Operations | Monitoring | ITSM | DevOps | Cloud

Autonomous IT operations use AI, operational data, observability, and automation to enable IT environments to detect issues, understand their context, determine the appropriate response, and act with minimal human intervention. As businesses grow, IT environments rarely stay simple. More employees, endpoints, applications, and cloud services generate even more alerts, incidents, and operational work. The traditional model scales linearly: more environment means more manual effort.

Monitoring Kubernetes in production is structurally different from monitoring traditional servers: pods are ephemeral, nodes autoscale, and the layered resource model—containers, pods, nodes, namespaces, deployments, services—creates an observability challenge that tools built for static infrastructure were never designed to handle.

There is a peculiar moment that happens when talking to AI. You ask it to rewrite an email, it does a good job, and you type, "Thanks!" Then, almost without thinking, you add, "Sorry, one more thing." It is software. It cannot be kept waiting, interrupted, or offended. Still, somehow, you have developed the manners. Then the questions get a little more personal.

Most Azure monitoring setups look the same: Azure Monitor for metrics, Application Insights for apps, Log Analytics for logs, a separate tool for network, and another for cost. Each works in isolation. None of them talk to each other when something breaks. The Azure monitoring tool in ManageEngine OpManager Nexus consolidates infrastructure, application, network, log, and cost visibility data into a single console.

How do you know a server is about to cause problems before it actually does? You track the right metrics. Not all of them, just the leading ones that consistently surface performance issues before they worsen into outages. This guide breaks down the 10 server performance monitoring metrics every SRE should have on their radar.

Strategically ready, operationally unsure. That's how 42% of organizations describe their own AI preparedness when it comes to strategy versus infrastructure, data, risk, and talent, according to Deloitte's State of AI in the Enterprise 2026 report. As AI adoption accelerates, organizations now face the mounting pressure from the board to move past pilots and show AI delivering measurable business outcomes.

AI adoption is accelerating across enterprises, but governance isn't necessarily keeping pace. As AI becomes part of everyday business workflows and applications, organizations need to understand where it is being used, what data it can access, and who is responsible for managing the risks. ManageEngine's shadow AI researchhighlights this challenge.

Top tips is a weekly column where we highlight what's trending in the tech world and list practical ways to explore these trends. This week, we're looking at how to search the web more effectively and find the information you need faster. Searching the web can feel a little like playing hide-and-seek. You know what you're looking for is somewhere out there, but the internet has an impressive number of places to hide it.

I don't remember the last time I actually searched for something. Not in the way I used to, anyway. There was a time when having a question meant opening a search engine, typing a few words, staring at a page full of links, opening three or four of them, reading contradictory answers, deciding which one sounded believable, and eventually coming to a conclusion of my own.