Google is making a dramatic bet that owning everything from AI chips to workplace software gives it an edge in the enterprise agent race that neither OpenAI nor Anthropic can match. At Cloud Next 2026, the company announced a sweeping reorganization of its AI platform, renaming Vertex AI to the Gemini Enterprise Agent Platform and absorbing its employee-facing assistant Agentspace into a unified product called Gemini Enterprise. The move signals Google's determination to compete for enterprise customers by offering a complete, integrated system rather than individual components.

The timing matters. OpenAI's Operator agent is scoring 87% on complex browser task benchmarks and has recruited major systems integrators like Cognizant and CGI to push its Codex coding agent into enterprise software shops, with enterprise revenue now accounting for 40% of OpenAI's total revenue. Anthropic has launched a marketplace for Claude-powered enterprise tools and its Model Context Protocol (MCP), a standard for connecting agents to tools and data, has reached 10,000 servers and 97 million monthly SDK downloads. Google is fighting from third position in cloud market share behind AWS and Microsoft Azure, but it exited the fourth quarter of 2025 with the fastest growth rate of the three at 50% year on year.

What Is Google's New Agent Platform Strategy?

Google's rebranding and consolidation effort centers on a full-stack approach. Thomas Kurian, Google Cloud's chief executive, titled the keynote "The Agentic Cloud" and drew a deliberate contrast with competitors, saying other vendors are "handing you the pieces, not the platform," leaving teams to integrate components themselves. Google's strategy is to own the model, the runtime, the silicon, and the distribution channel through Google Workspace, giving it an advantage neither competitor can replicate.

The platform includes several major components designed to work together seamlessly:

• Workspace Studio: A no-code platform that lets business users build and deploy AI agents across Gmail, Docs, Sheets, Drive, Meet, and Chat by describing automations in plain language, with connections to third-party applications including Asana, Jira, Mailchimp, and Salesforce.
• Gemini Enterprise Agent Platform: The developer-facing platform featuring Agent Designer for visual workflow building, Agent Engine Sessions and Memory Bank for persistent agent context, and a new Agent Garden with prebuilt solutions for customer service, data analysis, and creative tasks.
• Model Garden: Now hosts more than 200 models spanning Google's Gemini and Gemma families, third-party models including Anthropic Claude, and open models such as Llama.
• Project Mariner: Google DeepMind's web-browsing agent powered by Gemini 2.0, scoring 83.5% on the WebVoyager benchmark and handling ten concurrent tasks on cloud-based virtual machines.

How to Build AI Agents on Google's New Platform

Google has designed multiple pathways for different user types to create and deploy agents, from non-technical business users to experienced developers. Here are the primary ways organizations can get started:

• No-Code Automation: Business users can describe automations in plain language through Workspace Studio, such as typing "every Friday, ping me to update my tracker" and having Gemini create the automation automatically.
• Visual Workflow Design: Developers can use Agent Designer, a visual flow canvas for building agent workflows, which is currently in preview and allows teams to construct complex agent behaviors without writing code.
• Code-First Development: Engineers can use the open-source Agent Development Kit, which reached stable v1.0 releases across Python, Go, and Java, with TypeScript support also available, optimized for Gemini but model-agnostic and deployable to any container or Kubernetes environment.
• Prebuilt Solutions: Organizations can leverage the Agent Garden, which provides prebuilt agent solutions for customer service, data analysis, and creative tasks, reducing development time and complexity.
• Partner Integrations: Teams can access partner agents from Box, Workday, Salesforce, ServiceNow, Dun and Bradstreet, and S&P Global, which are integrated into the platform and provide prebuilt capabilities for document intelligence, HR self-service, IT operations, and financial data.

Why Agent-to-Agent Communication Is the Real Game Changer

While Workspace Studio and the developer platform grab headlines, the most strategically significant announcement may be the least visible to end users. Google's Agent2Agent (A2A) protocol, originally launched with more than 50 technology partners, has reached 150 organizations in production, not pilot, routing real tasks between agents built on different platforms. The protocol is now governed by the Linux Foundation's Agentic AI Foundation and has reached version 1.2, with signed agent cards using cryptographic signatures for domain verification.

A2A is designed to complement rather than compete with Anthropic's Model Context Protocol. MCP handles how an agent connects to tools and data sources. A2A handles how agents communicate with each other across organizational and platform boundaries. The practical implication is that a Salesforce agent built on Agentforce can hand off a task to a Google agent running on Vertex AI, which can query a ServiceNow agent for IT asset data, all through A2A without any of the three systems needing to understand each other's internal architecture.

Microsoft, AWS, Salesforce, SAP, and ServiceNow are running A2A in production environments. Native A2A support is now built into Google's Agent Development Kit, LangGraph, CrewAI, LlamaIndex Agents, Semantic Kernel, and AutoGen. This broad adoption suggests that A2A is becoming the standard for orchestrating multiple agents from multiple vendors working together on a single task.

What Does This Mean for Enterprise Competition?

The enterprise AI agent market is not a two-horse race. It is a five-way contest in which each competitor has a structural advantage the others lack. OpenAI has the strongest consumer brand and the most advanced reasoning models. Anthropic has the most trusted safety position. Microsoft has Copilot embedded in virtually every Fortune 500 company. AWS has Bedrock with its own agents framework maturing rapidly. Google is betting that vertical integration, owning the model, the runtime, the silicon, and the distribution channel through Workspace, gives it an advantage neither competitor can replicate.

Google's fastest growth rate of 50% year on year in the fourth quarter of 2025 suggests the strategy is gaining traction. By consolidating its AI offerings under the Gemini Enterprise brand and emphasizing seamless integration across its cloud services, Google is positioning itself as the vendor that can deliver a complete platform rather than requiring customers to assemble components from multiple vendors. Whether this full-stack approach proves decisive in the enterprise market will become clear over the next 12 to 18 months as organizations evaluate which AI agent platform best fits their needs.