Operations | Monitoring | ITSM | DevOps | Cloud

Executives rarely state the full truth publicly, but inside boardrooms the conversation has changed. Observability, once viewed as a technical capability deep within operations, has become a strategic requirement for understanding business performance. Leaders may not always use the term itself, yet they focus intensely on the outcomes it promises. Their environments have grown too fast, too fragmented, and too interdependent for traditional visibility approaches to keep pace.

Since the advent of Artificial Intelligence (AI), it has become the buzzword for modern day businesses. It has tremendous benefits which has lured enterprises invest hefty money with a view of getting ahead of their competitors. Yet, many CIOs are still figuring out ways to get the best ROI of AI that resonates with their businesses. While there are many initial programs and proof of concepts that show promise, in the long run they fail to deliver their promise.

If you’re planning to move from ManageEngine OpManager to the Progress WhatsUp Gold solution, this guide outlines key differences, recommended migration steps, and practical checks to help you transition with minimal disruption. It also includes an example script you can use to start monitoring imported devices in the WhatsUp Gold solution.

As we wrap up RSAC, one theme that repeatedly emerged in conversations with security leaders is that the modern enterprise has reached a critical inflection point where the velocity of machine-generated telemetry has outpaced the capacity of traditional architectures. This trend requires an approach that moves beyond the storage of information to the activation of it in ways that don’t simply exacerbate alert fatigue.

Poor voice call quality isn't just annoying, it's a productivity killer. Dropped calls mid-negotiation, garbled audio on client meetings, and one-sided conversations where half the words don't make it through: these aren't random technical glitches. They're symptoms of network performance problems that haven't been identified, measured, or fixed. And when your business runs on VoIP, Microsoft Teams, or any cloud-based communication platform, unmeasured voice quality is a liability.

Imagine you're troubleshooting a production issue: your application is slow, the CPU is spiking, and users are complaining. You turn to your profiler for answers—after all, this is exactly what it's built for. The profiler runs, collecting thousands of stack samples. eBPF profilers, including the OpenTelemetry eBPF profiler, operate at the kernel level, so they capture raw program counters: memory addresses pointing into your binary.

For most of the history of software engineering, the primary constraint was production. Code was expensive, skilled engineers were scarce, and shipping features required concentrated human effort. Velocity was limited by how fast people could reason, implement, test, and deploy. That constraint shaped everything from team size, architecture, release cadence, through to how we thought about technical debt. When production is expensive, you optimise for output. You remove friction from shipping.

AI coding agents are getting good at calling tools. Claude Code, Codex, and others can run shell commands, parse JSON, and reason about the results. But they need to know what tools are available and how to use them. That's what skills are for. A skill is a small package of documentation that teaches an AI agent how to use a specific tool. We've built one for Oh Dear.