# Looker’s semantic layer governs Gemini Enterprise data for user trust > Source: > Published: 2026-08-11 16:00:00+00:00 For organizations deploying AI agents at scale, there’s often a critical divide between structured and unstructured data. While large language models (LLMs) excel at parsing text documents, emails, and PDFs, they can struggle when presented with raw enterprise databases. Meanwhile, standard natural-language-to-SQL (NL2SQL) models often guess how database schemas fit together, which can lead to unpredictable queries, inconsistent metrics, and AI hallucinations that erode user trust. [Gemini Enterprise](https://cloud.google.com/gemini-enterprise?utm_source=google&utm_medium=cpc&utm_campaign=1713704-Workspace-DR-APAC-IN-en-Google-BKWS-MIX-Hybrid-GeminiEnterprise&utm_content=c-Hybrid+%7C+BKWS+-+EXA+%7C+Txt-Gemini+Enterprise-Generic-435278751514&utm_term=gemini+enterprise&gclsrc=aw.ds&gad_source=1&gad_campaignid=23381004221&gclid=Cj0KCQjwlqTRBhCBARIsANrkrxgdte2Ry_iXPSiT0N7_AEygV0lPiuKDnLLm9uPdxiQKE39HhLfoQsgaAgdmEALw_wcB&e=0) brings the best of Google AI to every employee through an intuitive chat interface that acts as a single front door for AI in the workplace. And now, Looker’s governed semantic layer serves as the trusted foundation for structured data within Gemini Enterprise, enabling trusted self-service business intelligence for all Gemini Enterprise users. With this integration, Looker analysts and admins can publish conversational agents natively into their Gemini Enterprise environments via the Agent-to-Agent (A2A) protocol. Now, organizations can provide their AI-accelerated taskforce with robust and trusted tools, powered by real-time analytics, that they can explore in natural language in addition to their daily workspace workflows. Making it easy to offer conversational agents in Gemini Enterprise expands discoverability and promotes a data-driven culture, while reducing friction to adoption. By combining Looker’s semantic layer with Gemini Enterprise, you can query both structured databases and unstructured documents in plain English, all in one place. Instead of jumping between dashboards and other tools to understand your numbers, teams can instantly connect hard metrics with real-world context to solve problems and make decisions faster. If you ask the typical AI chatbot to calculate "revenue" or "churn rate" against an unstructured cloud database, it has to guess which tables to join, which filters to apply, and which timestamps to trust. This can result in different people asking the same question, only to get completely different answers. Looker’s semantic layer eliminates this guesswork, serving critical context to Gemini Enterprise in the form of codified data, allowing the agent to give deterministic, predictable responses. When a Gemini Enterprise user requests a business KPI in Gemini Enterprise, the request is routed directly to a Looker agent. The semantic layer generates deterministic, precise SQL based on version-controlled business logic. This helps ensure when an executive asks for "Revenue," they get the exact, governed enterprise metric — not a guess. Data governance and security are critical when introducing AI to enterprise data warehouses. Organizations can’t risk corporate information being loosely ingested, indexed, or exposed outside of strict permissions. Looker’s integration with Gemini Enterprise is built on a zero-risk pass-through architecture, processing the data, but not writing to persistent storage. Gemini Enterprise does not ingest, replicate, or store your underlying database records. Instead, the integration operates safely and securely over the A2A protocol, following these core tenets: **OAuth authorization:** In order to interact with a Looker agent within Gemini Enterprise, end users provide a secure, one-time OAuth consent. This binds their Gemini session to their specific Looker credentials. **Strong governance enforcement:** Because the architecture relies on live pass-through queries, Looker’s existing row-level and column-level access controls are maintained. **Strict security isolation:** If a user does not have permission to view, say, sensitive regional payroll or financial rows within the Looker platform, the Looker agent actively restricts that data in the Gemini environment. Should an agent be published to the Agent Gallery to simplify discovery, it still does not bypass the security controls that you established. Deploying Looker agents natively into Gemini Enterprise via the [A2A protocol](https://docs.cloud.google.com/gemini/data-agents/conversational-analytics-api/integration-patterns#a2a-orchestration) doesn't just make it smarter — it makes it more interactive and interoperable, without sacrificing security. Here are some of the features you’ll find in this release. **Rich visual interactivity: support for charts** They say a picture is worth a thousand words. When users interact with Looker agents inside Gemini Enterprise, the platform goes beyond textual explanations and provides native, interactive data charts. If a user asks for a visual trend—such as monthly sales performance or regional distribution—the Looker agent maps the database response with rich, presentation-ready visualizations directly inside the universal chat box. **Note:** If you published Looker agents in Gemini Enterprise prior to Looker release 26.12, we recommend [updating or refreshing](https://docs.cloud.google.com/looker/docs/conversational-analytics-looker-data-agents#republish-agent-ge) them to take advantage of these enhanced visualization capabilities. **I****nteroperability with first- and third-party agents** Looker agents published to Gemini Enterprise can understand context across different agents and data sources. Leveraging standard communication frameworks, these agents can securely share structured, governed insights with other first-party Google Cloud agents like the [Deep Research Agent](https://ai.google.dev/gemini-api/docs/deep-research) or external third-party agents to create structured workflows. This enables complex multi-agent orchestration, where an enterprise operational agent can pull data from a Looker agent to feed into a separate productivity or supply-chain workflow. **Looker-based user authentication** To preserve enterprise governance, this integration implements a robust, identity-centric authentication model. Users are required to provide a one-time OAuth consent, binding their active Gemini Enterprise session securely to their underlying Looker credentials. This helps ensure that every conversational query hitting your databases is authenticated at the user level, enforcing pre-existing Looker permission structures, row-level data access filters, and column-level masking rules — no exceptions. The future of work is agentic. Gemini Enterprise provides a single, secure architecture to deploy a global digital task force,empowering your business with the best of Google AI for developers, employees, and customers. The integration of Looker with Gemini Enterprise not only brings trusted data analytics to business users but also adds rich interactivity, visual charts, and data storytelling directly into their everyday workspace. As business users embrace this agentic new way of working, they aren't just getting text answers; they are getting presentation-ready visualizations that bring operational metrics to life and deliver complex insights. To get started, [learn how to publish your data agents in Gemini Enterprise](https://docs.cloud.google.com/looker/docs/conversational-analytics-looker-data-agents#publish-data-agents) to make your agent’s predefined context and analytics available to your entire organization.