Operations | Monitoring | ITSM | DevOps | Cloud

There usually isn’t a hard and fast rule about who should be on-call. Teams often look for criteria like seniority, experience, or expertise. While those factors certainly help, they might matter less than you think. It is often more useful to look at whether your processes are ready. When incident responses rely on memory and intuition rather than documentation, even experienced engineers can struggle. They might handle things through internal knowledge that isn’t available to everyone else.

An on-call responder is the first line of defence when something breaks. They assess the situation and take appropriate action. This guide walks you through what that actually looks like. You’ll see how on-call responders think through an incident and figure out what needs to be done.

Most engineering leaders have a very known and very annoying "normal error." It's the log entry or deployment glitch that has been around so long that it is simply accepted as part of the status quo. Jeff Schnitter, a Solution Architect at Cortex, describes this as a form of organizational Stockholm syndrome. This mindset is unsustainable for several reasons.

Looking for Rancher alternatives? Compare the top 10 Kubernetes Management Platforms for 2026. From Qovery to OpenShift, find the best tool to scale multi-cluster operations and reduce TCO.

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?

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.

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.