Integration guide
Operations and Data
Ground Agentforce in serverless BigQuery analytics
BigQuery is Google Cloud's serverless warehouse: datasets and tables you query without managing infrastructure, described in its Knowledge Catalog so the meaning of a field is captured alongside the data. When Emerge Digital connects it to Salesforce Agentforce, an agent can answer from those datasets without anyone standing up servers, and the catalog helps it reach the right table for a question. IAM decides access, so the agent reads the same governed analytics your teams already run in Google Cloud.
Discuss your Google BigQuery integrationThe opportunity
What the connection unlocks
- Agents query BigQuery datasets and tables on demand, and because the warehouse is serverless there is no cluster to provision for an agent's questions to run.
- The BigQuery Knowledge Catalog gives agents described, discoverable data, so a question can be pointed at the table that actually means what it asks.
- IAM roles and dataset permissions scope what each agent may read, keeping a customer-facing agent and an internal one safely inside their own grants.
- Data structured in Google's Open Knowledge Format lines up naturally with BigQuery's Knowledge Catalog, so knowledge you keep OKF-compliant is ready to be reasoned over here.
Illustrative workflows
Where it can make a difference
These scenarios explain possible workflows. They are not claims of delivered client results; licensing, permissions and feasibility are confirmed during discovery.
Answer from Google Cloud analytics
A rep asks about product usage and the agent queries the relevant BigQuery dataset directly, so the reply reflects the serverless warehouse your analytics team already reports from rather than a stale copy.
Reach the right table by meaning
When a question is ambiguous, the agent uses the Knowledge Catalog's descriptions to find the dataset that fits, so it answers from the table that genuinely holds the metric instead of guessing at a name.
Journey fit
Connect the workflow to the outcome.
BigQuery supports the measured end of the journey — Convert and Optimize — where serverless analytics tell an agent what is actually happening. Because BigQuery sits inside Google Cloud and pairs with the OKF story, an agent grounded here connects governed warehouse data with OKF-structured knowledge, so a recommendation reflects both the numbers and the documented context behind them.
Delivery
Built around your environment.
Emerge Digital integrates BigQuery with Agentforce as a consulting engagement, not an app you install yourself. We map which datasets and tables your agents need, set the IAM roles and dataset permissions that govern access, and build the retrieval and actions that let an agent query the warehouse safely — drawing on the Knowledge Catalog so questions land on the right data. Because Emerge's VaultOS is OKF-compliant, knowledge you keep in that format sits comfortably next to BigQuery's Knowledge Catalog when you extend the picture. Access is governed by IAM rather than prompt wording, and a person stays in the loop where a figure carries weight.
Explore AI and automationPractical questions
Before we connect.
Do we need to run infrastructure for agents to query BigQuery?
No. BigQuery is serverless, so there is no cluster to provision for an agent's queries — Emerge configures the access and retrieval, and the warehouse scales the work itself.
Is this a pre-built BigQuery connector we install ourselves?
No. Connecting BigQuery to Agentforce is a services engagement. Emerge maps the datasets, sets IAM permissions, and builds the retrieval and actions around your Google Cloud setup rather than shipping a self-install plugin.
How does OKF relate to BigQuery here?
OKF is Google's Open Knowledge Format for structuring knowledge, and BigQuery's Knowledge Catalog describes your data's meaning. Keeping knowledge OKF-compliant — as Emerge's VaultOS does — means it aligns with that catalog so agents can reason over both together.
Keep exploring
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