Operations | Monitoring | ITSM | DevOps | Cloud

In the previous post, we looked at what happens after detection; when incidents stop being purely technical problems and become human ones, with cognitive load as the real constraint. This post assumes that context. The question here is simpler and more practical. What actually helps teams think clearly and act well once things are already going wrong? One answer, used quietly but consistently by high-performing teams, is the checklist.

This article explains how to integrate metrics from Prometheus into Icinga checks using the check_prometheus plugin. There can be multiple reasons why this could be desired: Maybe you have different teams with their own monitoring systems, and you need to bridge the gap, or you want to perform queries that are just better expressed in Prometheus than in plain Icinga check plugins. The latter can be the case if you want to aggregate data from multiple sources or you want to take historic data into account.

From an IT pro’s perspective, the future of networking technology in 2026 is a mixed bag of potential and security risk. New wireless tech, agentic AI, and the increased distribution of networks are enabling new use cases and helping automate toil, but they also create new attack surfaces and risk profiles. In this article, we’ll take a look at the ten network security trends we’re most excited about in 2026 and provide key insights about what each one means for IT and MSP teams.

Test coverage is one of those metrics everyone agrees matters until it’s time to actually write the tests. Between shipping features, fixing bugs, and handling production issues, writing comprehensive tests for edge cases and error paths often falls to the bottom of the backlog. The result is coverage gaps that accumulate technical debt and leave your codebase vulnerable to regressions. As AI-powered development tools reshape how we write code, the volume and velocity of changes is accelerating.

Bugs hide in plain sight. A date validator that rejects February 29th on leap years. An edge case that slips through code review. A flaky test that passes locally but fails in CI. These issues erode trust in your codebase and waste hours of debugging time. In the era of AI-assisted development, code is being written faster than ever. But speed creates risk.

For anyone shipping software in regulated industries, the word “control” gets thrown around all over. Compliance frameworks demand controls, auditors verify controls are used, engineering teams implement controls, and there are even Control Owners. But what exactly is a control? And more importantly, how do we design controls that actually serve their intended purpose while enabling rather than hindering delivery velocity?

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.