Operations | Monitoring | ITSM | DevOps | Cloud

In 2026, we are seeing incredible feats of engineering with agentic AI, impacting metrics and distributed traces that map thousands of microservices. Our systems have never been more intelligent and complex. However, as our observability becomes more intelligent, fewer employees know how to manage and troubleshoot complex systems. These employees, who often bear the brunt of an error’s impact, may need to rely on specialists to interpret the system.

It’s your site’s huge, annual sale weekend, and your online store’s checkout process went down for 10 minutes. At your conversion rate, that’s $10,000 in lost sales. Thankfully, it came back up after only 10 minutes, but the real issue is that you only found out from customer complaints on social media. You spent months on email marketing and other campaigns driving traffic to this sale, and now those efforts are turning into customer frustration instead of revenue.

Dashboards provide a useful prism through which we can study the broader evolution of the IT professional’s role in the era of agentic AI. For years, dashboards have been the centerpiece of IT work, serving as the interface where teams interpret system behavior, diagnose issues, and plan actions. Dashboards epitomize the relationship between humans and their systems: humans observe, interpret, and act. As agentic AI enters the picture, that relationship begins to change. Let’s explore how.

This third article in our Agentic AI Essentials series lays out a process for capturing feedback and setting out a clear framework for effective use of an agentic system. It’s more than just collecting data; it's building a system you can trust. Let’s explore what success looks like in your environment.

In the last article in this series, we explored how IT professionals and leaders can cut through the hype surrounding agentic AI and gain a deeper understanding of what the technology actually offers. Now, we turn to the practical side: how to integrate it effectively. Let’s explore the challenges and outline strategies that organizations of all sizes can use to adopt agentic AI with confidence.

Amid predictions that 39.42 billion devices will have internet connectivity by 2030, IP address management has become a fundamental housekeeping and security concern for any networking admin. As the Internet of Things (IoT) continues to endow more and more devices with smart capabilities, networking grows more complex, making IP-centered network security measures a business imperative.

Now, we will explore how this foundational strength translates into organizational maturity and lay out the durable operating habits required to bridge the gap between reactive firefighting and strategic performance engineering.

The IT landscape has evolved rapidly, transitioning from monolithic applications to complex, distributed system architectures comprising microservices that run on platforms like Kubernetes. With this added complexity, simply checking if a server is running is no longer sufficient. As IT professionals, we need insight into what’s really happening inside these systems. That’s where observability comes in.

In the first article of our Agentic AI Essentials series, we’ll establish what makes agentic AI distinct. We’ll look at the process of tool calling and examine how agentic systems convert intelligence into action. We’ll also explore the human fears, pressures, and ambitions that fuel the hype around agentic systems. By sorting the signal from the noise, IT decision-makers can take the first step toward making sound decisions around agentic AI adoption.

Damaging cyber attacks are a rising concern as organizations increasingly rely on digital technology for managing sensitive data and running core business operations. While technology can increase business efficiency, without security measures in place, a digital-first approach can end up introducing vulnerabilities and putting data at risk.