Why this matters
Ingestion, classification, lineage, quality and ownership together turn a set of tables into context an agent can reason over. This module shows how the knowledge graph and the ontology explorer use that context to ground agent answers.
Before you start
Curated metadata is the context
Every curation step covered so far contributes: a description gives an asset meaning, lineage gives it provenance, a quality result gives it a trust signal, an owner gives it accountability. Together they are the context an agent needs to answer correctly.
A knowledge graph expresses relationships
An asset list names what exists. A graph describes how assets relate to each other and to business concepts. That structure is what allows a question posed in business language to reach the right physical asset.
Ungoverned metadata produces confident wrong answers
The failure is an agent that answers fluently from stale, untested or mislabeled metadata.
The interactive demo
Work through the demo below. It runs in your browser with nothing to install. Watch for:
- Using the AI native interface
- The Context Center that feeds the Knowledge Graph
- The Ontology Explorer
- A walkthrough of AI Studio
- The new Dashboards
This demo is also available on its own at the Learning Center.
After this module you should be able to
- Explain how curated metadata becomes usable context for an agent
- Describe what a knowledge graph adds beyond a list of assets
- Argue why governance quality determines AI answer quality