At Cloud Next, Google positions 'agentic' AI as core infrastructure

Cloud infrastructure is usually the boring layer — chips, storage, networking, security, orchestration. At Google Cloud Next ’26, Google tried to make “agentic” AI part of that same category. That is the real news here. Not just new models, and not just another chatbot demo, but a pitch that agents now need a full operating stack underneath them — and Google wants to sell that stack end to end. (cloud.google.com) ### What changed at Next? Google’s keynote and follow-up posts kept returning to the same idea: the future enterprise is “agentic,” and the way to support it is a layered platform. That stack ran from Gemini models and Vertex AI tooling up through orchestration, governance, security, data systems, and custom silicon. The centerpiece was Gemini Enterprise A(cloud.google.com)and optimize agents. (cloud.google.com) ### Why call this infrastructure? Because agents break if they are treated like a thin app feature. A real enterprise agent needs model access, tool use, identity controls, observability, workflow integration, and a way to run for longer than a single prompt-response session. Google’s framing is basically: if AI is going to do actual work, then the hard pa(cloud.google.com) agent platform with AI Hypercomputer updates, new storage and networking, and eighth-generation TPUs. (cloud.google.com) ### What is in the platform? The new platform bundles the old pieces people already knew — model selection, tuning, and agent building from Vertex AI — with newer pieces that feel much more like enterprise middleware. Google highlighted agent integration, DevOps, orchestration, security, an Agent Designer, an inbox for agent activity, long-running agents, (cloud.google.com)ntrol plane for many agents. (cloud.google.com) ### Where does data fit? Right in the middle. Google also pushed “Agentic Data Cloud,” including a cross-cloud lakehouse and a knowledge catalog meant to feed agents trusted context at enterprise scale. That is a quiet but important move. Agents are only useful if they can reach the right internal data without turning into a compliance ni(cloud.google.com)ry. (cloud.google.com) ### Why the TPU talk? Because this pitch only works if Google can argue that it owns the economics too. The company paired its software story with eighth-generation TPUs and broader AI infrastructure claims across compute, storage, and networking. In plain English, Google is saying: don’t just rent a model from us — build your agent estate on our hardware, (cloud.google.com)usiness. (cloud.google.com) ### Is anyone buying the framing? Early signs say yes, or at least vendors think customers will. On May 5, Dyna Software launched Platform Copilot for ServiceNow and described it as an agentic AI tool that can autonomously configure, design, and build on the platform. That does not prove Google caused the launch. But it does show the market is converging on the same idea — agents are becoming a platform category, not just a feature layer. (siliconangle.com) ### So what is Google really trying to do? Google is trying to move the AI conversation away from model horse-race coverage and toward stack ownership. That is smart. Models commoditize fast. Infrastructure, governance, and workflow integration do not. If enterprises accept that “agentic AI” requires a full operating environment, then Google gets to compete on the whole cloud, not just on Gemini. (blog.google) ### Bottom line? The bet at Cloud Next was simple: agents are not an app feature anymore. Google wants them treated like core infrastructure — and if that framing sticks, it changes where enterprise AI money goes next. (cloud.google.com)