Network operations (NetOps) teams have been managing more for years. Users and businesses have higher performance expectations despite increasing complexity from more applications, devices, cloud dependencies, security controls, and telemetry. Now AI workloads are adding another layer of NetOps demand with new traffic patterns, performance requirements, and operational complexity.
A recent Cisco and Omdia study of 1,000 IT and network operations leaders confirms this shift to AI-led networks run by autonomous agents. It shows a market that is moving faster than many expected: 75% of organizations have already deployed AI for network operations. More than half are running agentic AI systems that act in production today, and 84% expect to reach an AI-led operating model within 12 months.
Network operations leaders aren’t simply testing AI at the edge of the workflow; they’re allowing agents to take on operational responsibility, including actions in production environments. Therefore, the question for enterprise leaders isn’t whether autonomous AI belongs in network operations, but what needs to be in place to safely expand the use of AgenticOps for operations across the network.
The Impact of Agentic AI on Network Operations
A 2026 Cisco and Omdia research report
The operating model has hit a practical limit
When a P1 incident hits at 3:00 a.m., NetOps teams aren’t suffering from a lack of data. In fact, they’re drowning in it. They’re surrounded by alerts, telemetry, tickets, topology changes, application signals, device events, security notifications, and user complaints.
The Impact of Agentic AI on Network Operations report puts real numbers behind that pressure. The average organization generates around 4,100 monitoring alerts and events per day, with about half tied to the network.
The hard part is connecting these signals fast enough to understand what’s happening and take action.
A typical practitioner can clear roughly 21 network alerts in a day, which means clearing the daily backlog manually would require a team of roughly 100 IT specialists. Most teams do not have anything close to that. So, teams are forced to make compromises: they triage aggressively, close alerts that remain uninvestigated, and lose focus through context-switching across multiple tools and chasing false positives. Ultimately, they sacrifice engineering time for reactive maintenance.
This current operating model is hitting a structural wall, ushering agentic AI to rapidly enter production. For many organizations, the next improvement in operations will come from reducing the burden of manual correlation and giving teams systems that can sense, reason, act, and verify within clear controls.
The hard problems cross boundaries
The research found that most issues span multiple domains. For anyone who has operated large environments, that reality is familiar. A user reports a slow application. The symptoms may appear on the wireless network, while the cause may sit in cloud connectivity, identity policy, application behavior, endpoint posture, security inspection, DNS, or an upstream provider. The issue is felt in one place, detected in another, and resolved somewhere else.
An agent with a single-domain view has limited functionality, as it can only summarize, troubleshoot, and recommend action within its specific domain. Enterprise operations, however, rarely stop at one domain. The agent needs enough context to understand how networking, security, applications, infrastructure, and user experience interact.
It’s that context that changes the quality of the decision. If an agent sees only a wireless symptom, it may recommend a wireless fix. If it also sees topology, policy, application performance, security path, and user experience, it has a better chance of identifying the real cause and choosing the right action.
This is why Cisco’s AgenticOps is so powerful: it replaces guesswork with total visibility. By providing a unified view across domains, it gives agents the context they need to act decisively and reliably within even the most complex environments.
Trust is earned in the loop
The research shows that NetOps professionals are more comfortable with operational automation than many people might assume. Eighty percent of organizations are comfortable granting AI a high or fully autonomous role in NetOps, and many are ready to enable agentic AI to make production changes for some categories of action.
But with that comfort comes clear conditions. Leaders want explainability, approval gates for higher-risk actions, policy-based limits, audit trails, emergency override, and role-based access. Nearly seven in 10 organizations require detailed explainability for AI-driven actions.
While teams are ready for AI to act, they also expect autonomy to operate inside a governed model. A trustworthy agentic system observes a condition, explains what it found, recommends an action, follows the right approval path, executes within policy, and verifies the outcome. Each successful loop gives operators more confidence in where the system can act independently and where human judgment should remain involved.
AgenticOps is built around this principle: trust is earned, not given. Agentic systems must be able to check their reasoning against the network, show the evidence behind a recommendation, operate through policy, and prove that the action improved the experience. AI’s ability to do so depends on the quality of the operational foundation around it: trusted telemetry, shared context across domains, policy-aware workflows, clear boundaries on what actions are allowed, where approval is required, and how outcomes are measured. That foundation matters even more as AI usage continues to change the network itself.
Building for where NetOps is already going
As the survey shows, agentic AI is already part of network operations. Teams aren’t waiting for a distant future; they’re transitioning toward systems that can help them act at an escalated speed.
But there is a deeper shift underneath this. As AI moves from prompt-and-response tools to long-running agents that pursue goals over hours or days, the network stops being the path between a request and a response. It becomes where the work actually happens, and the environment those workloads depend on to run reliably.
AgenticOps is human-led, agent-powered operations built on shared context and governed action. Operators stay in control; agents take on more of the work that overwhelms teams today.
The direction is clear. The next phase of NetOps will be defined by how well enterprises build the foundation for trusted autonomy: shared context, governed action, and proof that every action improved the experience. That is how autonomy earns its place in critical infrastructure, one action at a time.
For a closer look at findings, dive into the full report here: The Impact of Agentic AI on Network Operations.
