Most teams have more answers sitting in their data warehouse than they realize. The challenge is getting those answers into more people's hands, in the moment they need them, without requiring everyone to become a SQL expert first. That's where BigQuery data agents come in. Built on Google's conversational analytics framework, these agents let business users ask questions about their data in plain language and get back grounded, accurate answers pulled straight from BigQuery.
But building an agent that actually works in an enterprise environment takes more than flipping on a feature. It takes thoughtful preparation: curated tables, verified queries, a glossary of your team's specific terminology, and guardrails to keep costs and accuracy in check. Here's how we’ve approached that work at Adswerve for two recent clients, and what it looks like to get a BigQuery agent from proof of concept to daily use.
Every large enterprise has some version of the same workflow: an analyst manually pulls data, writes commentary about what the numbers mean, formats a report, and sends it to a stakeholder group on a recurring schedule. It gets the job done, but it also consumes hours that the analyst could spend on higher-value work.
That reporting bottleneck is where BigQuery agentic workflows start to make a real difference. We recently built a BigQuery agent for a major automotive manufacturer that needed to automate a weekly funnel report. The report combined pipeline metrics, KPI comparisons against prior periods, and written commentary explaining what shifted and why. Dashboards alone couldn't solve it, because the commentary was just as important as the numbers.
Generic AI tools couldn't solve it either. Feeding a past report into a general-purpose model and asking for next month's version sounds reasonable, but the model has no way of knowing what the correct numbers are for the current period or what the team's metric definitions mean. It might produce something that reads well and is still wrong.
The BigQuery agent we built approached this differently. We prepared clean, pre-aggregated tables and mapped specific questions to specific queries, so the agent wouldn't have to write complex SQL on its own or guess at metric definitions. Past reports were included as style references so the output matched the format the marketing team already expected. From there, an analyst could type a single prompt ("write me a weekly report"), and the agent would query the right tables, pull current numbers, and generate the first draft of that report, complete with commentary.
With this agentic approach, the client was able to save about two to three hours per reporting cycle, giving that time back to the analyst for more strategic work. And because the agent was grounded in curated data rather than general knowledge, the numbers it produced were verifiable against the same tables the team already trusted.
Another other drain on analyst time is ad-hoc requests: one-off questions from stakeholders across the business who need a data pull but don't have the SQL skills to get it themselves.
We built another BigQuery agent for a financial services client facing exactly this situation. The company had one data analyst fielding a constant stream of requests from internal teams, each needing a specific cut of their Google Analytics 4 (GA4) data.
The agent we configured was connected to a single, clean Google Analytics event table updated daily via a scheduled query that wrote the latest three days of data. Verified queries handled the most common request types, like identifying which marketing channels drove the most sessions. Those verified queries mattered, because Google Analytics stores marketing channel data across multiple fields, and an unguided agent could easily pick the wrong one.
The agent also included a business glossary that defined client-specific terms. When someone asked about "calculator submissions" or "form submissions," the agent already knew which Google Analytics events those terms mapped to, so it could skip the exploratory queries that would slow it down and risk pulling incorrect results.
Just as importantly, the agent was built to push back when it didn't have enough context. During testing, questions like "what's the conversion rate?" prompted the agent to ask a clarifying question rather than guessing which conversion action the user meant.
Both of these examples point to the same principle: an analytics agent itself is only as useful as the preparation behind it.
Grounding is the work that keeps an agent's answers tied to your actual data and definitions rather than whatever the underlying model assumes on its own. Without it, an agent might answer confidently while getting the details wrong, treating "session" as a generic concept when your team uses it to mean something specific, or including canceled transactions in a total it shouldn't.
The grounding work we do for each BigQuery agentic workflow includes three components.
This preparation is also what distinguishes a purpose-built BigQuery agent from a general-purpose coding assistant. A tool without this context would burn through tokens doing exploratory research, potentially ask the user technical questions they can't answer, and still produce results that need manual verification.
The one caveat with any BigQuery agentic workflow is that the agent surfaces answers from whatever data it has access to. If the underlying data has quality issues, such as broken tagging, inconsistent event tracking, or misconfigured implementations, the agent will reflect those problems in its answers. An agent shouldn’t be a shortcut past data governance, which is why it’s so important to build a strong data foundation for AI use cases.
Cost controls also matter. BigQuery agents consume Gemini tokens with each interaction, and connecting an agent to large, unscoped tables can generate expensive queries. We mitigate this by limiting each agent to a single prepared table, capping the query time range, and using scheduled queries to keep that table current without requiring manual refreshes.
On the deployment side, queries run using individual user credentials rather than a shared service account. That preserves auditability, so you can see which queries belong to which user, and ensures each person only accesses data they're authorized to see. Users can interact with the agent through a Data Studio interface for a cleaner experience, though BigQuery access is still required on the backend.
If your team has clean, structured data in BigQuery and recurring questions that follow a pattern, an agentic workflow can start returning time to your analysts quickly.
If you're exploring what BigQuery agentic workflows could look like for your team, whether that's automating a recurring report, fielding ad-hoc requests, or something else entirely, we'd love to talk through what's possible for your environment.