Gemini Enterprise Agent Platform (formerly Vertex AI)

Gemini Enterprise Agent Platform is Google Cloud's current platform for building and operating enterprise AI applications and agents. It continues capabilities associated with Vertex AI while expanding the agent development and governance story. Technical teams can work with models, training and tuning workflows, deployment, and enterprise controls in one cloud environment instead of treating a model endpoint as the entire system.

Features and what they are useful for

• Build applications or agents using supported Google and third-party models.
• Train, tune, test, and deploy machine learning models through the platform's documented lifecycle tools.
• Ground agent workflows in organizational data and connect supported development resources.
• Govern and observe deployed AI through the platform's enterprise management and optimization capabilities.

Who it suits

The platform suits cloud engineering teams, machine learning practitioners, and organizations building AI into existing systems. It is relevant when a project needs managed infrastructure, reproducible model operations, and access controls. A small user who only wants a consumer chatbot should compare that separate product, because a cloud platform brings billing and operational responsibilities as well as flexibility.

A practical workflow

For a customer-support classification project, start with a reviewed dataset and define the labels and evaluation criteria. Select a supported modeling or customization path, evaluate results on held-out examples, and inspect failure cases before deployment. Configure the endpoint or application with appropriate access and monitoring. If an agent is added later, test its data grounding and tool behavior independently so model performance is not confused with safe workflow operation.

Limitations and things to check

Available models, deployment regions, quotas, and feature names can change as the platform evolves. Its enterprise scope also does not remove the need for dataset quality, permission design, evaluation, and ongoing monitoring. An agent can produce a plausible answer while using the wrong source or tool. Review current service documentation for the exact component you intend to use, especially when migrating from an older Vertex AI workflow.

Pricing and access

Access runs through Google Cloud accounts and usage-based services. Different parts of the system can incur charges for inference, training, storage, networking, or deployed capacity. Check the current pricing and region availability for the selected components rather than estimating the whole platform from one advertised model price or introductory credit offer.

Frequently asked questions

Is this the consumer Gemini chatbot?

No. This is Google Cloud's enterprise development and operations platform.

What should a migration from Vertex AI verify?

Check the current component names, supported APIs, regions, permissions, and billing behavior used by the existing workload.

Official sources

Gemini Enterprise Agent Platform (formerly Vertex AI) official website · Official product guide