For most of networking’s history, the answer to managing more complex networks was more skilled engineers. That formula is running out of runway. A typical enterprise network today spans on-premises infrastructure, several public clouds, SD-WAN fabrics, and security overlays, running on hardware that spans a decade or more of purchasing cycles. Change volume, the number of moving parts, and the ambiguity of the signals coming off all of it have grown faster than any team can hire against.
This is the pressure behind the industry’s sudden interest in agentic network operations: software built to reason on live network context and act inside guardrails a human sets, rather than follow a fixed script or wait for a person to interpret an alert. Gartner’s first Market Guide for Agentic NetOps Software, treats this as an emerging category in its own right, built around software that investigates, reaches a conclusion, and, when permitted, acts on it.
There’s a sequencing problem worth naming before the autonomy conversation gets ahead of itself, though: an agent can only act as well as it can diagnose. And diagnosis, it turns out, is where most of the hard engineering still lives.
The Constraint is Codifying and Making Knowledge Available
For years, network operations worked as a set of gates where NOCs triage, then escalate to senior engineers to diagnose and fix issues. It can no longer scale solely via adding headcount. Hire enough senior engineers who carry the network’s history in their heads, and you could keep up with what broke. That philosophy has broken down. The constraint isn’t really a shortage of skilled people, although that shortage is real. It’s that no amount of headcount can keep pace with how fast modern networks generate ambiguous signals across dozens of overlapping domains at once. This is because knowledge is not shared and leveraged by others to solve and prevent reoccurring incidents.
That’s a diagnosis problem before it’s an automation problem. Nobody can safely hand a remediation workflow to software, or to a junior engineer for that matter, until they can trust what it concluded about the root cause. A lot of vendors currently marketing “agentic” operations are further along on the acting half of that equation than the reasoning half, which is backward.
Multi-Vendor and Ecosystem Integration Are Needed
Most conversations about complex networks focus on multi-vendor support: the ability to read and reason across devices from different manufacturers, not just one. No hardware vendor can offer that. Each vendor’s own tooling is naturally scoped to its own equipment, so a unified view across a mixed estate depends on an independent ecosystem layer, one most tools still don’t provide.
That’s only half the problem. The other half is multi-domain: integrating across the different categories of tools a NOC runs, ITSM platforms, monitoring systems, automation tools, ticketing systems, and more. Each of those was built for its own domain, and few products bring them together into one coherent workflow.
Getting both right, multi-vendor reach across hardware and multi-domain integration across the broader toolset, is what separates a diagnosis engine that works in a demo from one that holds up in a real production environment.
For diagnosis to work, it needs both kinds of reach: every vendor’s hardware, and every tool the team already relies on. That’s a materially harder problem than either one alone, and it’s what decides whether an agent is useful in the messy middle of an incident.
Autonomy Is a Staircase, Not a Switch
Handing work over to an agent isn’t one decision, it’s a series of narrower ones about what gets delegated, where, and under what conditions. A team might automate one specific, pre-approved class of CVE remediations and nothing else. They might run a more permissive policy in the data center than at the branch. They might scope by ticket type, by application, or by time of day. Setting those boundaries is most of the real work, and it looks less like buying a product and more like writing policy.
It helps to think of the progression as stages, not a binary switch. There’s engineer-driven work, where software just makes the person faster. Beyond that, NetBrain‘s Harness treats trust as a configurable setting rather than a fixed default: human-in-the-loop, where an engineer controls each step; human-on-the-loop, where an engineer approves before anything executes; and fully autonomous, where a narrowly scoped process runs from ticket to resolution under policy, with rollback presented as an option if validation fails. That setting can be applied globally or per action type.
Almost nothing should start at the far end. Gartner puts industry-wide agentic NetOps adoption under 1% today, projected to reach 50% by 2030, which is consistent with how early this still is for most teams. The actual skill is deciding, workflow by workflow, when a process has earned the right to move up a stage, and that judgment call belongs to the network professionals who know which of their own runbooks are genuinely ready and which just look ready on paper.
A Confidence Score Is Not the Same Thing as Trust
Here’s a position worth stating plainly: when a decision affects production traffic, “the model is 85% confident” isn’t something anyone can defend in a change advisory board meeting, no matter how good the underlying model is.
What actually holds up is reasoning that can be walked back through, the way a senior engineer would talk you through their own thinking out loud: here’s the evidence I looked at, here’s what I ruled out and why, here’s the conclusion. Software that reasons this way and then executes precisely, rather than handing over a probability and letting a human absorb the risk, is what makes wider delegation possible later. Nobody delegates to a system they can’t audit, and an audit trail has to mean more than a log line with a percentage attached.
The Best Diagnosis Work Should Make the Next Diagnosis Faster
There’s a part of this that tends to get undervalued: what happens after a problem gets solved. Every diagnosis surfaces something true about how the network is actually behaving right now, where it has drifted from intent, where the as-built configuration and the as-designed architecture have quietly diverged. At the pace most NOCs run, that insight evaporates. The post-mortem that would have captured it never happens because another ticket is already in the queue behind it.
The systems worth paying attention to treat that reasoning as a durable asset instead of a one-off. The logic behind a resolved diagnosis gets captured as a reusable check that can run proactively against the rest of the estate, hunting for the same condition anywhere else it might be hiding. That compounding learning loop matters more than any single automated fix: the network doesn’t just get repaired faster; the operation gets measurably smarter, incident over incident.
Start Where the Trust Actually Gets Built
If there’s one takeaway here, it’s to resist the pull toward the most visible, most automatable action and work backward from there. Start where trust has already been proven: diagnosis. It’s the one place nearly every customer already relies on AI, it’s where a network’s real state meets its intended state, and it’s where a learning loop can actually begin. Change validation, governance, and prevention all build on that same foundation, so getting diagnosis right first isn’t caution for its own sake, it’s what the adoption data already shows works.
Gartner’s own research points in the same direction, noting that agentic approaches to network operations deliver the most value where manual investigation, diagnosis, validation, and multidomain response dominate operational time and cost.¹ That’s not a coincidence. It’s the same bottleneck showing up in analyst research and on every NOC floor.
For network teams thinking through where their own network sits on this path, NetBrain publishes an ongoing field journal called NetOps Advance: practical, non-pitch write-ups for people who run hybrid networks day-to-day, grounded in what’s actually working for early adopters navigating this shift. New issues are published regularly at advance.netbrain.com.
¹ Gartner, Market Guide for Agentic NetOps Software, 19 May 2026, Mike Leibovitz et al. Gartner is a trademark of Gartner, Inc., and/or its affiliates.
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