What can an AI agent do with Google BigQuery?

A BigQuery AI agent can run SQL queries across your datasets, read table schemas, list available tables and datasets, fetch query job results, and generate plain-language analytical reports. It can deliver scheduled summaries to Slack, Sheets, or email, and alert you when key metrics shift. Write actions like creating datasets or inserting rows require your approval.

Which BigQuery projects can my agent access?

Your agent can access any project your connected Google Cloud service account has permission to query. You can scope tasks to specific datasets or tables in the task instructions.

Can my agent write SQL queries on its own?

Yes. You describe what you want in plain language and your agent writes and runs the SQL. You can also provide the query directly if you prefer exact control.

How is a BigQuery AI agent different from Looker or Data Studio?

Looker and Data Studio require you to build dashboards in advance and check them manually. A BigQuery AI agent runs queries on demand, interprets the results, and delivers a plain-language summary to wherever your team works - no dashboard required.

Analytics

Google BigQuery, run by an agent.

An AI agent that queries your BigQuery datasets, reads table schemas, and generates analytical reports.

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What needs your approval

Google BigQuery never changes anything without you.

Anything that creates, edits or deletes in Google BigQuery waits for your approval. Approve a recurring task once and it runs unattended after that.

Example tasks

Real work people hand to an agent that uses Google BigQuery.

run a daily revenue summary query and post key metrics to Slack every morning
query user activity tables to find churn signals and export results to a Google Sheet
generate a weekly product usage report from event data and email it to your team
monitor table row counts on a schedule to catch pipeline failures before they cause downstream issues