# From Context to Semantics: How Metadata Powers Agentic AI An episode of [Data Engineering Podcast](https://invisiblenetwork.ai/podcast/1193040557), 21 Dec 2025, 1 h 06 min. Audio: https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/6390193297761297808736db69-3731-4cea-a918-08b578b47c08.mp3 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 • Data teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. 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Start building at MongoDB.com/Build • 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… ## Chapters - 0:11 Intro and episode setup - 1:53 Guests return: Suresh Srinivas and Sriharsha Chintalapani - 2:09 Sureshs background and OpenMetadata origins - 3:12 Sriharshas background and Collate role - 6:27 Why metadata context matters for modern data and AI - 9:42 OpenMetadatas growth, community, and releases - 12:23 LLMs meet metadata: context for enterprise AI - 15:31 From human-first catalogs to AI-enabled workflows - 17:21 Scaling agentic workloads and minimizing data warehouse load - 21:30 Using AI agents to document, tier, and test data automatically - 25:52 Bridging governance and engineering with agents - 28:07 Context engineering: parsimony, semantics, and MCP - 33:28 From discovery to action: agents, MCP server, and toolchain orchestration - 37:52 Enterprise AI guardrails: identity, policy, and leakage risks - 38:38 AI governance in OpenMetadata: tracking agents, models, prompts - 41:37 Evolving asset scope: services, APIs, models, and AI agents - 42:23 Semantics over context: defining business meaning for LLMs - 44:46 Human-in-the-loop reality and confidence building - 45:03 Agentic UX: natural language over clicks, and unified workflows - 49:47 Market consolidation towards unified metadata platforms - 50:17 Community lessons, architectural choices, and scaling connectors - 53:04 When not to start with a catalog and org readiness - 55:18 Roadmap: semantics, AI governance, and RDF ontologies - 1:02:01 Wrapping up: gaps in data tooling, tool obsession, and future - 1:05:26 Closing and calls to action Shows are listed from Apple Podcasts' charts and directory, and Podcast Index where it is used. 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