IT teams are under increasing pressure. Networks are becoming more complex, the number of system alerts continues to grow, and skilled IT professionals remain in short supply. At the same time, businesses expect high availability, rapid issue resolution and seamless day-to-day operations.

This is where AIOps and AgenticOps come into play. While AIOps analyses operational data and helps IT teams identify the root causes of issues, AgenticOps takes things a step further. AI agents can independently plan tasks and automatically execute predefined actions.

Why Traditional Operating Models Are No Longer Enough

Modern networks now span data centres, cloud platforms, edge locations and, increasingly, AI workloads. As a result, organisations are generating vast amounts of telemetry data, logs and operational events.

For IT teams, identifying critical incidents quickly is becoming increasingly difficult. The result is higher operational overhead, slower response times and so-called alert fatigue, where teams become overwhelmed by the sheer volume of notifications.

The ongoing shortage of skilled professionals further compounds the challenge. Many organisations are expected to manage increasingly complex infrastructures with limited resources.

AIOps and AgenticOps: What’s the Difference?

AIOps (Artificial Intelligence for IT Operations) uses AI and machine learning to detect anomalies, correlate events and identify potential root causes of incidents. It provides recommendations, but the final decision remains with the human operator.

AgenticOps builds on this approach. Rather than simply analysing data, AI agents can also initiate actions independently within clearly defined boundaries. This may include prioritising tasks, adjusting configurations or automatically responding to known issues.

Put simply, AIOps analyses, while AgenticOps analyses and acts.

Why AgenticOps Is Gaining Momentum

Several key developments are driving this shift:

• Increasing complexity across modern IT environments
• Ongoing skills shortages in networking and IT operations
• Growing demands for availability, resilience and security
• Advances in generative AI and autonomous agents

At the same time, vendors such as Cisco, Extreme Networks and NVIDIA are investing heavily in AI-driven operational models. What was considered a future vision just a few years ago is rapidly becoming part of real-world product strategies.

What Are the Benefits of AgenticOps?

The greatest advantage lies in reducing the workload for IT teams. Repetitive tasks can be automated, allowing specialists to focus on more complex challenges.

Key benefits include:

• Faster issue detection
• Shorter response times
• Fewer manual routine tasks
• Higher network availability
• More efficient use of skilled IT professionals

Organisations with limited IT resources, in particular, stand to benefit from automated and semi-autonomous operating models.

What Risks Should Organisations Consider?

AgenticOps is not a silver bullet.

The quality of the outcomes depends directly on the quality of the underlying data. Fragmented or incomplete telemetry data can lead to inaccurate analyses and poor decision-making.

Clear governance policies are equally important. Organisations need to define which actions may be performed automatically and where human approval should remain mandatory.

For this reason, most successful implementations begin with a human-in-the-loop approach, where AI agents provide recommendations and employees make the final decision.

Conclusion

AgenticOps is emerging as the next logical step in network operations. While AIOps already helps organisations reduce complexity and detect issues more quickly, AgenticOps enables a significantly higher level of automation.

For IT decision-makers, the key question is no longer whether AI will play a role in network operations. The real challenge is identifying which processes can be automated effectively while ensuring security, transparency and control at every stage.

Organisations that begin with clearly defined pilot projects while strengthening their data and governance strategy will lay the foundations for more efficient, resilient and future-ready network operations.