--- title: Google's Agentic Data Cloud: what it is and what... description: Google Cloud Next 2026 centered on agentic AI as shipping product across security, data, and developer tooling. Key announcements include AI-powered SOC agents... canonical: https://daily.dev/posts/google-s-agentic-data-cloud-what-it-is-and-what-analysts-are-skeptical-about-yzjum6sjp twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Google's Agentic Data Cloud: what it is and what analysts are skeptical about | daily.dev og:description: Google Cloud Next 2026 centered on agentic AI as shipping product across security, data, and developer tooling. Key announcements include AI-powered SOC agents... og:url: https://daily.dev/posts/google-s-agentic-data-cloud-what-it-is-and-what-analysts-are-skeptical-about-yzjum6sjp og:image: https://api.daily.dev/og/posts/yzJum6SJp.png og:image:alt: Google's Agentic Data Cloud: what it is and what analysts are skeptical about og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Google's Agentic Data Cloud: what it is and what analysts are skeptical about **[Collections](https://daily.dev/sources/collections)** · 5 min read · 1 upvotes · 0 comments ## Summary Google Cloud Next 2026 centered on agentic AI as shipping product across security, data, and developer tooling. Key announcements include AI-powered SOC agents cutting alert triage from 30 minutes to 60 seconds, the completed Wiz acquisition for cross-cloud AI security, and the Gemini Enterprise Agent Platform with components for routing, governance, identity, and long-running agents. Google rebranded its data portfolio as the 'Agentic Data Cloud' — an AI-native cross-cloud lakehouse built on BigQuery, Spanner, AlloyDB, and Looker, all now supporting Model Context Protocol. A new Cloud Data Agent Kit integrates with VS Code, Gemini CLI, and Claude Code. New hardware includes TPU 8t and 8i, while Claude 4.6 is now available on Vertex AI. Google also quietly launched the Universal Commerce Protocol for agentic transactions. Analysts note real governance, vendor lock-in, and cost risks as enterprises begin deploying autonomous agents at scale. ## Content ## What Google announced Google Cloud Next 2026 was heavy on agentic AI — not as a concept but as shipping product. The announcements span security, data infrastructure, developer tooling, and enterprise workflows, so here's what actually matters and why. --- ## Multicloud security and AI-powered SOCs Google Cloud COO Francis deSouza made the case that a durable AI roadmap requires multiple models and multiple cloud providers — not just Google's stack. The argument is straightforward: no single vendor will win every model benchmark, and enterprises that bet on one provider are setting themselves up for painful migrations later. On the security side, the numbers are striking. AI-powered SOC agents are reportedly cutting alert triage from 30 minutes to 60 seconds and reducing threat mitigation time by 90%. Whether those figures hold in production at scale is worth watching, but the direction is clear. The Wiz acquisition is now complete, and Google is positioning it as the layer that secures AI development lifecycles across clouds — not just Google Cloud workloads. That cross-cloud angle is important. If you're running AI pipelines on AWS or Azure, Wiz is supposed to cover you too. --- ## Gemini Enterprise Agent Platform The platform has four pillars: build, scale, govern, and optimize. The practical pieces include: - **Agent Gateway** — routes and manages agent traffic - **Model Armor** — safety and policy enforcement for model outputs - **Agent Identity** — authentication and access control for agents acting on behalf of users - **Agent Designer** — a governance layer that lets teams define what data and tools agents can access - **Agent Gallery** — a catalog of pre-built agents - **Projects** — shared persistent workspaces for team collaboration - **Long-running agents** — agents that can operate over extended timeframes without constant human prompting The design team's UX philosophy is worth noting separately. They built an AI Inbox dashboard for monitoring agent activity and put significant thought into "thinking state" narration — agents explain what they're doing and cite sources as they work. The stated goal is that users should focus on their goals, not on managing the AI. That's easier said than done, but the governance layer (Agent Designer) at least gives teams a concrete way to set boundaries. --- ## Agentic Data Cloud This is Google's rebranding and expansion of its data and analytics portfolio into something it calls an "AI-native cross-cloud lakehouse." The core idea: static data platforms aren't useful to AI agents that need to reason over data dynamically. The Agentic Data Cloud is meant to fix that. Three pillars: 1. **Universal context engine** — a semantic layer that maps business meaning across data sources, built on an evolved Knowledge Catalog (Dataplex Universal Catalog). Native integrations with Salesforce and SAP are included. 2. **Agentic-first developer experiences** — the new Cloud Data Agent Kit (in preview) is a portable suite that plugs into VS Code, Gemini CLI, and Claude Code. It includes specialized agents for data engineering, data science, and database observability, and works with dbt, Apache Spark, and Airflow. 3. **AI-native cross-cloud lakehouse** — BigQuery, Spanner, AlloyDB, Cloud SQL, and Looker all now support Model Context Protocol (MCP) for secure, universal agent access. The competitive framing here is Google vs. Microsoft Fabric IQ and AWS Nova Forge. Analysts are cautiously positive — the semantic layer approach does address a real problem (inconsistent data meaning across enterprise systems) — but they're flagging legitimate concerns: governance risks from automated semantic inference, vendor lock-in at the orchestration layer, and cost unpredictability when agents start dynamically spinning up workflows. BigQuery also got a natural language Comments-to-SQL feature, which is a smaller but genuinely useful addition for analysts who don't want to write SQL by hand. --- ## Hardware and models - **TPU 8t** (8th-gen, training-optimized) and **TPU 8i** (inference-optimized) are now available - **Gemini Embedding 2** — described as the first natively multimodal embedding model - **Veo 3.1 Lite** — a lighter video generation model - **Claude 4.6** is now available on Vertex AI, which fits the multicloud/multi-model narrative --- ## Enterprise AI deployment: what Anthropic is saying Separately from Google's announcements, Anthropic published a guide on enterprise AI transformation that's worth reading alongside all of this. Their framing centers on what they call the "agentic thinking divide" — the gap between organizations that understand how to deploy agents at scale and those still treating AI as a chatbot. Their six-month deployment framework for Claude Cowork (their enterprise offering) covers three areas: employee upskilling, compressing information-dense processes, and building revenue-generating product capabilities. Case studies from L'Oreal, Lyft, and Rakuten are included. The honest takeaway is that most enterprise AI failures aren't technical — they're organizational. Teams don't know how to redesign workflows around agents, and that's where the real work is. --- ## Universal Commerce Protocol One announcement that didn't get much attention: Google launched the Universal Commerce Protocol (UCP), designed to enable agentic commerce — AI agents completing purchases and transactions on behalf of users. It's early, but it's the kind of infrastructure layer that becomes important quietly. --- ## The honest read Google's vertical integration story — hardware, AI models, and security in one stack — does give them a real advantage in deploying new models to security and data products quickly. The multicloud positioning is smart because it's true: enterprises aren't going to run everything on one cloud, and pretending otherwise is a losing strategy. The risks are real too. Agentic systems that autonomously orchestrate data workflows and make purchases introduce governance and cost problems that most enterprises haven't solved yet. The tooling Google announced (Agent Designer, Model Armor, Agent Identity) is a start, but the hard work of actually governing these systems at scale is still ahead of everyone. ## Similar posts on daily.dev - [The top startup announcement from Next ‘26](https://daily.dev/posts/the-top-startup-announcement-from-next-26-ssimjxtxu) · Google Cloud · 0 upvotes · 0 comments - [Welcome to Google Cloud Next26](https://daily.dev/posts/welcome-to-google-cloud-next26-6e0yqgtcz) · Google Cloud · 0 upvotes · 0 comments - [Google Cloud Next 2026: AI agents, A2A protocol, Workspace Studio, and the full-stack bet against OpenAI and Anthropic](https://daily.dev/posts/google-cloud-next-2026-ai-agents-a2a-protocol-workspace-studio-and-the-full-stack-bet-against-op-gudoph7ma) · The Next Web · 0 upvotes · 0 comments - [What Google Cloud announced in AI this month – and how it helps you](https://daily.dev/posts/what-google-cloud-announced-in-ai-this-month-and-how-it-helps-you-r008abf3t) · Google Cloud · 3 upvotes · 0 comments --- Tags: [#gcp](https://daily.dev/tags/gcp), [#ai-agents](https://daily.dev/tags/ai-agents), [#data-engineering](https://daily.dev/tags/data-engineering), [#mcp](https://daily.dev/tags/mcp), [#google-bigquery](https://daily.dev/tags/google-bigquery) [View this post on daily.dev](https://daily.dev/posts/google-s-agentic-data-cloud-what-it-is-and-what-analysts-are-skeptical-about-yzjum6sjp) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Google's Agentic Data Cloud: what it is and what analysts are skeptical about","url":"https://daily.dev/posts/google-s-agentic-data-cloud-what-it-is-and-what-analysts-are-skeptical-about-yzjum6sjp","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/google-s-agentic-data-cloud-what-it-is-and-what-analysts-are-skeptical-about-yzjum6sjp"},"datePublished":"2026-04-29T16:16:35.241Z","dateModified":"2026-04-30T20:28:58.379Z","description":"Google Cloud Next 2026 centered on agentic AI as shipping product across security, data, and developer tooling. 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