The Path to the Agentic Enterprise: What Google Cloud Next ’26 Means for Businesses

From GenAI experiments to the Agentic Enterprise

In many companies, generative AI has so far been a hodgepodge of pilot projects: a chatbot here, a text or code assistant there. Exciting – but rarely business-critical. Google Cloud Next ’26 draws a clear line here: in future, it will no longer be just about generating content, but about AI agents acting proactively, cooperating with one another and being deeply integrated into business processes.

Google summarises this under the term ‘Agentic Enterprise’: AI agents access systems, data and APIs, orchestrate complex workflows, and do so within a controlled environment where security and compliance are built in from the outset.

Where do you currently stand in this development?

To assess what the following announcements mean for your own projects, it is worth asking the following questions.

Where do I already have use cases today that would benefit significantly from a transition to agent workflows? These could include, for example, chatbots in customer service, document processes in the back office, support for IT operations or security teams.

Is my data and cloud architecture ready for agents? Here, it is important to consider access paths, permissions, API design, multi-cloud strategies, observability and cost models.

How do I ensure governance, security and compliance? Among other things, this involves scrutinising data access, possible actions and the traceability of agents.

Gemini Enterprise Agent Platform: From prompt to networked agent

At the heart of it all is the Gemini Enterprise Agent Platform. The previous name, Vertex AI Platform, is being replaced so that the name itself clearly reflects the direction of travel: away from a pure model and ML platform, towards an environment centred on networked AI agents. The expectation is that, in future, companies will use agent networks instead of addressing individual models:

• An Agent Development Kit (ADK) provides a graph-based framework for defining various sub-agents and orchestrating their collaboration.
• With Agent Studio, business departments can create low-code/no-code workflows and configure agents without delving deep into the ML details.
• The GKE Agent Sandbox provides isolated runtime environments in which production agents scale securely and are strictly separated from one another.

What does this mean for businesses and for you?

These changes are more than just a rebranding. The applications you create are moving away from individual LLM prompts towards standardised, reusable agent building blocks.

Taking a customer support agent as an example, this development could look as follows: thanks to access to internal APIs, data sources and business logic, it is not just possible to generate responses. This integration also allows bookings to be adjusted or complaints to be created directly within backend systems. To achieve this, multiple agents must collaborate within controlled workflows. A key advantage is that such agent networks can be integrated into existing applications (e.g. CRM, ERP, portals).

For your architecture, this means: in future, you will no longer need to think solely in terms of individual “AI features”, but rather agent services with clear responsibilities and interfaces.

Agentic Data Cloud: Data that AI understands from the outset

Agents are only as good as the data they can access. This is where Google comes in with the Agentic Data Cloud, expanding the Smart Storage introduced last year with crucial new agent functions:

• When storing data, Cloud Storage now automatically generates semantic metadata (e.g. image or document tags).
• Complex, manually built data pipelines for pre-enrichment are reduced or eliminated entirely.
• Data objects become ‘self-describing objects’ that can be interpreted immediately by agents.

Let’s imagine we want to build an HR agent that analyses incoming CVs according to specific criteria. Thanks to the semantic metadata, this is now possible directly within Cloud Storage; as a result, the HR agent knows where to find the CVs without first having to implement a dedicated extraction pipeline to make the data AI-understandable.

This results in less preparatory work for data engineers. And less work means faster iteration of use cases.

AI infrastructure: Operating agents that run continuously in a cost-effective manner

Agent-based workloads differ significantly from traditional web or batch applications: agents run in the background over the long term, perform complex inference and planning tasks, and interact continuously with systems and users.

As a result, computationally intensive applications—such as thousands of logistics agents running in parallel to monitor supply chains, optimise routes or predict events—can easily become very expensive and slow.

Google addresses this with a series of infrastructure updates centred on GKE, Compute and specialised hardware (GPUs/TPUs):

• Accelerated storage architectures that make efficient use of GPUs and TPUs.
• Native quantisation optimisations directly at the hardware level to enable higher throughput at lower cost.
• Improved scaling mechanisms for high-scale inference and training workloads.

Why is this relevant?

The cost per agent transaction decreases when utilisation and storage bandwidth are optimised. Organisations can run more agents productively in parallel without their cloud bill skyrocketing. Particularly for industries with high peak loads (retail, media, logistics, financial services), productive, 24/7 agent operation becomes a realistic prospect.

Agentic SecOps: Security at the speed of AI

As business units increasingly create agents independently (‘Vibe Coding’ via low-code/no-code), the risk of shadow AI, data leakage and misconfigurations grows, as governance, security and compliance are not adhered to. With Agentic SecOps and the deep integration of threat intelligence and partner solutions, Google addresses precisely these challenges:

• Threat Hunting Agents proactively scan environments for new attack patterns – including those generated by AI.
• Detection Engineering Agents help security teams develop and adapt new rules.
• Agentless scans automatically check code, configurations and architectures and suggest corrections directly within the development tools.

Implications for your security architecture:

Security itself becomes agent-based. The traditional ‘develop first, test later’ approach is shifting towards Security-by-Design, automated by AI agents. Furthermore, governance, role models and policies must be designed to apply to AI agents as well – and not just to human users.

What does this mean specifically for your roadmap?

Think back to the questions from the beginning:

Where do I already have use cases today that would benefit greatly from further development into agent workflows? Is my data and cloud architecture ready for agents? How do I ensure governance, security and compliance?

A sensible roadmap towards the Agentic Enterprise usually starts with:

• the identification of a few, but strategically relevant pilot scenarios,
• an architectural blueprint for agents based on the new Google Cloud building blocks, and
• the definition of guard rails (guidelines, roles, control mechanisms) for AI agents.

Conclusion: Set the course for the Agentic Enterprise now

Google Cloud Next ’26 makes it clear: the next stage of AI in enterprises is not the better prompt, but the connected agent ecosystem that accesses data, processes and infrastructure and generates added value in day-to-day business.

Anyone investing in Google Cloud today or evaluating it as part of a multi-cloud strategy should not view the new building blocks – Gemini Enterprise Agent Platform, Agentic Data Cloud, optimised AI infrastructure and Agentic SecOps – in isolation, but rather as part of a long-term architectural vision.

Thus, the announcements from Google Cloud Next ’26 do not result in yet another wave of prototypes – but rather a concrete roadmap towards the Agentic Enterprise, which brings together business requirements, technology and operations.