After the synchronization, the assets are available in the specified domain.
Warning Do not move the assets to another domain. Doing so may lead to errors during future synchronizations.
By default, the assets are shown in a plain list, but you can enable a multi-path hierarchy to show it in a tree structure.
Newly ingested assets are assigned the first status listed in your Operating Model statuses, and existing assets keep their assigned status.
Synchronized metadata per Azure AI Foundry asset type
This table shows the metadata for the Azure AI Foundry asset types. If you do not see any of the listed synchronized metadata, you can add characteristics to the layout on the asset type page.
| Asset type | Synchronized metadata | Public ID |
|---|---|---|
| AI Base Model | Creation Date in Source | CreationDateInSource |
| Retirement Date in Source | RetirementDateInSource | |
| Azure AI Foundry Model Version | Description from source system | DescriptionFromSourceSystem |
| Version | Version | |
| Supported Input Modalities | SupportedInputModalities | |
| Supported Output Modalities | SupportedOutputModalities | |
| Supported Model Customizations | SupportedModelCustomizations | |
| Any custom metrics defined via the configuration. If you use custom metrics, ensure that you add them to the assignment and layout on the asset type page. | ||
| AI Model Deployment | Initiating User in Source | InitiatingUserInSource |
| Creation Date in Source | CreationDateInSource | |
| Modification Date in Source | ModificationDateInSource | |
| Implemented Content Filtering | ImplementedContentFiltering | |
| Compute Configuration | ComputeConfiguration | |
| Azure AI Foundry Agent | Description from source system | DescriptionFromSourceSystem |
| Instructions | Instructions | |
| Document creation date | DocumentCreationDate | |
| Creation Date in Source | CreationDateInSource | |
|
Any custom metrics defined via the configuration. If you use custom metrics, ensure that you add them to the assignment and layout on the asset type page. |
||
| AI Agent uses / is used by AI Model Version | AIAgentUsesAIModel | |
| AI Agent has version / is version of AI Agent Version | AgentHasVersion | |
| AI Agent has agent config stored in / contains config files for Storage Container | AIAgentContainsFileContainer | |
| AI Agent Version | Version | Version |
| AI Agent has version / is version of AI Agent Version | AgentHasVersion | |
| AI Agent Version can call / can be called by AI Agent Tool | AgentVersionCallsTool | |
| AI Agent Tool | AI Agent Version can call / can be called by AI Agent Tool | AgentVersionCallsTool |
| Azure AI Foundry Project | Project ID | ProjectId |
| Location | Location | |
| URL | Url | |
| AI Endpoint | Access Method | AccessMethod |
| Access Instructions | AccessInstructions | |
| Traffic Split | TrafficSplit | |
| AI Monitor | Data Drift Detection Enabled | DataDriftDetection |
| Prediction Drift Detection Enabled | PredictionDriftDetection | |
| Schedule | Schedule | |
| Alert Configuration | AlertConfiguration | |
| URL | Url | |
| File | Description | Description |
| URL | Url | |
| Document size | DocumentSize | |
| Storage Container |
Operational trust metrics
The Azure AI Foundry integration also reports operational trust and token consumption metrics, sourced from Azure's continuous evaluation feature (pass/fail) and Azure Monitor (token counts).
| Asset type | Operational trust metrics | Token consumption metrics |
|---|---|---|
| AI Agent / AI Agent Version | Daily pass/fail results from Azure's Continuous Evaluation judges | Daily input and output token counts |
- Like attributes, these metrics are stored internally. However, unlike attributes, these aren't accessible through the Collibra Platform APIs.
- These metrics power the Agents dashboard, the Quality tab on AI Agent assets, and the Operational Health theme on the AI Trust Score.
- The metrics run on their own monitoring schedule, separate from the regular synchronization schedule.
- Azure's continuous evaluation feature (pass/fail) doesn't exist for agents built through the legacy Assistants API or for agents hosted on a classic Azure ML workspace. Those agents are still cataloged but without a trust score.
- If the monitoring job hasn't run in over 61 days for a deployment, only the metrics for the most recent 61 days are fetched automatically. Older gaps can only be backfilled manually.
- No additional Azure permissions are required to ingest operational trust or token consumption metrics.
To explore these metrics, in Settings, under AI Command Center, switch on Operational trust for AI agents.
For more information, go to Operational trust for AI agents and AI monitoring for AI agents.