Operating a network has not changed in decades. Every network engineer knows the drill. An alert fires. You open a dashboard. Then another. Then you pull logs, review client history, ping the wireless team, and, hopefully, an hour later, there is enough context to start solving the problem. The industry spent a decade building better dashboards, even though operations teams needed fewer.

The first wave of artificial intelligence in networking was supposed to fix that, but it largely didn’t. When I sat down with Nabil Bukhari, chief technology officer and president of AI at Extreme Networks Inc., at its chief information officer event in Seattle this week, he was refreshingly candid about why and didn’t point the finger at competitors.

“The first generation of AI from multiple vendors in networking, or adjacent markets like security, was essentially a chatbot built on a lot of unconsolidated data,” Bukhari told me. “They demoed really well. Everybody showed amazing demos. But when you push them into actual production and people started using them, there were multiple areas where they just did not work.”

Extreme has 18 months of its own production data to draw on, since the Extreme Platform ONE shipped with first-generation generative AI. That experience shaped Extreme Agent ONE Coworker, which became generally available Wednesday to all Platform ONE customers worldwide as part of their existing subscription. I’ve spoken with beta customers who say it reduces resolution time from hours to minutes and cuts new-engineer onboarding time by as much as half.

Three failure modes, three fixes

Bukhari identified three specific things that broke in generation one, and each corresponds to a design decision in this release.

The first is data. “Unless you absolutely fix your data and have a custom-built context layer, it’s never going to be good enough for a network admin to actually use it,” he said.

His example is mundane and devastating: Within Extreme alone, fabric, switching, Wi-Fi and software-defined wide-area networking each carry a field called client ID in their databases, and each one means something different. Plug that into an LLM unnormalized, and the model has no idea what it’s looking at.

Extreme’s answer is a purpose-built context layer plus a knowledge graph mapping relationships across users, devices, applications, services and network conditions. I’ve argued for a while that in networking, context is the moat. Any vendor can wire a model to a chat box and have it explain spanning tree. Almost none can answer why the third-floor conference room degraded at 2 p.m. Tuesday.

The second is behavior. Extreme ran focus groups with network administrators, dropped them in front of an open chatbot, and watched what happened. Mostly nothing. “They just kind of go, ‘Ah, well, I don’t know,’ and then they ask you how many devices I have — which is a completely useless question, because it’s right there on the dashboard,” Bukhari said. Worse, when a real incident hit, engineers forgot the AI existed and reverted to old habits.

The third is honesty. General-purpose models are built to be helpful, so they’d rather generate something than admit a gap. Extreme built what it calls an awareness scale, so Agent ONE quickly determines whether it can help, says so plainly when it can’t, and then explains what it can do instead — often by packaging the evidence it collected and opening a support ticket on the user’s behalf.

In an analyst pre-briefing, Michael Jones, Extreme’s vice president of AI and a former Salesforce AI leader, demonstrated this live by asking whether Agent ONE could remediate wired devices. It said no, then listed what it could do.

Ambient beats interactive

The fix for the behavior problem is the most interesting part of this release. Agent ONE Coworker is ambient, which means it’s always running and it reaches out to you rather than waiting to be asked. Extreme calls the mechanism a Nudge: a command bar at the bottom of Platform ONE that blinks when the agent spots something, after running a pre-investigation, before interrupting you.

The early numbers suggest that framing matters enormously. “In the first eight days, we have seen about a 900% increase in interaction,” Bukhari said. “When a problem happens and you see it in your logs, nobody thinks of opening a chatbot to ask what these logs mean. Those demos well, but people don’t do that.”

The guardrails are critical. Informational nudges can be snoozed for an hour or dismissed for days. Severity-one nudges can’t be snoozed. I compared it to a car’s collision alerting: If you switch it off, it should come back on when the system thinks you’re about to crash. Bukhari’s logic was the same. If a Sev1 is still there after 15 minutes, you should probably look at it, so why turn it off?

A second flavor of nudge targets the skills gap. If the system detects you lingering on a Platform ONE screen where you knew how to do the equivalent task in ExtremeCloud IQ, it surfaces the top three things users typically do there and offers to walk you through it or just do it for you. Nobody has time to read product documentation, so this meets engineers where they are.

Trust is a gradient, not a switch

Historically, network engineers have been skeptical of automation, and Extreme is working around that skepticism rather than debating it. Coworker mode is ambient but never autonomous, built with a human in the loop by design, with governance controls that define what it may and may not touch. Operator mode, the autonomous tier, arrives early next year and includes its own inverse of the nudge: a recap that briefs you the way a human teammate would upon your return.

“Trust is not binary. It’s not like I trust AI or I don’t trust AI,” Bukhari said. “It’s gradual. You can say, ‘I trust it to do these things, but I don’t yet trust it to do these kinds of things,’ and that progression is what’s needed. Nobody is going to go from ‘I’ve never used it’ to ‘I want my entire network run by AI.'”

Standard operating procedure underpins both modes. Bukhari’s argument here is the sharpest critique of general-purpose AI in operations I’ve heard from a vendor chief technology officer: Ask a frontier model how to solve a networking problem seven times, and you’ll get seven answers. “You do not want to troubleshoot your network that way,” he said. “Networks have standard operating procedures. You can call them blueprints, you can call them validated designs — that’s how predictability is built into the network.”

Extreme loaded its own blueprints and best practices into Coworker; with Operator, customers can load theirs via skills. Bukhari said that early adopters who first tried to bolt their network data onto general-purpose frontier models became Extreme’s fastest adopters. They’d already discovered the predictability problem on their own.

Why this matters for Extreme

Fiscal 2026 ended with revenue of $1.28 billion, up 13% year-over-year, and fourth-quarter revenue of $339 million, marking its ninth consecutive quarter of sequential growth. Platform ONE drove nearly half of subscription bookings in the quarter, and the number of customers spending more than $1 million annually rose to 187.

Extreme is a challenger to much larger network vendors and cannot win a spending war. It must win on velocity and by shipping first. Platform ONE went GA in 2025 with 265 early adopters; roughly a year later, the agentic layer is live globally on a monthly release cadence, with the next wave landing at Extreme’s AI Summit in Amsterdam on Oct. 20.

Shipping Coworker before Operator is the right call. The fastest way to poison enterprise AI adoption is to let an agent make a change that no one has sanctioned. This lets Extreme roll out its AI capabilities in a measured way, letting customers get their feet wet with AI in a safe, low-risk way.

Advice for IT professionals

Pick one use case and protect it from distraction. This was Bukhari’s answer when I asked what “Just get started” should mean. “Pick a use case you care about, not a random one. It’s going to be frustrating at first, but the ramp-up is very quick.” The failure mode isn’t picking the wrong one — it’s chasing 27 things at once because the demos are shiny.

Test for honesty, not just accuracy. Use your pilot to probe the agent’s boundaries and see whether it admits gaps. An agent that bluffs is worse than no agent.

Grade vendors on context, not on model names. If the pitch leads with which large language model is under the hood, it’s a commodity conversation. Ask how the system normalizes data across domains, how long it retains history and how it maps relationships.

Measure mean time to context. Everyone tracks mean time to recovery; almost nobody tracks how much of it is spent gathering evidence before diagnosis starts. That window is what Nudge is designed to eliminate, and it’s the metric that will tell you whether 15x holds in your environment.

Bring your standard operating procedures to the table. If your runbooks live in someone’s head or on a stale wiki, no agent can follow them. Codifying them is prerequisite work for operator-class autonomy, and it pays off regardless of which vendor you choose.

The dashboard isn’t dead yet, but for the first time, the industry has a credible answer to what replaces it.

Zeus Kerravala is a principal analyst at ZK Research, a division of Kerravala Consulting. He wrote this article for SiliconANGLE.

Image: Extreme Networks