The Agentic AI Continuum: Bringing AI Where Your Data Lives

Key takeaways: The future of AI will be a continuum, and the principle that guides it is simple: bring AI to where the data lives. Some AI work belongs in cloud environments. Some in enterprise data centers. And a growing share of serious agentic AI work belongs at the workgroup, on the desk, where teams actually build, test and refine intelligent workflows.

Compute has always moved to where it works best. Sometimes that means centralizing. Sometimes it means distributing. But the pattern is consistent: workloads find the place where performance, cost, control and experience come together.

AI is entering that same cycle and agentic AI is forcing a different question than the one the first wave asked.

Not just: What model should I use?

But: Where should AI happen?

For most enterprises, AI needs to live where the data is created and managed: across cloud environments, in the data center, at the edge and increasingly inside the workgroups where teams actually build, test and refine intelligent workflows. The principle is straightforward: rather than moving data to AI, organizations should bring AI to where the data already lives.

That is why this moment matters.

At Dell Technologies World, we are expanding the Dell AI Factory with NVIDIA to accelerate agentic AI across that continuum and bring AI capability to wherever the data lives: from infrastructure in the data center, at the edge and to the workstations where developers and engineers work with local data every day. And with Dell Deskside Agentic AI, we are bringing serious agentic AI capability directly into the workgroup — not to pull data away from its source, but to place intelligence closer to the datasets teams already rely on.

This is an important distinction. The conversation about AI is often centered on the data center and the edge, where data is created. Agentic AI reinforces why that focus was right, and makes it more urgent. Agents don’t just process data; they participate in processes. They reason, plan, call tools, write code, test outputs and iterate alongside the people doing the work. Each of those steps requires access to the underlying data, and each generates new inference demand.

That changes the economics very rapidly. Even though token costs have dropped, token spend is rising sharply because agentic workflows drive exponentially more compute than the chatbots that came before them. When every step multiplies token consumption, the distance between the agent and its data is not just a latency problem. It is a cost multiplier. That is why bringing AI to where the data lives has never mattered more.

And for organizations working with sensitive information, proprietary code, regulated data or valuable IP, there is a second reason to keep the work local: the data cannot leave the building. Deskside addresses both by bringing AI to where that data already lives, behind the firewall, under the workgroup’s control.

That is the idea behind Dell Deskside Agentic AI: bringing agentic AI to where the workgroup data lives, so teams can move fast, have enough compute power to handle serious workloads and scale into the broader enterprise AI architecture.

For developers and power users, that means a secure local environment where they can iterate without sending every step to a remote model. For IT, it means real control over cost, governance, data exposure, and operational risk. For the business, it means a faster path from experimentation to workflows that improve productivity, decision-making, and innovation.

With NVIDIA OpenShell now supported across the entire Dell AI Factory, enterprises get a secure, sandboxed environment to build, deploy and govern AI agents across the Dell AI Factory from workstations to servers. Teams can develop and refine agents at the deskside, then move successful workflows toward data center deployment with a more consistent operating model.

And our customers don’t need to navigate this shift alone. Dell Services guides organizations through the full agentic AI lifecycle — from initial strategy and use case identification through deployment, agent prioritization and scaling — because the infrastructure decision is only the beginning.

I believe the winners in enterprise AI will be the organizations that consistently bring AI to where the data lives, and that means embracing a hybrid approach. In the cloud, where elastic scale and access to the largest frontier models remain essential. In the data center, where consolidated enterprise datasets power AI at scale. At the edge, where real-time operational data demands instant inference. And at the deskside, where teams work with proprietary code, research and regulated information that cannot and should not move to remote environments.

No two enterprises will strike the same balance. The right mix depends on the business: its data, its regulatory context, its workloads and its economics. Hybrid is not a compromise. It is the operating model for the agentic era. 

The compute pendulum is swinging toward the data, wherever it is created and lives. Not because the cloud matters less, but because AI must meet data where it is created, governed and consumed. For a growing share of enterprise knowledge work, that place is the workgroup.

That is the architecture we are building. And the pendulum is just beginning its swing.