Autonomous Networks: Delivering on the Promise of AI

The experimentation stage is done, and the verdict is in. According to Extreme Networks’ State of AI for Networking 2026 report, nearly eight in 10 organisations have deployed agentic AI in their networking environments. For the rest, the problem isn’t a lack of vision or ambition, but rather systems that weren’t designed to work with or scale alongside AI – combined with inflated expectations following the initial demo and prototype results. Networks built for older workloads, data created solely for operational purposes, and the lack of a semantic layer to help AI and agents understand the true meaning of enterprise data – all of this simply cannot support the accuracy, scale, real-time access or adaptability that AI requires. 

With AI going from experimental to essential, it’s no longer enough for enterprise networks to react to issues as they arise. They need to predict what’s coming, reconfigure themselves in an instant, and operate at a scale manual oversight just can’t sustain.  

That requires a redefinition of the network foundation, one that enables faster execution, better coordination and sustained momentum across the business. As AI agents begin operating across enterprise systems, the network stops being a background element and becomes the very space in which they operate. 

Networks that won’t wait 

Engineers don’t need to hunt for bottlenecks or manually fix failures themselves. AI detects disruptions before they ripple outward, recommends remediation actions, and, if set do so, can execute them autonomously.  

Until then, it works under strict controls, keeping humans in the loop for critical choices while ensuring governance, transparency and traceability. This means every automated decision can be examined, verified and audited. 

Multi-agent systems handle this complexity. Some agents focus on narrow jobs while others interpret business intent and coordinate capabilities. 

As trust builds through continuous evaluation of system accuracy – reinforced over time by repeated, consistently correct decisions – controls can be gradually relaxed, allowing processes to move faster and execute changes while human involvement shifts from being “in the loop” to being “on the loop,” focusing on supervision and freeing up time for proactive work and strategic tasks. 

Building trust is an essential step on the path to success. The technology matters, but the bigger shift is human. People have to get comfortable relying on something that behaves differently from traditional systems, and that takes time and evidence. 

That evidence is stacking up. The State of AI for Networking report shows 88% of organisations already rely on more than one AI-powered tool to run networking and security operations. For teams stretched thin and struggling to keep up with complexity, this is now a practical necessity. The real question isn’t whether autonomous networking will arrive. It’s whether the underlying infrastructure can support it, and whether the organisation and its people are fully prepared to take advantage of it. 

The strain is real. In fact, 92% of leaders now say AI is putting heavier demands on bandwidth and computing resources. When systems are under this kind of pressure, gaps in visibility, access control and governance get exposed fast. 

Security under new conditions 

This is where many organisations underestimate the changes happening. As AI workloads spread, networks fill up with non-human identities that are constantly active. Agents in finance, marketing, engineering and operations are constantly interacting with systems, data and with each other. That challenges a lot of assumptions about security. These entities make decisions, adjust behaviour and trigger actions based on outcomes. Traditional identity and access models were built for people and predictable machines, not AI agents that learn and take action on their own.  

And yet, confidence is rising. Research shows that 93% of executives, for instance, believe that AI-powered networking helps reduce security risk rather than increase it. That shift matters, signalling a move away from fear and toward practical control. 

That control comes from tightening the boundaries around what AI agents are allowed to do. 

Permissions can’t be broad or permanent. They need to be narrowly defined, context-aware and continuously checked, granting access based on purpose, data sensitivity, and context. Every action needs to leave a trail so organisations can understand and verify what’s happening across the network. Past incidents show exactly what can happen when these rules and architectural decisions aren’t established correctly from the start. 

But none of this works without complete visibility and high-quality data. If you can’t clearly see how traffic moves and how systems connect in real time, enforcement becomes a theoretical jumble and agents can’t act effectively. In fast-moving environments where systems constantly adapt and interact, real-time visibility allows organisations to spot unusual activity, understand dependencies, and respond before small issues escalate. Full observability across a unified network is essential for managing performance, resilience, and security. It enables organisations and their agents to connect insights about identities, access rights, and system behaviour, turning isolated data into actionable control across the network. 

Where theory meets practice 

Let’s use retail in 2026 as an example. AI enables hyper-personalised shopping through IoT sensors, cameras, mobile apps, edge devices and AI platforms. Inventory data, customer preferences and behavioural patterns flow constantly. Shelf labels, RFID systems and automated checkouts all require network connectivity. 

Healthcare is also heading toward an increasingly connected reality. AI supports diagnostics, predictive alerts and robotic-assisted procedures, with patient information moving nonstop across EMR systems, monitoring devices and AI analysis platforms. If infrastructure and access controls aren’t designed properly, the network becomes both a critical pathway and a vulnerability point. 

Manufacturing sees this evolution at scale, using AI to predict equipment failures, optimise production lines and coordinate autonomous robots as sensors and machinery communicate continuously. 

When networks aren’t built to support distributed AI safely, the number of connected endpoints grows fast, and the stakes grow with it. 

Across industries, without a unified enterprise network, strong access control, and clearly defined human checkpoints, a single misconfigured AI agent could leak sensitive data, shut down operations or even endanger lives.  

Security and AI-ready networks must be foundational to make innovation safe in 2026. 

Production AI, not experimental AI 

2026 is the year AI makes the move from experimentation into production. Ambition stops being the differentiator. What separates leaders from those left behind is whether the network underneath can keep up.  

AI systems rely on networks that can anticipate demand, enforce boundaries, and adapt continuously, without slowing everything else down. And here’s the key: a strong network doesn’t just support AI– it becomes smarter through AI. By leveraging intelligent automation, centralised data, and AI-driven insights, networks can optimise themselves, maintaining performance, resilience, and security at scale. Autonomy becomes an advantage, not a risk, and AI starts delivering value at the speed your business demands.  

The organisations that get this right won’t just deploy more AI. They’ll run faster, operate more securely and respond to change with far less friction. Embedding AI into the network is the key to achieving the promised outcomes and ROI.