Beyond the model: The real AI bottleneck is storage

Every generation of enterprise computing introduces new abstractions for scaling systems.

We began in the scale-up era, deploying ever-larger servers. Then came the scale-out era, where containers and Kubernetes transformed infrastructure into orchestrated fleets of commodity systems. Today, we are transitioning to agentic-scale.

This is not an incremental tweak to the infrastructure playbook; it is a fundamental architectural evolution that rivals that same major shift we saw from scale-up to scale-out. In the age of agents, many of the assumptions that defined the scale-out era no longer apply.

Applications are no longer just for human operators

Traditional enterprise applications were built around human behavior. People logged into systems during business hours, establishing predictable infrastructure usage patterns. AI agents operate around the clock and traverse resources in nondeterministic ways.

Ask an agent to onboard a supplier, investigate a security alert, or resolve an insurance claim, and that request cascades into autonomous decisions. The agent invokes APIs, consults internal systems, delegates tasks to specialized sub-agents, and iterates before returning a recommendation or taking action.
Where scale-up capacity planning revolved around forecasting user traffic, agent-driven volatility will become the new “peak usage” scenario for infrastructure.

The urgency of fluid compute and AI sandboxing

Autonomous agents can spawn clusters of computing activity at any moment, but keeping idle pools of resources on standby is financially reckless.

Just as containers became the unit of application deployment in the cloud era, isolated execution environments are emerging as the operational unit for agents. Serving as secure sandboxes, these environments can be created and retired on demand, allowing enterprises to absorb unpredictable bursts of agent activity and execute untrusted code safely without maintaining costly idle capacity.

Moving intelligence, not data

Agents only create value if they can operate across your enterprise estate, applications, and data. Historically, enterprises centralized data so applications could find it. Agentic systems reverse that assumption by bringing intelligence closer to where data already resides.

Unifying the data layer for agentic AI success requires a semantic mapping of the enterprise’s data assets, also known as a “knowledge catalog.” Agents must be able to instantly discover and query unstructured data across hybrid environments, which is why data infrastructure is evolving to support “smart storage” and systems that automatically tag objects with metadata for this new era of agentic discovery and execution.

The scale-up era taught enterprises how to optimize machines. The scale-out era taught them how to coordinate fleets of machines. Agentic-scale optimizes orchestration itself. Organizations must decide where intelligence executes, when environments are created and retired, how agents gain access to data and tools, and how all of it comes together across clouds, on-premises environments, and the edge without forcing every system into a single repository.

The new trust model for agentic systems

Today’s rapid build-out and agent sprawl has major security implications for CIOs.

Because developers can easily spin up agents using open-source frameworks and unvetted repositories, organizations risk introducing new attack surfaces into production. Infrastructure must treat agents as digital personas, enforcing centralized identity, policy-based access controls, and clear governance over what each agent can access and what it can do.

That means trust now extends beyond security to sovereignty. For critical infrastructure, defense, and other regulated industries, that means deploying AI in disconnected and fully air-gapped environments while protecting data at rest, in transit, and in memory. One straightforward architectural goal for CIOs should be to bring AI capabilities to wherever sensitive data resides, rather than forcing sensitive data to move to wherever the AI happens to be.

Preparing for the era of more agents doing more things

Organizations should assume they will have more agents doing more work across more systems.

As autonomous agents operate around the clock and execute highly unpredictable, non-linear workflows, traditional capacity planning must give way to continuous adaptation. Navigating this shift requires an integrated infrastructure stack. CIOs must pair elastic compute tailored for agentic workloads with a unified data layer that unlocks enterprise knowledge. This foundation must then be secured by modern, policy-driven networking and strict data sovereignty controls wherever sensitive workloads run.

The cloud era was won by coordinating fleets of machines. The next era will be won by orchestrating intelligence.

To read more about this topic, explore Google Cloud infrastructure solutions on our blog.