At Google Cloud Next '26, Google introduced Gemini Enterprise Agent Platform, a new platform for building, scaling, governing, and optimizing enterprise AI agents. Google's official launch post was published on April 23, 2026.
Google is turning its enterprise AI products into a single agent stack.
For the last year, Google's enterprise AI story was spread across Gemini models, Vertex AI, Agent Builder, Workspace, and a growing set of agent features. Gemini Enterprise Agent Platform pulls those pieces into one product story: build agents, connect them to company systems, govern them, operate them in production, and expose them to employees.
What is Gemini Enterprise Agent Platform?
Google describes Gemini Enterprise Agent Platform as the evolution of Vertex AI.
It is not a new UI on top of Gemini. Google says future Vertex AI services and roadmap updates will move through Agent Platform rather than Vertex AI as a standalone service. Agent Platform is becoming Google's main product surface for enterprise agent development.
The platform covers four jobs:
- Models: access to Gemini models, plus support for some third-party models
- Build: tools for creating agents and defining how they work
- Scale: runtime, memory, orchestration, and production execution
- Govern: identity, registry, gateway, security, and policy controls
- Optimize: simulation, evaluation, observability, and automated improvement
Google is not only selling model access. It is packaging agent development, deployment, governance, and production operations together.
What is actually in the stack?
The official announcement is dense, but the platform breaks down cleanly if you look at the lifecycle.
Model access
Agent Platform includes first-class access to more than 200 models through Model Garden. Google highlights first-party models such as Gemini 3.1 Pro, Gemini 3.1 Flash Image, Lyria 3, and open models like Gemma 4. It also supports third-party models including Anthropic's Claude Opus, Sonnet, and Haiku.
Interestingly though, it does not support all of OpenAI models, just their open source ones - so no access to GPT 4.X or 5.X models.
The platform is designed as a governed model-and-agent layer, not a single-model product.
Building agents
Google is offering two main paths:
- Agent Studio for low-code, visual agent building
- Agent Development Kit (ADK) for code-first agent development
The upgraded ADK now supports graph-based agent design, where agents can be organized into networks of sub-agents. That matters for enterprise use cases because many internal workflows are not a single prompt-response loop. They need routing, tool calls, deterministic steps, and handoffs between specialized agents.
Google also introduced secure Workspaces, which give agents a sandboxed place to run bash commands and manage files without touching core systems directly. For agent builders, that is one of the more practical pieces: agents often need to manipulate files, run commands, and inspect outputs, but enterprises need that work isolated.
Connecting agents to company systems
Google is also pushing integration into the platform itself.
The announcement calls out Native Ecosystem Integrations for connecting agents to internal tools and data without custom glue code. It also mentions Batch and Event-driven agents that can run background work using systems like BigQuery and Pub/Sub.
That is a meaningful distinction from a chatbot product. The goal is not only to answer employee questions. It is to let agents run asynchronous business work across existing systems.
Scaling agents in production
The reworked Agent Runtime is meant to handle production execution. Google says it supports sub-second cold starts, fast provisioning, long-running agents, and workflows that can run for days.
Google also introduced Agent Sandbox for safely executing model-generated code and computer-use tasks, including browser-based automation.
The other major piece is memory. Agent Memory Bank gives agents long-term memory, while Agent Sessions lets companies map interactions back to their own identifiers, such as internal user IDs, database records, or CRM records.
Together, these features show the direction Google is betting on: long-running workflows, persistent context, and actions across tools instead of one-off chat sessions.
Governing agents
The governance layer is the most enterprise-specific part of the launch.
Google announced:
- Agent Identity: gives every agent a verifiable identity and audit trail
- Agent Registry: a central library for approved agents, tools, and skills
- Agent Gateway: a control point for agent-to-tool and agent-to-agent connectivity
- Model Armor protections: controls for risks like prompt injection and data leakage
- Agent Threat Detection and Anomaly Detection: monitoring for suspicious behavior
- Agent Security dashboard: a Security Command Center-backed view into agent risk
Google is not only saying "build agents here." It is saying enterprises need a governed fleet of agents, with identity, policy, monitoring, and approved assets.
Testing and improving agents
Google also added tools for quality control:
- Agent Simulation to test agents against synthetic users and virtualized tools
- Agent Evaluation to score live agent behavior
- Agent Observability to trace reasoning and debug production failures
- Agent Optimizer to cluster real-world failures and suggest better system instructions
That is a practical acknowledgement of a real problem: agents do not become reliable just because they worked in a demo. They need testing, traces, evaluation, and a feedback loop.
Which teams is this for?
Gemini Enterprise Agent Platform is strongest for companies that already have:
- a serious Google Cloud footprint
- Google Workspace adoption
- platform or IT teams that want centralized control
- enterprise security and compliance requirements
- multiple internal AI use cases that need one umbrella stack
If your company is already standardized on Google Cloud and wants a central platform for many internal agents, Gemini Enterprise Agent Platform should be on your shortlist.
It is especially relevant for teams building agents that need to:
- run for longer than one session
- connect to production data and tools
- operate under enterprise security policies
- expose approved agents across departments
- support both low-code builders and code-first developers
Why Google is doing this now
The market is moving from "which model is smartest?" to "which system lets a company deploy agents safely?"
That shift changes the competitive set. Model quality still matters, but enterprise buyers also care about data access, governance, operations, reliability, and employee adoption. Gemini Enterprise Agent Platform is Google's answer to that broader buying motion.
Where teams should be cautious
There are two tradeoffs to watch.
- First, ecosystem lock-in. The more complete Google's agent platform becomes, the more it encourages teams to build around Google Cloud, Workspace, Vertex-style tooling, and Google's approach to enterprise AI. That can be a good tradeoff for large Google Cloud customers. It can be limiting for teams that want a more portable setup. Google is known to kill products despite strong user adoption.
- Second, operational complexity. A large enterprise stack can solve hard governance and integration problems, but it also adds process, configuration, and operational overhead. Not every team needs Agent Identity, Agent Gateway, Agent Registry, Runtime, Simulation, Observability, and a full security dashboard on day one.
If your real problem is simpler, such as "we need one shared AI agent for a team with reusable tools and approvals," a full enterprise agent platform may be unnecessarily heavy.
Where TeamCopilot fits differently
Gemini Enterprise Agent Platform is strongest when you want a cloud-first enterprise agent stack with deep controls and close integration with Google's ecosystem.
TeamCopilot is stronger when you want a self-hosted, lighter, open source, and team-centric agent layer that your company can control directly.
That matters when your priority is:
- one shared agent surface for the team
- reusable skills and tools
- approvals and permissions for skill and workflow use.
- self-hosting
- direct control over how internal agents are exposed to users
- to use OpenAI models
Final take
Gemini Enterprise Agent Platform is Google's clearest enterprise AI agent move so far.
The launch matters because Google is packaging agent building, runtime, memory, governance, security, evaluation, and employee access as one platform story.
For buyers, the question is not whether the platform is impressive. It is whether your team needs a full cloud-first enterprise agent stack, or a simpler to use, self-hosted agentic framework.