Foundation for AI Catalog Health SLOs

AI platforms fail quietly when the catalog is technically up but operationally unhealthy.

Uptime is not enough for an AI control plane

The catalog is becoming the control plane for open data infrastructure. That creates a new reliability problem. A catalog can respond to API calls while serving stale metadata, missing ownership, broken policy evaluation, or incomplete lineage.

Catalog health SLOs should measure the behaviors AI systems depend on. Availability matters, but so do metadata freshness, namespace ownership coverage, policy decision latency, lineage completeness, credential vending success, and incident response time.

Catalogs sit between table state and system behavior

The Iceberg REST Catalog specification defines catalog operations for managing table metadata through an API boundary. OpenLineage documentation provides a model for run, job, and dataset lineage events. OpenTelemetry signals documentation separates traces, metrics, logs, baggage, and profiles as observability signals.

Those sources point to the same operational truth. Catalog health has to be observable. If a catalog is the control plane, its health signals need SLOs.

Measure control-plane behavior

• Track catalog API availability, latency, and error rates by operation type.
• Measure metadata freshness from table commit to catalog visibility.
• Report ownership coverage for namespaces, tables, views, and AI-facing data products.
• Measure policy evaluation availability, decision latency, and denial evidence completeness.
• Track lineage event completeness for critical data products.

This extends ODI as the foundation for AI, what a catalog does in an open lakehouse, and observability scorecards for AI. A catalog SLO should tell users whether the control plane can be trusted right now.

What breaks first

• The catalog is up, but recent table commits are not visible to consumers.
• Policy evaluation is slow, so agent tools time out or bypass the governed path.
• Ownership metadata is missing for the tables that matter most.
• Lineage events lag behind, making incident review reconstructive instead of diagnostic.

Questions to ask before declaring the catalog healthy

• Which catalog behaviors are critical to AI workflows?
• What freshness and latency targets does each behavior need?
• How does the system degrade when a catalog SLO is violated?
• Can users and agents see catalog health before taking action?

Sources to start with

These primary sources anchor the technical claims in this guide.

• Apache Iceberg REST Catalog specification
• OpenLineage documentation
• OpenTelemetry signals documentation

The catalog is healthy only when the systems depending on it can still make governed decisions.