Transforming Next-Gen NOC Operations with Cisco AI IQ: Power of Agentic AI

Insight:

Artificial Intelligence is transforming every layer of networking—from operation, to optimization, to incident response. Cisco is taking a significant leap forward with Cisco AI, which includes the new AI-enhanced Agentic interface that aims to bring lifecycle management together, speed troubleshooting and provide personalized proactive insights.

This blog explains what Cisco AI is, how its Agentic interface operates and how the AI works in the background to address intricate technical problems with unmatched accuracy.

How’s Cisco’s AI-Based interface is working?

Cisco's AI-based interface operates as a single operational plane that builds technology into the fabric of the complete Cisco infrastructure stack. At its base layer, it presents a conversational AI interface where end-users interact with the network in natural language — stuff like “Why is site X down? “What are packet loss movements?”, or “Assist me in shaping my SD-WAN topology.” Under the hood, the interface seamlessly extracts real-time telemetry, automates event correlation and serves up insights. It is driven by the Cisco Knowledge Graph, a large semantically controlled code data set that has been created out of decades of documentation, TAC cases, field engineering notes, configuration recommendations and platform expertise. This information is represented such that the AI system can query, comprehend and reason over it with high fidelity. To complete that picture is Cisco’s Dynamic Data Fabric, a process of normalizing the data from devices, controllers, logs and policies into one data model. This holistic fabric allows AI to relate behavior across disparate layers of the network—routing, wireless, SD-WAN, security and applications—to identify complex issues with one touchpoint accuracy. When AI does identify an issue, the automation engine takes over to create configuration fixes, suggest workflows or process, perform approved changes and validate post-change health. This is where Agentic AI comes into play.

What is Agentic AI?

Agentic AI An even more developed form of AI that serves as an autonomous problem executer (not only a task respondent). While traditional AI answers questions, Agentic AI can understand the context of an entire environment; form and plan goals and sub-goals autonomously; whether the system is allowed to do something or the implications if it doesn’t; verify results all while continually learn from more data. In reality, it works like an around the clock virtual network engineer–correlating conditions, running diagnostics, suggesting root causes and repairs while verifying end-to-end correctness. Put simply, while classical AI answers, Agentic AI answers and reasons and acts too Sheering in a new era of autonomic intelligent network operations.

Scenario – 1 Mark, a network engineer for Verizon at Australia NOC, is in Favor of a backbone that makes use of Cisco IQ-enabled routers. Now, for over five years, an AI interface linked to Agentic AI has been monitoring router telemetry signals in real time and correlating events across models so that the system can take action autonomously to rectify problems.

Let's deep and dive--

Step-1: The latency, outage, and ping-drop reports serve as the initial input dataset for the workflow.

Here last 5 years latency report, ping drop and outage history report provided as a dataset of AI agent to create workflow as an input.

Step-2 Create a workflow where an AI Agent continuously analyses network telemetry, predicts future abnormalities before they impact users, and automatically applies corrective actions to prevent or fix the issue.

Why Cisco AI IQ based Routers?

Traditional manual operations are no longer feasible in modern networks, which have become too complex with multi-cloud and hybrid architectures that bring new patterns of failures. Meanwhile, businesses struggle with the scarcity of qualified NOC and TAC engineers who can support such dynamic environments. This has changed over time and customers are now wanting more proactive, predictive support rather than reactive break-fix and automation is a key part of delivering this. By automating diagnostics and remediation – intelligently doing so continuously – human error can be minimized, root-cause analysis time shortened and MTTR slashed, all of which supports more predictable and efficient network operations.

Conclusion:

AI-powered workflows can self-protect and self-heal networks today. By using its ability to interpret live telemetry, historical data patterns and real-time anomalies, an AI Agent has the means to predict what’s liable to fail – be that ascending latency, deteriorating link trends or an imminent outage. Rather than waiting for something to break, it is pre-emptive by applying a range of fixes, tuning configurations, or invoking workflows at scale. This predictive and self-driving methodology shifts from a reactive, firefight mode to smart, self-healing network management.