Alongside the TPU 8t and 8i chips, Google used Cloud Next 2026 to rename and expand its enterprise AI platform. Vertex AI is now the Gemini Enterprise Agent Platform, and it adds Agent Designer — a no-code builder — an Inbox for tracking agent activity, long-running agent support, Skills, and Projects. Workspace Studio extends the same builder experience inside Gmail, Docs, and Sheets. The Model Garden now hosts more than 200 models including Anthropic's Claude, and Project Mariner, Google's web-browsing agent, is generally available. The Agent2Agent protocol (A2A) — a standard for agents to discover and call other agents — is reported as in production at 150 organizations.
The Agentic Data Cloud is the other half of the pitch. It includes a cross-cloud Lakehouse and Knowledge Catalog that let agents read and reason over data sitting in Google Cloud, AWS, or Azure without being moved first. That matters because agent quality is usually bottlenecked by data access, not model capability: an agent that cannot see the right warehouse, CRM, or document store cannot take useful action, and re-ingesting data into a new cloud is typically a year-long project that kills pilots before they ship.
Google's strategy here is explicitly full-stack: own the chips, the foundation model, the agent runtime, the data layer, the identity layer, and the office applications that agents act inside. That is a different bet than OpenAI's — which is building a consumer-and-developer product platform and partnering for the rest — and different from Anthropic's, which is focused narrowly on the model and a marketplace of third-party tools. For buyers, the trade-off is familiar: a tightly integrated single-vendor stack is faster to deploy and harder to leave.
For learners: agent platforms are where a lot of the near-term AI jobs are being created, and they reward a particular kind of skill — process decomposition. The interesting question is rarely whether a model can do a task; it is how to break a real business workflow into steps that an agent can do reliably, with clear handoffs to humans when it cannot. That skill is closer to product management and operations research than to ML research, and the supply of people who can do it well is much smaller than the demand.