At Cloud Next 2026 in Las Vegas, Google made one of the biggest moves in its cloud history. Vertex AI, the platform that millions of developers have been using since 2021, is gone. Not deprecated. Not just renamed. Replaced, structurally, by something built from the ground up for a different era.
It is called the Gemini Enterprise Agent Platform.
And if you are building anything with AI today, you need to understand what just changed.
The Old World vs The New World
Vertex AI was built for a world where you pick a model, train it, deploy it, and call it a day. One model, one job, one endpoint.
That world is over.
Businesses today are not trying to build one AI assistant. They are trying to run hundreds, sometimes thousands, of AI agents at the same time. Agents that search the web, write emails, call APIs, talk to each other, handle customer requests, and make decisions, all simultaneously, all day long.
Vertex AI was not designed for that. So Google replaced it.
What Is the Gemini Enterprise Agent Platform
Google CEO Thomas Kurian described the strategy in simple terms at the keynote. Competitors, he said, are “handing you the pieces, not the platform.” Google wants to own the entire stack, from its custom TPU chips all the way to the three billion inboxes inside Google Workspace.
The Gemini Enterprise Agent Platform is the result. It brings together model selection, development tools, deployment infrastructure, security, and governance into a single place. Everything you need to build, run, and manage agents, under one roof.
It also absorbed Agentspace, Google’s enterprise AI search and discovery product, into a unified Gemini Enterprise offering. No more juggling separate products.
The Four Things the Platform Does
The platform is organized around four core jobs.
The first is building. For developers who want to write code, there is the Agent Development Kit, or ADK. It just hit stable version 1.0 across four languages: Python, Go, Java, and TypeScript. This is significant. Enterprise teams do not always work in Python. A Java shop can now build production-ready agents without maintaining a separate Python service just to connect to Google’s infrastructure. ADK also now includes a graph-based framework for orchestrating multiple agents working together.
For teams that do not want to write code at all, there is Agent Studio. It is a low-code interface where you describe what you want in plain English and the platform helps you build it. Non-technical teams inside a company can now create agents without filing a ticket with engineering.
The second is scaling. There is a feature called Agent Runtime that Google says delivers sub-second cold starts, meaning new agent instances spin up almost instantly when demand spikes. There is also a new Memory Bank. This gives agents persistent, long-term memory across sessions. Previously, every time you started a new conversation with an agent, it had no idea what happened before. Memory Bank fixes that. An agent can now remember context from a week ago and act on it today.
The third is connecting. The Model Garden now has over 200 models, including Google’s own Gemini models as well as Anthropic Claude and many others. On top of that, partner agents from Box, Workday, Salesforce, ServiceNow, Dun and Bradstreet, and S&P Global are already integrated. You do not have to build everything from scratch. If you need an agent that handles HR self-service or financial data, there is likely already one ready to plug in.
The fourth is governing. This is the part that enterprise IT teams care most about. The platform has a single control plane where every agent deployed inside a company is visible, auditable, and controllable. Model Armor blocks prompt injection attacks. Zero-trust security handles decentralized setups. IAM manages access and keeps audit logs. Every employee can use and share agents, and IT can see all of it.
The Piece Most People Are Sleeping On: A2A Protocol
The flashiest announcements get the attention. But the most strategically important thing Google announced at Cloud Next 2026 might be the Agent2Agent protocol, or A2A, now at version 1.2.
Here is the problem it solves. You might build an agent on Google Cloud. Your partner company builds an agent on Microsoft Azure. Your vendor uses a Salesforce agent. Today, those three agents cannot easily talk to each other. They live in different systems, speak different formats, and have no way to securely pass tasks back and forth.
A2A is the answer. It is an open standard that lets agents built on completely different platforms communicate, delegate tasks, and share state. It does not matter which model or cloud they are built on.
The numbers back up how serious this is. Over 150 organizations are already running A2A in production, not in pilot programs. Real work, real tasks, real companies. Microsoft, AWS, Salesforce, SAP, and ServiceNow are all live. The protocol is now governed by the Linux Foundation’s Agentic AI Foundation, which means no single company controls it.
And for developers already using LangGraph or CrewAI, both frameworks already have native A2A support built in. You do not need to rewrite anything.
Project Mariner: An Agent That Browses the Web For You
[Update — June 2026: Google has officially discontinued the standalone Project Mariner experiment to integrate its web-browsing capabilities directly into Gemini Agent and AI Mode.]One of the more visible pieces is Project Mariner, built by Google DeepMind and powered by Gemini 2.0.
Mariner is a web-browsing agent. You give it a goal, and it opens browsers, navigates websites, fills out forms, retrieves information, and completes purchases, all on its own. It scores 83.5% on the WebVoyager benchmark, which is the standard test for web agents, and can handle ten tasks running at the same time on cloud-based virtual machines.
Right now it is available to Google AI Ultra subscribers in the United States. The roadmap includes a visual builder called Mariner Studio in the second quarter of 2026, cross-device sync in the third quarter, and an agent marketplace in the fourth quarter.
What This Means If You Are Already on Vertex AI
The change is structural, not just cosmetic. All the Vertex AI features you know, Model Garden, Custom Training, AutoML, Model Registry, Endpoints, and Pipelines, are still there. They have just been reorganised under a “Models” sub-menu inside the Agent Platform.
The underlying API endpoint, aiplatform.googleapis.com, is not going anywhere. Google has committed to keeping it alive for compatibility. If you are reading documentation in 2027 and the API still says aiplatform, do not be surprised.
But new capabilities will not ship as Vertex AI updates. They will ship exclusively through the Gemini Enterprise Agent Platform. The roadmap has moved. If you want access to what Google builds next, that is where it will live.
One important date: if you are using deprecated SDK modules from the old Vertex AI Python SDK, the migration deadline is June 24, 2026. That is soon.
The Bigger Picture
Every major cloud provider is making the same move at the same time. AWS launched AgentCore. Anthropic shipped Claude for Small Business. Google launched this. The consolidation pattern is unmistakable. Every cloud is going agent-first. Every cloud is differentiating on governance, identity, and security. Every cloud is building partner marketplaces to seed adoption.
Google’s bet is that owning the full stack, from the hardware layer to the productivity layer, gives it an advantage that point solutions cannot match. If your company already runs on Google Workspace and Google Cloud, the integration story is genuinely compelling. The economics also make sense when your data already lives in BigQuery and your team is already on Google tools.
If you are pulling data from outside Google’s ecosystem and paying only for the agent layer, the math gets less favorable.
The Bottom Line
Vertex AI served its purpose. It was a solid platform for the era of single models and single deployments. But that era ended.
The Gemini Enterprise Agent Platform is built for the era of agent networks, agent communication, agent governance, and agent scale. Whether you are a developer, a cloud architect, or a business leader trying to figure out where AI is actually going, this is the direction.
Google has drawn a line. The agentic era is not coming. It is here. And Google just bet its entire cloud platform on it.
This article is based on announcements from Google Cloud Next 2026 in Las Vegas on April 22, 2026.
Vertex AI Is Gone. Here Is What Google Built Instead. was originally published in Google Developer Experts on Medium, where people are continuing the conversation by highlighting and responding to this story.
