Cloud Computing

We are officially moving past the era of one-size-fits-all AI. Enterprises today require highly specialized, role-specific tools to drive real productivity — but they cannot afford to sacrifice security, oversight, or control to get there. What’s new: Today, we’re bringing partner-built agents from our Agent Marketplace directly into the Agent Gallery inside the Gemini Enterprise […]

This post is cowritten by Anil Akarsu and Dr. Renè Holschuh from BASF. BASF is a global chemical industry leader and active member of the Catena-X Automotive Network. It pioneers sustainable solutions that enable automotive organizations to track carbon emissions across complex supply chains. CircularTree transforms sustainability reporting through innovative digital solutions that systematically identify

As AI models improve, technical practitioners are increasingly turning to agentic AI tools to build with Google Cloud products, from Firebase and the Gemini API, to BigQuery and GKE.  But how can you ensure that the model is equipped with accurate, up-to-date information about these technologies?  One way to do this is to plug your

Significant contributors to this article include Megan O’Keefe, Senior Staff Developer Advocate, and Karl Weinmeister, Director of Developer Relations. Whether you are joining us in person in Las Vegas or tuning in virtually from around the world, Google Cloud Next ’26 offers a deep look into the practical evolution of AI. With 89% of sessions

The AI era requires a fundamental rethink of physical cloud architecture — networking, in particular. With foundational model parameters growing exponentially, traditional general-purpose networks are reaching their breaking points. To fuel the next decade of machine learning, Google designed Virgo Network, a new megascale AI data center fabric that embraces a “campus-as-a-computer” philosophy, and that

While generative AI sparked a revolution, the true paradigm shift is the rapid evolution from standalone AI models to multi-agent autonomous systems. In this new era, the network transcends basic connectivity to become the critical integration layer for your agentic enterprise. As AI agents and services surge, your core applications remain as vital as ever.

In the agentic era, the role of enterprise data is shifting from a passive resource to a dynamic System of Action. That’s why we announced the Agentic Data Cloud — a unified architecture that integrates models, analytics, and operational databases into a single, AI-native system. This allows organizations to unlock the full potential of their

In the early days of generative AI, building safe and reliable business tools took massive engineering effort and a high tolerance for trial and error. We helped solve that with Vertex AI, our trusted AI development platform. But today, we’re managing a different level of complexity, with agents interacting across multiple systems — and often

This week at Google Cloud Next ‘26, we are sharing the evolution of Google Kubernetes Engine (GKE), delivering leading performance, efficiency, security, and scale for your most demanding and complex workloads, and the next generation of AI and agentic applications. Why it matters:  Kubernetes has rapidly become the operating system for the AI era, with

Traditional data catalogs were built as manual inventories for technical users, focusing on table structures rather than the deep context that AI agents need. When agents lack business semantics and data relationships, this triggers hallucinations, high latency, and stale insights.  To address this problem, we are evolving Dataplex into a dynamic, always-on Knowledge Catalog that