Projects · 01
Ontology-Grounded Agentic AI Data Steward
Independent technical apprenticeship and engineering project. Exploring how an enterprise AI Data Steward can combine deterministic data-quality and entity-resolution capabilities with bounded AI reasoning and human stewardship.
Status: active development
Problem
Enterprise AI is only as trustworthy as the context underneath it.
Agents that act on enterprise data inherit every ambiguity in that data — duplicated identities, unclear ownership, missing lineage. Before an agent can be useful, it needs context it can rely on, and boundaries that make clear what it knows, what it is guessing, and what it is allowed to do.
Current exploration
- Identity resolution
- Structured evidence
- Governed agent capabilities
- Human-in-the-loop stewardship
- Enterprise context and governance
Engineering philosophy
- Facts should have authoritative sources.
- AI recommendations should not silently become system actions.
- Missing evidence should remain missing evidence.
- Human decisions and machine authority should remain distinguishable.
Selected implementation details intentionally withheld while IP options are evaluated.
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