Google announces its single platform for enterprise AI agents: Gemini Enterprise

Vertex AI's successor, rebuilt for the agent era

Google Cloud positions the Gemini Enterprise Agent Platform as the evolution of Vertex AI: the model selection, model building and agent building capabilities of Vertex AI move into this platform, and according to Google, Vertex AI services and their roadmap will be delivered through it going forward. The backbone of the announcement rests on four verbs: build, scale, govern and optimize agents.

The second piece of the package is the Gemini Enterprise app: every agent developed on the platform surfaces directly in this app, where employees discover and use agents. The third piece is an open partner ecosystem; validated agents from providers such as Oracle, Salesforce and ServiceNow can connect under the same roof.

What is inside the platform?

On the build side there are two doors: Agent Studio, a visual, low-code interface, and the code-first Agent Development Kit (ADK) for developers. Ready-made agent templates live in Agent Garden, and an isolated Agent Sandbox handles commands and file operations. On the model side, Model Garden opens access to more than 200 models: alongside Google's Gemini 3.1 Pro and Gemma 4, third-party options such as Anthropic's Claude models are on the list.

On the scale side, the re-engineered Agent Runtime promises sub-second cold starts and agent workflows that can run uninterrupted for days. Memory Bank gives agents persistent, long-term memory, and agent-to-agent orchestration is part of the platform. Google says more than 6 trillion tokens are processed monthly on Gemini models through the ADK.

On the governance and security side, the announcement covers Agent Identity, which gives every agent a cryptographic ID, Agent Registry as the library of approved tools and agents, and Agent Gateway, which polices traffic from a single point. Model Armor protections against prompt injection and data leakage, anomaly and threat detection, and a security dashboard powered by Security Command Center are also in the package. On the optimization side sit testing against synthetic interactions (Agent Simulation), continuous scoring on live traffic (Agent Evaluation), visual tracing (Agent Observability) and Agent Optimizer, which clusters real-world failures into improvement suggestions.

The chatbot-to-agent transition, made official

The real message of this announcement is bigger than any single product: the major cloud providers are moving AI beyond a "box you ask questions" into agents that take on tasks, connect to systems and produce outcomes. The fact that governance layers such as identity, registry, auditing and anomaly detection sit at the center of the announcement is a signal in itself: agents are no longer treated as experiments but as a workforce running in production. Examples cited in the announcement, including Comcast, L'Oréal and PayPal, show this infrastructure already operating inside real business processes.

The scale may be distant, the direction points at you too

Most SMBs in Türkiye will not buy this platform directly; its audience is enterprise IT teams. But the pattern it establishes concerns everyone: agents that connect to business data, take over processes and produce measurable outcomes are becoming the standard of the coming period. The preparation you can do today is clear: keep customer and process data in order, define which jobs could be delegated to an agent, and set up the data side in line with KVKK discipline. A business with this groundwork ready adapts quickly, whichever tool the agent technology arrives with.

Primary sources for verification

All technical details in this article were compiled from Google Cloud's official announcements: "Introducing Gemini Enterprise Agent Platform" and "The new Gemini Enterprise: one platform for agent development".