The universal semantic layer agents
At the core of the agentic transition is Looker’s semantic layer. In a world where hallucinated data and conflicting metrics can kill business, LookML is critical for customers such as YouTube, Telenor and Allo Fiber, deploying agents into production at scale and keeping them grounded in verified enterprise truth.
Key governance strengths include:
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Unified analytics governance: Centralized, code-based semantic layer that guarantees metric consistency across the organization. Pairing this single source of truth with hierarchical permissions and a new certification framework controls content trust levels and enables proactive platform auditing.
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Governed semantic layer for in-database analytic and graph modeling: Native integration with in-database analytic models, including BigQuery Graph and Snowflake semantic views, enables organizations to define, version-control, and manage complex relational and graph-based data relationships natively within LookML while maintaining a consistent semantic truth across external applications.
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Enterprise lifecycle management: Native, git-based version control supports continuous integration testing and seamless multi-environment management before production deployment.
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High concurrency architecture: Elastic resource allocation mitigates usage spikes, allowing teams to deliver scalable insights during heavy user demand.
Powered by Gemini’s reasoning capabilities
True agentic business intelligence requires deep cognitive reasoning. Looker’s purpose-built BI generative AI capabilities with Gemini 3 serve as Looker’s native AI fabric, unlocking two significant advantages across the enterprise:
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For business users: It enables complex, multi-layered strategic analysis through advanced, natural-language abstract reasoning.
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For developers: It embeds directly into the daily workflow to dramatically accelerate analytics engineering with reliable LookML auditing and automated code writing.
Leaping into the Agentic BI era with Looker and Gemini
Gemini is powering the reimagination of the Looker stack from agentic semantic modeling, data explorations, Dashboard Agents, to Conversational Analytics. At Google Cloud Next 2026, we showcased our latest Looker product innovations:
- Looker everywhere: We are expanding Looker’s footprint beyond traditional interfaces through headless BI architectures, highlighted by Looker’s Managed MCP offering. This capability brings our semantic intelligence directly to external platforms, as showcased by PayPal, which successfully scaled accurate conversational analytics to 3,000+ users via Claude Desktop and Looker MCP. Developers can also leverage the Conversational Analytics API to build, secure, and deploy custom, trusted data agents within any proprietary application or third-party agent platform.
- Looker BI Agents: Our specialized conversational agents allow users to query complex data models across applications using plain natural language, while dashboard agents embed these interactive conversational experiences directly into existing dashboards. Fully integrated with Gemini Enterprise, these agents can be deployed straight into your corporate workspace workflows. Furthermore, across all environments, these agents enable users to orchestrate autonomous agentic workflows and monitor execution, helping ensure teams can interact with LookML-governed metrics and trigger automated actions right where they already cooperate.
- AI-powered self service: We’ve completely reimagined Explore Mode, combining an intuitive drag-and-drop canvas with conversational analytics. Tools like the Visualization Assistant use natural language to design beautiful charts on the fly, while the Insight Assistant auto-generates narratives to surface key trends in seconds. This sits alongside now generally available (GA) paginated reporting and tabbed dashboards.
- Vibe-coding with the LookML Agent: This specialized AI agent and new VS Code extension enable full-lifecycle LookML development, management, and deployment entirely outside of Looker in any VS Code-based IDE. The agent accelerates semantic modeling by translating natural language into LookML, generating models directly from existing BigQuery/AlloyDB datasets, and integrating easily with existing agentic skills. Allowing developers to develop and interact with Looker within a single interface delivers a highly cohesive and accelerated environment.
- Universal semantic layer: With the new in-database analytics model support, LookML can support graph models and complex semantic ontologies with BigQuery Graph and Snowflake Semantic Views. LookML can flex to a broad set of governed use cases for data agents for industries like retail, supply chain, cybersecurity, with graph relationships alongside table-based models for scale-out BI agents.
By combining Looker’s analytical governance with Google’s Agentic Data Cloud, we help ensure your autonomous AI agents operate on verified enterprise metrics, not hallucinated guesswork. Whether you are deeply embedded in the Google Cloud ecosystem or leveraging Looker across a multi-cloud data architecture, Looker provides the openness, scale, and semantic grounding required to power the future of business.
Gartner Magic Quadrant for Analytics and Business Intelligence Platforms – Anirudh Ganeshan, Edgar Macari, Christopher Long, June 29, 2026
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