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Gemini Enterprise Agent Platform

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Gemini Enterprise Agent Platform, formerly Vertex AI, is Google Cloud’s platform for developers and technical teams to build, deploy, govern, and optimize AI agents, generative AI applications, and machine learning models.

What is Gemini Enterprise Agent Platform?

Gemini Enterprise Agent Platform, formerly Vertex AI, is Google Cloud’s platform for building, deploying, governing, and optimizing enterprise AI agents. It is designed for developers, data scientists, and ML engineers working on agentic systems, generative AI applications, and machine learning workflows.

The platform brings together agent development, access to generative and open models, model customization, evaluation, deployment, and MLOps. Its purpose is to provide a shared environment for moving AI systems from experimentation to production while grounding agents and applications in enterprise data.

What can Gemini Enterprise Agent Platform do?

Agent development and deployment

Build and deploy production-ready AI agents and applications using Agent Studio, the Agent Development Kit, and a platform designed to scale with enterprise workloads.

Model Garden and model choice

Discover, test, customize, and deploy Google, third-party, and selected open models through Model Garden, including Gemini, Claude model family, and Gemma models cited on the product page.

Model tuning and evaluation

Customize models with available tuning options and assess generative AI models using Model Evaluation tools intended for objective, data-driven comparison.

Integrated notebooks and data workflows

Use Agent Platform notebooks, including Colab Enterprise or Workbench, with native BigQuery integration to work across data and AI tasks from a shared surface.

MLOps lifecycle management

Manage model projects with tools for training, prediction, pipelines, Model Registry, Feature Store, and monitoring for input skew and model drift.

Agent-powered workflow orchestration

Coordinate multiple agents for workflows such as website code generation, on-brand asset creation, and customer email production through the referenced Agent Platform development tools.

Use Cases

“Build enterprise AI agents”

Develop agents grounded in organizational data for applications and workflows that need more than a conversational interface, using Agent Studio or the Agent Development Kit.

“Create generative AI applications”

Use Gemini and other available models to build applications that extract, summarize, or classify information and can be tuned or evaluated before deployment.

“Move models into production”

Train or customize models, evaluate their performance, register them, and deploy them using the platform’s training, prediction, and MLOps capabilities.

“Automate coordinated development work”

Use multiple agents to execute parts of a workflow such as generating website code, producing branded media assets, and drafting customer communications.

“Support data science and ML teams”

Give data scientists and ML engineers a common environment for notebooks, BigQuery-connected data work, model experimentation, pipeline orchestration, and model monitoring.

Frequently Asked Questions

What was Gemini Enterprise Agent Platform previously called?

It was formerly called Vertex AI. The product page describes Gemini Enterprise Agent Platform as the next name for the Vertex AI capabilities now presented within the Gemini Enterprise Agent Platform.

Who is the platform designed for?

It is primarily aimed at developers, data scientists, and ML engineers building AI agents, generative AI applications, and machine learning systems.

Can teams use models other than Google’s Gemini models?

Yes. The product page describes Model Garden as offering Google models, third-party models such as Anthropic’s Claude model family, and selected open models such as Gemma.

Does the platform support both custom training and managed model workflows?

Yes. Custom training supports preferred open-source frameworks, custom training code, and hyperparameter tuning options. The platform also provides managed capabilities for training, prediction, evaluation, pipelines, model registration, feature management, and monitoring.

How is Agent Platform priced?

Google Cloud uses pay-as-you-go pricing, so costs depend on the services and usage involved. New customers can receive up to $300 in Google Cloud credits to try Agent Platform and other Google Cloud products. Google provides a pricing calculator and sales-based quotes for more detailed estimates.

Quick Facts

Product type
Enterprise AI agent, generative AI, and machine learning platform
Former name
Vertex AI
Primary users
Developers, data scientists, and ML engineers
Core workflows
Build agents, train and tune models, evaluate models, and deploy to production
Model access
Google, third-party, and selected open models through Model Garden
Pricing model
Google Cloud pay-as-you-go pricing; costs vary by product and usage

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