Looker + Gemini Enterprise: Trusted Data for AI Agents

Google Cloud just dropped a big one for teams wrestling with AI hallucinations and messy enterprise data. Looker’s semantic layer is now the trusted backbone for Gemini Enterprise, giving your AI agents a governed, deterministic path to your structured data.

Think of it this way: LLMs are brilliant at chewing through documents and emails, but hand them a raw database and they start guessing—joining tables willy-nilly, applying wrong filters, and spitting out metrics that don’t match your boardroom numbers. That’s a swamp of inconsistency, and it erodes trust fast.

With this integration, Looker’s semantic layer becomes the clean channel through which Gemini Enterprise queries your data. No more guesswork—just precise, governed answers.

What’s Actually New?

Looker admins can now publish conversational agents directly into Gemini Enterprise using the Agent-to-Agent (A2A) protocol. These agents act as a bridge between natural language and your data warehouse, generating deterministic SQL based on version-controlled business logic.

So when an exec asks for “Revenue,” they get the revenue—the exact, board-approved metric—not a random number from a rogue join.

Killing Hallucinations at the Root

Here’s the problem with typical NL2SQL: it guesses how schemas fit together. Different people ask the same question, get different answers, and suddenly your data culture is a swamp of doubt.

Looker’s semantic layer eliminates that by codifying business definitions. The AI doesn’t guess—it reads the rulebook. That means consistent, predictable responses every time.

Security That Doesn’t Cut Corners

Security is a big deal when you’re letting AI loose on enterprise data. This integration uses a zero-risk pass-through architecture—Gemini Enterprise doesn’t ingest, replicate, or store your underlying database records. It just queries live.

Here’s how it stays locked down:

• OAuth authorization: Users give one-time consent, binding their Gemini session to their Looker credentials.
• Governance enforcement: Row-level and column-level access controls from Looker are maintained on every query.
• Strict isolation: If a user can’t see sensitive rows in Looker, they can’t see them in Gemini—even if the agent is published to the Agent Gallery.

Visuals, Interop, and Enterprise Readiness

This isn’t just text answers. Looker agents in Gemini Enterprise can render interactive charts right in the chat interface. Ask for monthly sales trends, and you get a presentation-ready visualization, not just numbers.

Plus, these agents play nice with others. They can share insights with first-party agents like Deep Research or third-party tools, enabling complex multi-agent workflows. And authentication is identity-centric, so every query is tied to a specific user’s permissions.

If you published Looker agents before release 26.12, you’ll want to refresh them to get the new visualization features.

The Bottom Line

This is a solid step toward making AI agents actually trustworthy for enterprise analytics. By anchoring Gemini Enterprise to Looker’s governed semantic layer, Google Cloud is giving teams a way to explore data in plain English without sacrificing accuracy or security.

For more on how Google Cloud stacks up against other platforms, check out our Google Cloud review. And if you’re comparing AI model costs, our pricing comparison might help.

Ready to dive in? Learn how to publish your data agents in Gemini Enterprise and start building a data-driven culture that actually trusts its numbers.

Original announcement published on Google Cloud.