Build and customize agents
Agent Studio provides a visual workflow for designing, testing, and managing prompts, while developers can use the Agent Development Kit to build and customize more sophisticated agents.
Gemini platform
Gemini platform is Google Cloud’s platform to build, customize, deploy, govern, and optimize enterprise AI agents and generative AI applications with MLOps services.
What is Gemini platform?
Gemini platform is Google Cloud’s development platform for building, scaling, governing, and optimizing enterprise-grade AI agents and generative AI applications. It combines Gemini and other models, agent development tools, enterprise-data grounding, custom training, deployment services, and MLOps capabilities in one environment.
Technical teams can build agents with Agent Studio, the Agent Development Kit, Model Garden, or custom training workflows. Agent Search can support conversational search across websites and enterprise data and provide retrieval-augmented grounding for applications that need answers based on organizational information.
Platform capabilities
Choose and adapt models
Model Garden offers access to Google, third-party, and open models and supports discovering, testing, customizing, and deploying selected models and assets.
Ground responses in enterprise data
Agent Search supports Google-quality search experiences for websites and structured or unstructured enterprise data, and can ground generative AI applications through retrieval-augmented generation.
Develop and deploy ML models
Notebooks, including Colab Enterprise or Workbench, integrate with BigQuery. Training and Prediction support model development and production deployment using open-source frameworks and optimized AI infrastructure.
Manage the ML lifecycle
Model Evaluation, Pipelines, Model Registry, Feature Store, and model monitoring help teams assess, orchestrate, manage, reuse, and monitor predictive and generative AI models.
Govern agent deployment
Gemini platform supports registering, managing, and governing custom-built agents, while Gemini Enterprise provides centralized visibility and control for Google-made, third-party, and internally built agents.
Common uses
Build production AI agents
Developers can prototype and test prompts in Agent Studio, access Gemini models through the API, and move toward production agents or generative AI applications using frameworks such as the Agent Development Kit.
Create enterprise search experiences
Organizations can use Agent Search to add conversational search to websites or internal data, including structured and unstructured sources, instead of relying only on keyword matching.
Ground applications in business data
Teams can ground generative AI applications in company information with Agent Search’s retrieval-augmented generation capabilities and, when needed, use APIs for more customized grounding workflows.
Operate predictive and generative ML
Data scientists and ML engineers can evaluate models, tune or train them, orchestrate pipelines, register and serve models, reuse features, and monitor for input skew and drift.
Manage an organizational agent portfolio
Organizations can register, manage, and govern custom-built agents alongside Google-made and third-party agents, with centralized visibility described for Gemini Enterprise editions.
Pros and Cons
Pros
- Covers agent design, model selection, tuning, deployment, governance, and optimization in a connected Google Cloud environment.
- Offers developer choice across Google, third-party, and open models, plus custom training with preferred frameworks and code.
- Includes enterprise-data grounding options through Agent Search and APIs for search, retrieval, and grounded generation.
- Provides MLOps components for evaluation, pipelines, model management, feature reuse, and monitoring.
- Supports different levels of control, from prompt and agent design in Agent Studio to custom model training.
Cons
- Product-specific pricing, usage dimensions, and included capabilities are not specified in the supplied page content.
- Some workflows require technical development through APIs, frameworks, notebooks, or custom training rather than a no-code interface.
FAQ
Who is Gemini platform for?
Gemini platform is intended for technical teams building, customizing, fine-tuning, deploying, and governing enterprise AI agents and applications. Developers can use Agent Studio, the Agent Development Kit, Model Garden, or custom training workflows.
How can developers start building with Gemini platform?
Agent Studio supports designing, testing, and managing prompts with natural language, code, images, or video. Developers can also access Gemini models through the Gemini API and use available code samples and notebooks.
Can the platform ground AI applications in enterprise data?
Agent Search can provide an out-of-the-box retrieval-augmented generation system for enterprise data. It supports search experiences for websites and internal data, while its APIs expose components such as document layout parsing, ranking, grounded generation, and grounding checks for more customized solutions.
What options are available for model development?
The platform supports several development paths, including Agent Studio, Model Garden, and custom training with a preferred machine-learning framework, custom training code, and hyperparameter-tuning options. The appropriate path depends on the required level of control.
How is Gemini platform priced?
Google Cloud describes pricing as pay-as-you-go, with costs varying by product and usage. The site offers a pricing calculator, sales quotes, and free-start options; new Google Cloud customers may receive credits, subject to the terms of the applicable offer.
Quick Facts
- Category
- AI development platform
- Provider
- Google Cloud
- Primary users
- Developers, data scientists, and ML engineers
- Core workflows
- Build, customize, deploy, govern, and optimize AI agents and models
- Enterprise data
- Agent Search supports search and retrieval-augmented grounding
- Pricing model
- Google Cloud pay-as-you-go; product-specific rates vary by usage
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