The Overlooked Role of Networks in AI Infrastructure

Organizations are pouring money into GPUs, servers, models and the compute capacity needed to move AI into production. But agentic AI puts different demands on the network.

A chatbot typically waits for someone to ask it a question, whereas an agent can be given a task, interact with different systems and applications, make decisions and take action. As agents take on more work across the enterprise, much of that activity will happen without someone at a keyboard.

According to Google Cloud’s State of Infrastructure in the Agentic AI Era, 83% of organizations say they require infrastructure upgrades to support production-grade autonomous systems. And although compute power is part of the picture, organizations also need to prioritize their network, because it will need to support activity that doesn’t follow the typical pattern of a human employee.

The Death of the 9-to-5 Network

For decades, enterprise networks have largely been designed around how people work. Employees connect to applications, access data and consume cloud services throughout the workday, creating relatively predictable traffic patterns that shape network architecture and operations.

AI and agentic systems are changing that equation. Agents can execute tasks and interact with applications, APIs, data sources and other agents at any hour, creating more continuous, dynamic traffic flows across campus networks, data centers, cloud environments and the WAN. As deployments scale, networks must support increasingly complex dependencies, making performance, latency, resilience and security critical to reliable execution.

These demands also expose the limits of traditional NetOps models, which rely on monitoring tools to identify issues and human operators to investigate and resolve them. The next generation of NetOps must move beyond managing individual devices and events towards understanding application and agent dependencies end to end, using deeper visibility, AI-driven correlation and root-cause analysis, and intelligent automation to proactively optimize and remediate issues.

The opportunity is to move from networks managed with AI assistance to networks operated through AI-driven intelligence and governed autonomy. Network teams can spend less time troubleshooting repetitive issues and more time defining policies, establishing guardrails and optimizing architectures. The death of the 9-to-5 network isn’t simply about networks running 24/7; it’s about a fundamental shift in what drives network demand and how networks must be designed, secured and operated to support an always-on ecosystem of intelligent, autonomous systems.

From a Few AI Tools to Fleets of Agents

Most organizations are still at a stage where AI is easy to manage, with copilots, assistants and AI applications running across different parts of the business.

However, IBM found that only 11% of the leaders surveyed are fully prepared for the scale of AI-agent deployment expected over the next 12 months. As deployments grow, network teams will have more activity to understand and more issues to investigate, often without additional people doing the work.

That makes it important to reduce the time engineers spend searching dashboards, gathering information and escalating routine questions. Giving teams relevant operational information and troubleshooting guidance when they need it can also make expertise easier to share across teams, rather than relying on a small number of experienced specialists to answer every question. People’s expertise is better spent on the decisions that need judgment, such as defining policies, setting boundaries and evaluating risk.

The Network Needs a Seat at the AI Strategy Table

AI planning often starts with models, applications, data and compute resources, but networking should be part of those discussions from the beginning. Agents need the right context to make useful recommendations, and in network operations that means understanding how information about users, devices, and applications fits together rather than looking at an isolated alert or metric.

That context helps teams reach the source of a problem faster and decide what to do next. Human oversight still matters, especially as agents gain more autonomy, which requires well-defined limits on what they can do independently and controls to enforce those boundaries. Teams should also be able to see why a recommendation was made and what information it was based on before deciding whether to act on it.

Gartner predicts that by 2029, at least 70% of organizations running AI agents in their IT operations will have serious problems with material service, security or cost that is at least partly down to weak controls. That is why visibility, automation, reliability and control need to be built into AI infrastructure plans from the start.

Building Infrastructure for an Always-On AI Enterprise

Agentic AI changes how enterprise networks are used. Activity is no longer tied as closely to when employees are online, and network teams will need to manage that change without allowing the work required to operate the network to grow at the same pace.

Enterprises don’t have to entirely overhaul their networks, but they should identify potential weaknesses in their current infrastructure as their AI use grows, particularly where teams lack the visibility to quickly identify problems, where response times begin to suffer or where routine troubleshooting still requires significant manual work.

A network built around a nine-to-five working day will have to give way to one that supports people, applications and agents working side by side, often on very different schedules and at very different speeds. Getting that infrastructure right will ultimately show up in the measures businesses already care about: fewer disruptions, faster resolution when something goes wrong, better user experiences and less time spent tracking down problems.