Context
Data assets don't exist in isolation — they run on infrastructure. Service dependencies, Kubernetes deployments, and cloud resource relationships are essential context for AI agents investigating issues or planning changes.
Scope
New extractors for infrastructure metadata:
Kubernetes
- Extract deployments, services, configmaps, secrets (metadata only)
- Capture service-to-service dependencies from service mesh or network policies
- Emit
depends_on relationships between services and the data assets they consume
Cloud Resources (GCP/AWS)
- Extract resource metadata: compute instances, managed databases, storage buckets, IAM policies
- Capture resource-to-resource dependencies
- Link cloud resources to the data assets they host
Design Considerations
- Read-only access to cluster/cloud APIs
- Support namespace/project filtering to scope extraction
- Focus on relationships and topology, not operational metrics
Why
When an AI agent investigates why a dashboard is showing stale data, knowing that the upstream service was recently redeployed or that the database instance was scaled down is essential operational context.
References
Context
Data assets don't exist in isolation — they run on infrastructure. Service dependencies, Kubernetes deployments, and cloud resource relationships are essential context for AI agents investigating issues or planning changes.
Scope
New extractors for infrastructure metadata:
Kubernetes
depends_onrelationships between services and the data assets they consumeCloud Resources (GCP/AWS)
Design Considerations
Why
When an AI agent investigates why a dashboard is showing stale data, knowing that the upstream service was recently redeployed or that the database instance was scaled down is essential operational context.
References