Operations | Monitoring | ITSM | DevOps | Cloud

AIOps tools use machine learning, big data, and automation to enhance IT operations. These tools analyze IT data, detect anomalies, and automate tasks, improving efficiency and reducing manual effort. Popular AIOps tools include Selector, Splunk, Dynatrace, Datadog, BigPanda, Dell AIOps, IBM Cloud Pak for AIOps, and LogicMonitor.

In our previous post, we explored the urgent need for intelligent automation in network automation, specifically how the Model Context Protocol (MCP) enables AI agents to dynamically discover and interact with the necessary tools. But access to tools is only part of the equation. To truly operate autonomously in complex environments, agents need not only connectivity but also intelligence.

AIOps, or Artificial Intelligence for IT Operations, is the application of artificial intelligence and machine learning to automate and improve IT operations. It combines big data analytics, AI, and machine learning to monitor, manage, and optimize IT environments, enabling organizations to proactively detect, diagnose, and resolve issues more efficiently than traditional methods.

In the last decade or so, network infrastructure has undergone a massive transformation. With the rise of hybrid cloud, distributed applications, and software-defined everything, managing networks has become exponentially more complex. What used to be a stable, predictable environment is now a constantly evolving system of interconnected services, protocols, and devices, each with its own telemetry, APIs, and failure models.

Every second counts when it comes to detecting, diagnosing, and resolving network incidents, yet many teams still find themselves stuck in reactive mode, drowning in alerts, manually writing scripts, and managing tickets across disconnected systems. This is where Selector and Itential come in. Together, Selector and Itential deliver a powerful, enterprise-ready solution that closes the loop between detection and action.

In today’s interconnected world, a fundamental question plagues every network administrator and SRE: “Is my network running well?” The answer, often elusive, is precisely what Pingmesh aims to provide. By deploying a vast fleet of specialized probe agents, Pingmesh continuously monitors critical network health metrics, including latency, packet loss, jitter, and custom reachability checks, providing an unparalleled view into your network’s performance.

Throughout this blog series, we’ve followed how AI reshapes network operations – from foundational data harmonization to real-time correlation, from contextual insights to agent-driven automation, and most recently, to conversational access through natural language interfaces. But we haven’t reached the final destination.

When we discuss networks, we typically envision data packets racing along physical wires like vehicles on a highway. But beneath this visible traffic flows another critical pathway that few recognize: the control plane highway. This unseen infrastructure, where routing information flows between devices, makes the data highway possible. Before user data can flow, millions of paths must be established, creating a parallel network of equally vital importance.

For years, network operations have relied on complex query languages that demand specialized knowledge. Extracting insights from network data often meant writing intricate commands in formats like SQL, a skill reserved for seasoned IT professionals. But what if anyone, regardless of expertise, could ask a simple question and get immediate, accurate answers from their network?