Most business questions already have answers sitting in a warehouse. The bottleneck is the queue: someone files a request, waits for an analyst, and gets a report built from last week’s numbers. On September 10, 2026, OpenAI introduced a Data agent in ChatGPT Work that aims to remove that queue by letting people ask in plain language and get analysis back in the same conversation.
What the agent actually connects to
The Data agent links to approved sources including Amazon Redshift, Datadog, Google BigQuery, ClickHouse, Databricks, MongoDB, and Snowflake, and can pull files from Google Drive and SharePoint into an analysis. It also reads your organization’s business terms, metric definitions, custom calculations, and data relationships, drawing that context from semantic layers and trusted sources such as Databricks Genie Ontology, dbt, GitHub, Snowflake Horizon, and BI dashboards.
That second part is the interesting one. The agent is not guessing what “active customer” means. It is reading the definition your team already agreed on. Administrators still choose which connections exist and which roles can use them, and queries enforce the connected account’s existing table, row, and column restrictions.
From question to dashboard to action
You can ask follow-ups, inspect the evidence behind a finding, and turn the result into an interactive dashboard with built-in visualizations that teammates can edit, share, and refresh. Brand guidelines can be applied to outputs. The agent can also build and interact with dashboards in Omni, Oracle BI, Power BI, Sigma, Tableau, and ThoughtSpot, so the work can land in the BI tool a team already uses. It can suggest next steps, identify who needs to be involved, and share findings through Slack or email, carrying out actions you approve through connected tools.
OpenAI says nearly all of its product team and over two-thirds of its GTM organization use data agents in ChatGPT Work to analyze company data themselves, and that its data team enabled this by creating shared business definitions, setting access rules, and adding safeguards for sensitive data. NTT Data, Thermo Fisher, ServiceTitan, and Zipline are among the organizations in the Alpha program using it.
The part that is actually your job
Strip away the interface and the dependency is unglamorous: the agent is only as good as the definitions it can read. If your metrics live in three conflicting spreadsheets and a dashboard nobody trusts, natural-language access just makes the disagreement faster. If your semantic layer is clean, the agent becomes a new front end onto work you already did.
This is the same lesson that shows up whenever an agent is handed a whole system: the integration surface, not the model, decides whether the result is trustworthy. That pattern is worth revisiting in what it takes to hand an agent the whole system, because the Data agent inherits exactly those constraints.
For builders, the practical move is to treat the semantic layer as a product with users, not as plumbing. Name owners for metric definitions. Decide which roles get which connections before someone asks. Test the agent against questions where you already know the right answer, so you can tell a confident wrong number from a correct one. The supplied material does not specify pricing, rollout limits, or how conflicts between two definitions are resolved, so those are open questions rather than settled ones.
The queue does not disappear on its own. It moves from “waiting for an analyst” to “waiting for someone to fix the definitions.”
Sources
AI-assisted summary compiled from the sources above, reviewed by a human before publishing.