Gemini Enterprise Agent Platform: product and architecture
This Vertex AI review covers Gemini Enterprise Agent Platform, the name Google now uses, Google Cloud's unified machine learning platform that has become a central hub for ML teams operating at scale. The platform consolidates Google's previously fragmented ML services into a single control plane, covering everything from data preparation and model training to deployment and monitoring. For organizations already invested in the Google Cloud ecosystem, it offers a tightly integrated experience that reduces the operational overhead of managing separate ML tools. The platform targets data scientists, ML engineers, and platform teams who need production-grade ML infrastructure without assembling it from scratch.
Overview
Gemini Enterprise Agent Platform, formerly Vertex AI, is Google Cloud’s comprehensive platform for developers to build, scale, govern, and optimize agents. Google describes it as a single destination for technical teams that want to transform enterprise applications and workflows into agentic systems.
The platform combines agent development with model and ML workflows. Its stated capabilities include building, scaling, governing, and optimizing enterprise-grade agents grounded in enterprise data; accessing generative models through Agent Studio and the Gemini API; discovering and deploying models through Model Garden; and training, tuning, and deploying ML models.
For generative AI work, Agent Studio lets teams design, test, and manage prompts for Gemini models using natural language, code, images, or video. Google also identifies common supported tasks such as classification, summarization, and extraction, and notes that Gemini on Agent Platform supports flexible prompt structures and formats.
For model development, Model Garden offers Google, third-party, and open models, while custom training supports a team’s preferred ML framework, its own training code, and selected hyperparameter tuning options. The platform’s notebooks include Colab Enterprise or Workbench and are integrated with BigQuery.
For production operations, Gemini Enterprise Agent Platform's public documentation identifies Model Evaluation, Pipelines, Model Registry, Feature Store, and monitoring as MLOps tools. Models can be registered in Model Registry and deployed through the prediction service for batch or online predictions. Google also describes monitoring for input skew and drift.
The current product name matters when evaluating documentation and pricing: Google’s official product page identifies the offering as Gemini Enterprise Agent Platform and explicitly states that it was formerly Vertex AI.
Key Features and Architecture
Gemini Enterprise Agent Platform, formerly Vertex AI, is Google Cloud’s comprehensive platform for developers to build, scale, govern, and optimize agents. Google presents it as a unified platform spanning agent building, model training, deployment, and MLOps.
Agent Platform and Agent Studio provide the foundation for building enterprise-ready agents and generative AI applications. Agent Studio supports designing, testing, and managing prompts for Gemini models with natural language, code, images, or video, and lets developers evaluate, tune, and deploy generative models for AI-powered applications.
Model Garden is the discovery and deployment layer for models and assets. Google says it includes more than 200 Google and third-party AI models and tools, including Gemini models, third-party model families, and open models. Teams can discover, test, customize, and deploy selected open-source models and assets.
Custom training gives teams control over the training process. Gemini Enterprise Agent Platform's public documentation identifies choices such as the ML framework, training code, and hyperparameter tuning options.
Notebooks and data integration support data science and ML work. Agent Platform notebooks include a choice of Colab Enterprise or Workbench and are natively integrated with BigQuery. Google also describes training and prediction tools that help teams train models and deploy them to production with a choice of open-source frameworks and optimized AI infrastructure.
MLOps services include Model Evaluation, Pipelines, Model Registry, Feature Store, and monitoring. The platform positions these as modular tools for identifying a suitable model for a use case, orchestrating workflows, managing models, serving and reusing ML features, and monitoring models for input skew and drift.
Prediction and deployment support batch and online predictions. A production workflow can register a model in Model Registry and use the prediction service; Google also provides custom prediction routines and prebuilt containers for prediction and explanation.
Ideal Use Cases
Gemini Enterprise Agent Platform, formerly Vertex AI, is Google Cloud’s platform for developers building, scaling, governing, and optimizing enterprise-ready agents. It is suited to technical teams that want a single destination for turning enterprise applications and workflows into agentic systems.
Teams building generative AI applications can use Agent Studio to design, test, and manage prompts for Gemini models using natural language, code, images, or video. The platform also provides access to Gemini models through the Gemini API.
Organizations evaluating and customizing models can use Model Garden to discover, test, customize, and deploy Google, third-party, and open-source models. The platform lists tuning options and a Model Evaluation service for objective, data-driven model assessment.
ML and data science teams can use the platform’s tools for training, tuning, and deploying ML models. Agent Platform notebooks, including Colab Enterprise or Workbench, are integrated with BigQuery for data and AI workloads.
Teams operationalizing ML workflows can use the platform’s MLOps tools, including Pipelines, Model Registry, Feature Store, and monitoring. Google describes these tools as helping teams automate, standardize, and manage ML projects across the development lifecycle.
Production teams deploying models can register models in Model Registry and use the prediction service for batch and online predictions. The platform also provides custom training options for teams that need control over their ML framework, training code, and hyperparameter tuning choices.
Strengths & Trade-offs
Pros:
- Deep integration with the Google Cloud ecosystem (BigQuery, GCS, IAM, GKE) reduces operational overhead
- AutoML delivers strong baselines for tabular, image, and text tasks without ML expertise
- Managed training infrastructure with GPU and TPU support eliminates cluster management
- Feature Store prevents training-serving skew with point-in-time correctness
- Pipeline orchestration based on Kubeflow is production-tested and flexible
- Model monitoring catches drift and skew issues before they impact business metrics
Cons:
- Vendor lock-in to Google Cloud makes migration difficult if cloud strategy changes
- Pricing complexity makes cost forecasting challenging, especially for large training jobs
- AutoML image training costs ($3.465/hour) are substantially higher than custom training
- Documentation gaps exist for advanced configurations, and some features lag behind the API
