Open Data Infrastructure
Foundation for AI Requires Metadata SLAs
Why metadata freshness, coverage, and correctness need explicit SLAs when AI consumes metadata at runtime.
Metadata used to be documentation. For AI systems, metadata is runtime context.
The practical problem
AI systems use metadata to find data, interpret fields, choose tools, explain answers, enforce policy, and evaluate trust. If that metadata is stale or incomplete, the model may still produce an answer. That is the dangerous part.
The foundation for AI therefore includes metadata reliability. Not in the vague sense. In the operational sense: freshness, coverage, correctness, ownership, and incident response.
Metadata SLAs should be explicit
A metadata SLA should name the artifact and the promise. Catalog ownership must be current within a defined window. Lineage must cover production pipelines. Policy tags must be present before a data product becomes agent-accessible. Schema descriptions must meet a minimum completeness threshold.
These are not nice-to-have documentation goals. They are runtime requirements when agents use the metadata to decide what data to retrieve and how to interpret it.
Core idea: metadata reliability is part of the foundation for AI because agents consume metadata as part of the work path.
Treat metadata like production infrastructure
Metadata SLAs need owners, monitors, alerts, and exceptions. A missing owner should fail a promotion. A stale lineage graph should block agent access to a high-risk data product. A broken glossary mapping should become an incident when it changes model behavior.
This extends data observability as foundation for AI. Observability watches the data and the pipelines. Metadata SLAs watch the context that tells humans and agents what the data means.
What breaks first
- Agents rely on descriptions that have not been reviewed since the schema changed.
- Lineage exists for batch jobs but not for retrieval indexes or tool outputs.
- Policy tags are optional until a sensitive field appears in an answer.
- Metadata quality has dashboards but no escalation path.
Questions to ask
- Which metadata fields are required before AI access is allowed?
- How fresh must ownership, lineage, and policy metadata be?
- Who owns metadata incidents?
- Can metadata SLA failures block an agent workflow?
For related work, read Open Data Infrastructure Is the Foundation for AI, DataHub, OpenMetadata, and the Metadata Layer, and Metadata Is the Real Infrastructure Layer.
Sources to start with
These primary sources anchor the technical claims in this guide.
- DataHub documentation
- OpenMetadata data quality APIs
- OpenLineage documentation
- NIST AI Risk Management Framework
AI reliability starts with metadata that has to keep its promises.