Integrated Gemini Enterprise Agent Platform data

After the synchronization, the Gemini Enterprise Agent Platform (formerly Vertex AI) 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.

The Default Asset Status field in the capability determines the status of synchronized assets.

If you select No Status, newly created assets receive the first status listed in your Operating Model statuses, and existing assets keep their assigned status.
If you select Implemented, all assets receive the "Implemented" status.

Note For more information about AI Model Version asset statuses, go to Configure the lifecycle stages of AI Governance assets.

Synchronized metadata per Gemini Enterprise Agent Platform asset type

Note If you remove a label in Gemini Enterprise Agent Platform after a Gemini Enterprise Agent Platform synchronization, the attribute for the removed label is removed when you integrate Gemini Enterprise Agent Platform again.

This table shows the metadata for the Gemini Enterprise Agent Platform 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.

The Gemini Enterprise Agent Platform integration only ingests the latest metrics from Gemini Enterprise Agent Platform models that have been evaluated and added to the model registry in Gemini Enterprise Agent Platform.
The Gemini Enterprise Agent Platform integration currently only ingests AI agents hosted on Agent Runtime. This includes ADK agents hosted on Agent Runtime and more generally any agent deployed on Agent Runtime regardless of framework. An AI Agent Version asset is only created for agents deployed using a source-code-based method.

Asset type	Synchronized metadata	Public ID
AI Base Model	Description from source system	DescriptionFromSourceSystem
Vertex AI Model Version	Description from source system	DescriptionFromSourceSystem
	Framework	Framework
	Model Accuracy	ModelAccuracy
	Model Precision	ModelPrecision
	Mean Squared Error	MeanSquaredError
	Mean Absolute Error	MeanAbsoluteError
	Feature Importance	FeatureImportance
	Version	Version
	Supported Input Modalities	SupportedInputModalities
	Supported Output Modalities	SupportedOutputModalities
	Supported Model Customizations	SupportedModelCustomizations
	Any custom labels defined via the configuration. If you use custom labels, ensure that you add them to the assignment and layout on the asset type page.
AI Model Deployment	Description from source system	DescriptionFromSourceSystem
	Initiating User in Source	InitiatingUserInSource
	Creation Date in Source	CreationDateInSource
	Modification Date in Source	ModificationDateInSource
	Implemented Content Filtering	ImplementedContentFiltering
	Compute Configuration	ComputeConfiguration
AI Agent	Description from source system	DescriptionFromSourceSystem
	Creation Date in Source	CreationDateInSource
	Tool Usage	ToolUsage
	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 has version / is version of AI Agent Version	AgentHasVersion
AI Agent Version	Creation Date in Source	CreationDateInSource
	Version	Version
	AI Agent has version / is version of AI Agent Version	AgentHasVersion
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	URL	Url
Storage Container	URL	Url
Database
Schema
Table	Description from source system	DescriptionFromSourceSystem
Table	URL	Url
Column	Description from source system	DescriptionFromSourceSystem
Column	Technical Data Type	TechnicalDataType

Operational trust metrics

The Gemini Enterprise Agent Platform integration also collects operational trust metrics for AI agents from Google Cloud Monitoring.

Asset type	Operational trust metrics	Token consumption metrics
AI Agent / AI Agent Version	Daily pass and fail rates	Daily input and output token counts, if the agent self-instruments them

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.
Metrics are updated once per day.
Your agent identity needs the roles/monitoring.metricWriter permission. For more information, go to Create a Google Cloud Platform connection to an Edge or Collibra Cloud site.
Token consumption self-instrumentation: Unlike pass/fail, there is no built-in Google Cloud Monitoring signal for token counts. Token metrics appear only if your agent code emits a custom metric:
- Metric name: workload.googleapis.com/collibra.agent.token_count.
- Metric kind: a monotonic counter, such as an OpenTelemetry Counter, not a gauge or up-down counter. Since the integration reads it as a cumulative value, a non-cumulative instrument causes the read to fail right away.
- Required labels: reasoning_engine_id, location, and direction (input or output).
- Optional label: project_id, only if the agent writes to a different project than it runs in.
Malformed or incomplete emissions, such as missing labels, are logged and skipped. You can check label names/values first if no token data appears.

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.