elements of that effort that have paid dividends for an AI audience?
  • How does the AI audience change the requirements around the cataloging and presentation of metadata?
• One of the constant challenges in data infrastructure now is the tension of making data accessible to AI systems (agentic or otherwise) and incorporating AI into the inner loop of the service. What are the opportunities for bringing AI inside the boundaries of a system like OpenMetadata vs. as a client or consumer of the platform?
• The key phrase of the past ~2 years is "context engineering". What role does the metadata catalog play in that undertaking?
  • What are the capabilities that the catalog needs to be able to effectively populate and curate that context?
  • How much awareness does the LLM or agent need to have to be able to use the catalog effectively?
• What does a typical workflow/agent loop look like when it is using something like OpenMetadata in pursuit of knowledge that it needs to achieve an objective?
• How do agentic use cases strain the existing set of governance frameworks?
  • What new considerations (procedural or technical) need to be factored into governance practices to balance velocity with security?
• What are the most interesting, innovative, or unexpected ways that you have seen OpenMetadata/Collate used in AI/agentic contexts?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on OpenMetadata/Collate?
• When is OpenMetadata/Collate the wrong choice?
• What do you have planned for the future of OpenMetadata?

Contact Info

• Suresh
  • LinkedIn
• Sriharsha
  • LinkedIn

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

• OpenMetadata 
  • Podcast Episode
• Hadoop
• Hortonworks
• Context Engineering
• MCP == Model Context Protocol
• JSON Schema
• dbt
• LangSmith
• OpenMetadata MCP Server
• API Gateway

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA