5 Defining Traits Every Self-Driving Network Needs

Networks used to have one job: stay up, says Sujai Hajela, HPE's executive vice president and general manager of campus and branch networking. But in today’s AI era, that's not enough, and manually managed networks simply can’t keep up with the growing scale, complexity and expectations around modern distributed network infrastructure.

Now, Hajela says, “up is not the same as good.” What organizations need are self-driving networks designed to track what users — from C-suite leaders in their offices to robots on factory floors — are actually experiencing, and use that insight to spot problems before they cause disruptions, adapt in real time and continuously optimize themselves.

As agentic AI technology becomes more mature, networks now have the ability to act automatically across campus, branch, routing and data center environments — at a scale human operators can’t match — ensuring both uptime and user experience keep pace, Hajela says.

“Up is not the same as good.”

But building one takes more than deploying another AI tool. A truly self-driving network requires these five defining traits.

1. AI Efficacy Builds Trust

AI efficacy — consistently diagnosing problems correctly and knowing what action to take — is the foundation of trust in a self-driving network. “If you do not have proven efficacy, how do you trust the solution?” Hajela asks.

Hajela compares it to a self-driving car. You may understand how the technology works, but when your car needs to turn left in front of oncoming traffic, you need confidence it will not only perform the action but cross the road without incident. “You cannot afford false positives,” he says. “Imagine you let the network self-drive without efficacy. It can bring your network down.”

2. AI-Native Architecture Provides The Right Foundation

From there, the system can recognize a problem, draw on what it has learned and take corrective action.