Deploying the Gemini Enterprise app across an organization marks a transformative leap forward in workforce productivity, providing employees with an amazing, high-performance suite of agentic AI tools, search-grounded assistants, and specialized solutions like NotebookLM. As adoption grows to a large scale, it can introduce a critical administrative scale challenge: how to audit, govern, and extract insights from a massive volume of telemetry without getting bogged down in manual overhead. To help administrators succeed, Google Cloud provides comprehensive, out-of-the-box analytics via pre-computed dashboards to track day-to-day adoption, user engagement, and active user metrics. While this provides a product-centric lens to look at Gemini Enterprise app’s usage, to understand the impact of agentic AI, administrators might need a more nuanced, organization-centric perspective tailored to their own internal context. This is where using Google BigQuery becomes a crucial tool in the administrator’s arsenal to run deep-dive forensics across their organization to analyze and govern the adoption of agentic AI.
Why Gemini Enterprise app + BigQuery is a game-changer
Augmenting the Gemini Enterprise app with BigQuery through log sinks allows a lean administrative team to analyze and govern a large-scale deployment. Specifically, it empowers IT, Data, and Security teams to:
-
Profile nuanced adoption and behaviors: Segment usage patterns by department to see which teams are building custom agents, track NotebookLM utilization, and calculate agent-to-employee ratios.
-
Quantify organizational value: Combine conversational logs with HR or line-of-business datasets to calculate actual employee hours saved, trace value creation, and build executive Looker dashboards.
-
Execute precision compliance audits: Audit grounding queries across Google Drive folders and enterprise directories to prevent data leaks and protect corporate IP.
-
Investigate safety alerts instantly: Query historical logs when security filters flag a prompt, identifying the exact text that triggered a Model Armor block to resolve compliance alerts.
To support these use cases, the telemetry is partitioned into five distinct log tables in BigQuery, capturing unique data fields:
|
BigQuery Destination Table |
Telemetry Captured |
|---|---|
|
Gen AI User Messages `discoveryengine_googleapis_com_g |
Verbatim prompt inputs typed by users |
|
Gen AI Choices `discoveryengine_googleapis_com_g |
Verbatim model responses, finish reasons, and LLM reasoning steps |
|
User Activity Telemetry `discoveryengine_googleapis_com_g |
Corporate identity (IAM emails) and grounding file access paths |
|
Cloud Audit Activity `cloudaudit_googleapis_com_activity` |
Control plane configuration changes and administrative user logs |
|
Cloud Audit Data Access `cloudaudit_googleapis_com_data_ac |
High-volume data plane interactions and search queries |
|
Aggregate OOB Metrics (Batch Export Table) |
Pre-aggregated seats claimed, seat purchases, and engagement metrics from the past 30 days. To be pulled asynchronously via custom daily batch runs of the analytics:exportMetrics API to build high-level adoption and cost dashboards. |
Ingestion pipeline and architecture
To implement scale-ready observability, administrators establish an automated telemetry pipeline. Moving your Gemini Enterprise data to BigQuery does not require complex custom software development; instead, it leverages a continuous Cloud Logging Log Router Sink for conversational logs and an asynchronous batch export API for high-level aggregate seat metrics.
The diagram below illustrates the ingestion pipeline and how telemetry is mapped to BigQuery:
