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What Is Agentic NetOps? Gartner Just Named the Category, Here’s What It Actually Means

John Capobianco

Head of AI & Developer Relations

Last updated August 25, 2026

Key Takeaways

- Agentic NetOps means AI agents executing network operations under governance, not chatbots answering questions about the network.
- Gartner projects that by 2030, AI agents will be the most common approach for executing network runtime activities, up from less than 1% in early 2026.
- Production adoption requires pairing probabilistic reasoning with deterministic execution.
- Infrastructure-independent software matters where operational complexity spans domains and vendors – embedded tools cannot coordinate what they cannot see.
- Adopt on the autonomy ladder: read-only → recommend → supervised execution → convert proven agents into deterministic workflows.

Twenty-five years in this field. CLI, then scripts, then pipelines, then platforms. And this spring, the thing a bunch of us have been building toward finally got a name: in May 2026, Gartner published its first-ever Market Guide for Agentic NetOps Software.

Every once in a while the industry hands a name to something practitioners are already living. It happened with DevOps. It happened with NetDevOps. It just happened again. So let’s do what we always do here. Skip the hype and break down what agentic NetOps actually is, what it is not, and what it means for how you run networks.

What Is Agentic NetOps?

Agentic NetOps is the use of AI agents in network operations – software that takes an operational goal, translates it into a plan, and executes that plan under governance. Not a chatbot that answers questions about your network. Not a script that does exactly one thing. An agent that can reason about intent, choose a course of action, carry it out, check the result, and keep going until the objective is met.

The key phrase in that definition is “under governance.” An agent without guardrails is just a language model with credentials to your routers.

💡 What makes agentic NetOps a real operational discipline is the machinery around the agent.
Validation before an action runs, verification after, rollback authority, and autonomy you can dial from human-approved to fully autonomous.

From “Cute” Chatbots to “Competent” Agents

Gartner’s research includes a framing I genuinely love, because it matches exactly what I have watched happen in the field. In 2023, AI in networking was “cute.” Chatbots. “How do I configure a VLAN?” Helpful? Sometimes. Operational? No.

By 2027, Gartner expects “competent” AI – agents that reason to achieve goals, prompted or unprompted. Not a chatbot answering your question. A colleague working the ticket.

And the stakes are bigger than most teams realize.

💡 Gartner projects that by 2030, AI agents will be the most common approach for executing network runtime activities, up from less than 1% in early 2026.

Read that again. The most common approach. In under four years, the default way network work gets executed flips.

The Magic & The Menace of Probabilistic Software

Here is the thing nobody puts on the marketing slide. The same property that makes AI brilliant is the property that makes it dangerous on a production network.

A language model is probabilistic. It weighs evidence and picks the most likely path. That is why it can correlate three alerts that have never appeared together in the history of your NOC and tell you they are the same incident. Nobody scripted that. Nothing deterministic could have done that. It is genuinely magic when you watch it happen. It is also why the same model, asked the same question at 2pm and 2am, can hand you two different answers. Magic in a diagnosis. Catastrophic in a change window. This is where most agentic AI projects go wrong.

Production does not want creative. Production wants the same input to produce the same output, every single time – because availability, security, and compliance all depend on it. That is what our workflows and automations have always given us, and it is also why pure automation eventually stalls: a workflow cannot handle anything it was not explicitly built to handle.

So you need both, and you need them in the right seats. Most modern agents run a ReAct loop – reason about the situation, take an action, observe the result, reason again. That loop is what lets an agent work a problem the way an engineer does. The question is what happens at the “act” step.

💡 Agentic NetOps Done Right
In agentic NetOps done right, the acting is not the model improvising against your routers. It is deterministic execution carrying out the change identically every time, with validation and rollback wrapped around it. The agent owns the reasoning in the loop. The platform owns the acting.

I said it in a blog earlier this year and I will keep saying it: governed, not chaotic. Or the way I put it to every team I work with – let the agent be creative about the diagnosis. Never let it be creative about the change.

Gartner’s research lands on the same requirement: products that achieve real enterprise adoption combine probabilistic reasoning with deterministic implementation, with defined guardrails – an approach often called an agentic harness. Without one, agents engage in what the research calls directionless execution. I have seen directionless execution. You do not want it.

The Choice That Actually Shapes Your Architecture

Gartner splits this software into two forms:

Infrastructure-Embedded
Agentic capabilities inside one network vendor’s own platform.
Infrastructure-Independent
Software that reasons and acts across every domain and vendor you run.

Here is my plain-language test. Where does your hardest operational pain actually live? After 25 years I can tell you: it lives in the seams. The incident that spans the firewall, the WAN, and the cloud. The change that touches three vendors’ gear. Software embedded in one vendor’s stack cannot coordinate what it cannot see. If your network were 100% one vendor, embedded tools would be fine. I have never met that network.

What I Have Learned Actually Matters

I build with agents every day, it is the best part of my job. Two years of that, plus a couple of decades of getting burned by every previous wave of network automation tooling, has boiled down to a short list of convictions about what separates agentic software that works from demos that impress:

Context beats model.
An agent that knows your topology, your templates, your runbooks, your design intent will outperform a smarter model that only knows general networking. Context is the difference between accurate reasoning and confident hallucination – and it cuts your token bill too, because an agent that is not guessing burns fewer cycles.
Guardrails have to be mechanical, not aspirational.
Pre-execution validation, post-change verification, rollback. If you cannot watch them fire, they do not exist.
Autonomy is a dial, not a switch.
Human-in-the-loop today, supervised tomorrow, autonomous where the agent has earned it. All-or-nothing autonomy is how pilots die.
Your existing automation is an asset, not technical debt.
The scripts and workflows your team spent years building are exactly what agents should execute through. Anything that asks you to rip that out is selling you a rewrite, not a platform.
Know your AI economics before production.
Model the cost under real usage, not pilot usage. Token pricing that looks cute in a proof of concept scales against you the moment your agents start succeeding. Ask me how I know.

The Autonomy Ladder

Nobody should hand an agent their provisioning pipeline on day one. The teams doing this well – and I get to work with a lot of them – climb a ladder, and every rung earns the trust for the next:

Read-Only
Investigation, root cause analysis, alert correlation, documentation. Agents that look but do not touch. This is where value shows up first, because Day 2 work is reasoning work, and reasoning is what agents are for.
Recommend
The agent proposes the fix, a human approves, deterministic automation executes. The agent is in the change process without being in control of it.
Supervised Execution
Scoped, reversible changes with validation on both sides and rollback standing by. Expand the scope as the track record grows.
Convert What Works
When an agent proves out an approach, turn it into a deterministic workflow and let the agent move on to the next unsolved problem. This rung is my favorite, because it is the difference between a platform strategy and a permanent science project.

Do not start by handing an agent your provisioning pipeline. Start by handing it your ticket queue.

Agentic NetOps: Key Use Cases & Best Places to Start

When you’re first getting started with agentic NetOps it’s best to start with use cases that are relatively small and safe. Begin by trying out use cases that only require read-only agents first (the same autonomy ladder covered earlier in this post), not a provisioning pipeline on day one.

One concrete build: a Network Interface Health Agent on Itential’s FlowAI platform, built through natural-language configuration and deterministic workflows underneath, that:

Connects to a device and collects live interface state
Dynamically generates health tests based on what it finds
Checks for errors, packet drops, flapping, and utilization anomalies
Delivers a report to Slack and email

No test code, no hand-rolled Slack API integration, no parsing logic to maintain against every vendor CLI change.

Build Agents vs. Operate Agents

There’s an even lower-risk on-ramp than read-only, though: build agents.

As Chris Wade, Itential’s co-founder and CTO, laid out in his AutoCon 5 keynote, build agents (tools like Claude Code or Cursor) generate deterministic automation through spec-driven development and never touch production themselves, only the code they produce does, after a human reviews it.

Operate agents, by contrast, act inside the network directly (troubleshooting, upgrades, port turnups), and that’s where the autonomy ladder above actually applies.

The Guide to Build Agents vs. Operate Agents

Tried & Tested Agentic NetOps Use Cases

Beyond health checks, the Gartner Market Guide for Agentic NetOps Software (May 2026) names five use cases already proven in production across telecom, financial services, and utilities, validated through Itential’s FlowAI Innovation Program with six enterprise customers:

Incident triage
Pre-flight change validation
Fault remediation
Firewall policy automation
Compliance evidence collection

These map cleanly onto the “AI reasoning plus deterministic execution” model: narrow, high-frequency problems now collapsing from weeks of specification and scripting into hours of natural-language iteration. Gartner’s research adds supporting context: prediction is already one of the best-established AI/ML use cases in networking, which lines up with why incident triage and fault remediation are among the first production wins.

You can learn more about how these agents work in practice with the video below, where Joksan Flores demonstrates how to build an AI agent for network device health checks, and then repurposes that same agent for platform, routing, interface, security, and environmental checks by changing a single parameter.

You should also watch this webinar with Karan Munalingal (SVP of AI Strategy & Innovation at Itential) and Holly Holcomb (Head of Customer Success at Itential) for a deep dive on the right way to approach building AI agents that actually work in enterprise network infrastructure, with concrete real life lessons you can apply to your own Agentic NetOps program.

Where Itential Fits

If that list of convictions reads like a product description of FlowAI, that is not an accident – it is why I joined this company. A decade of deterministic orchestration came first. FlowAgents reason about goals, scoped by natural language skills before they ever run, acting only through authorized tools, with the Itential Platform executing every change deterministically across every vendor in your environment.

I built my first production-grade FlowAgent in about two minutes and I wrote about that experience because I still cannot quite believe it. It is also why Gartner named Itential a Representative Vendor in this Market Guide.

Let’s Keep Talking

This category is brand new, which means the people figuring it out are figuring it out together. If you are working through what agentic NetOps means for your team, I would genuinely love to compare notes.

📖 Get the complimentary 2026 Gartner Market Guide for Agentic NetOps Software →
The report that defined the category.

🤝 Join me in the VibeOps Forum →
Where practitioners are sharing what is actually working (and what is not).

🤖 Watch me build a FlowAgent →
You’ll see how easy it really is.

💬 Or reach out to me directly on LinkedIn →
I read everything and I love this stuff.

John Capobianco

John Capobianco is the Head of AI & Developer Relations at Itential, and a technology leader, developer advocate, and builder at the intersection of AI and network automation. With a career spanning enterprise, government, and cloud networking, John has held roles including Head of Developer Relations at Selector AI, where he focused on AI-driven observability, configuration intelligence, and autonomous network operations, as well as Cisco AI Technical Leader and Senior Network Architect for the Parliament of Canada / House of Commons. He brings deep, hands-on experience applying automation and AI in highly regulated, mission-critical environments. His work centers on helping large organizations adopt AI safely while maintaining reliability, security, and operational trust. John is a former professor at St. Lawrence College, an author, speaker, and educator. He is the author of Automate Your Network (self-published, 2019) and the Cisco Press pyATS book (2024). He regularly shares insights through talks, workshops, and the Automate Your Network brand, with a focus on practical, production-ready AI, developer empowerment, and the evolution of network engineering in an AI-first world.

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Frequently Asked Questions

–+

Agentic NetOps is the use of AI agents in network operations: software that takes an operational goal, translates it into a plan, executes it under governance, checks the result, and keeps going until the objective is met. It is distinct from chatbots that answer questions and scripts that do exactly one thing.

–+

An agentic harness is the combination of probabilistic reasoning with deterministic implementation and defined guardrails that comply with enterprise policy. The agent reasons and plans; deterministic execution carries out every change identically, with validation and rollback wrapped around it. Without a harness, agents engage in directionless execution.

–+

Because production does not want creative. A probabilistic model asked the same question at 2pm and 2am can hand you two different answers – magic in a diagnosis, catastrophic in a change window. Production wants the same input to produce the same output every single time, because availability, security, and compliance all depend on it.

–+

Infrastructure-embedded software builds agentic capabilities into one network vendor’s own management platform, which works if your network is 100% that vendor. Infrastructure-independent software reasons and acts across every domain and vendor you run. The hardest operational pain lives in the seams – incidents that span firewall, WAN, and cloud – and software embedded in one vendor’s stack cannot coordinate what it cannot see.

–+

Climb the autonomy ladder: start read-only (investigation, root cause analysis, documentation), move to recommend (agent proposes, human approves, automation executes), graduate to supervised execution on scoped, reversible changes, then convert proven agent approaches into deterministic workflows. Do not start by handing an agent your provisioning pipeline – start by handing it your ticket queue.

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See how Itential connects AI reasoning to governed execution across your entire infrastructure.

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