Vertex AI to Gemini Enterprise Agent Platform: Migration Guide
Updated: 6 days ago
What changed?
Updated October 2026. Google has introduced Gemini Enterprise Agent Platform and renamed several products that used to sit under Vertex AI. This guide explains the current product names and separates those label changes from the code changes that some developers need to make. The mapping is useful when updating documentation, dependencies, API calls, and older tutorials.
Current product names
Google’s current name-change table maps several familiar services to new names. These are useful examples when searching the current documentation:
Vertex AI Platform → Gemini Enterprise Agent Platform
Vertex AI Studio → Agent Studio on Gemini Enterprise Agent Platform
Vertex AI Model Garden → Gemini Enterprise Agent Platform Model Garden
Vertex AI API → Gemini Enterprise Agent Platform API
Vertex AI Agent Engine → Agent Runtime on Gemini Enterprise Agent Platform
Vertex AI Search → Agent Search on Gemini Enterprise Agent Platform
Does the rename mean you need to change your code?
Not automatically. Product labels, console navigation, API hostnames, resource names, package imports, IAM permissions, and CLI commands are separate parts of an integration. Google’s current REST reference still uses aiplatform.googleapis.com in discovery URLs and endpoint examples, so don’t infer an API or endpoint change from a renamed product alone.
Before editing code, identify the exact service and operation your application uses. Check its current REST or client-library reference for the endpoint, API version, region, resource path, authentication method, and required permissions. A product rename can change where you find the documentation while a specific API contract remains separately documented.
Python SDK changes need their own review
Google’s Python migration guide lists vertexai.generative_models, vertexai.language_models, vertexai.vision_models, vertexai.caching, and vertexai.tuning as generative AI modules moved to the Google Gen AI SDK (google-genai). The guide gives June 24, 2026 as the removal date for those modules. That date has passed, so code that still imports them should be treated as needing migration and testing.
The package is not a single all-or-nothing replacement: Google says the classic machine-learning surface for datasets, training, models, prediction, tracking, and pipelines remains supported in google-cloud-aiplatform. The current migration guide also treats version 2.0.1 as the version 2 boundary and describes a temporary below-2.0.0 pin only while completing the migration. Follow the live guide for package-specific steps.
A safe migration checklist
Work through the integration in small, testable steps. Keep a record of what changed so you can review it before releasing to production.
Inventory old product names, imports, endpoints, resource paths, CLI commands, and links in code and internal docs.
Use Google’s name map to find current product documentation for each service; check release notes for behavior or lifecycle changes.
Change SDK imports or API calls only where the current migration guide or API reference calls for a change.
Test authentication, region, permissions, service availability, logging, and costs in a non-production environment before rollout.
What should stay separate from the rename
Do not mass-replace every occurrence of “Vertex AI” in code or rename resource identifiers by guesswork. Treat terminology cleanup, SDK migration, and API changes as separate work items. Confirm any identifier, endpoint, permission, or request change in the documentation for the exact feature you use.
How to update an older Vertex AI tutorial
Find the current page for the specific feature rather than searching only for the old platform name. Run the current quickstart, compare the package and API versions it uses, then check its region, IAM roles, and lifecycle notes. Update a tutorial’s date and links only after its steps have been tested against the current documentation.
Official references
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