Operations | Monitoring | ITSM | DevOps | Cloud

In the 2026 regulatory landscape, manual audits are a liability. This guide explores using GitOps to generate DORA-compliant audit trails through IaC, drift detection, and automated segregation of duties. Discover how the Qovery management layer turns compliance into an architectural output, reducing manual overhead for CTOs and Senior Engineers.

Inside hospitals and health systems, the performance of clinical technology underpins nearly every care workflow and directly influences the timeliness and quality of patient care. Electronic health records sit at the center of admissions, discharge, imaging, lab coordination, and prescribing, so even minor technology friction can become a patient safety and operational risk. At scale, reliability becomes a prerequisite for consistent care.

I’ve been an engineering leader for over a decade, and I’ve spent most of those years in private Slack groups with other engineering leaders, comparing strategies and kvetching about Kubernetes. Of the hundreds of threads I’ve taken part in, the one that got the most engagement the fastest was a recent one around AI adoption. “Where are you on this continuum?”, it read. “A. You don’t really care how people use AI; B. You push people to use AI; or C.

In the modern DevSecOps world, CISOs are constantly looking for signals in the noise, and the outputs of security scanners often carry a lot of weight. A security scan that returns a “zero CVE” report often unlocks promotion to production; a single red flag can block a release. This binary view of security has birthed two diametrically opposed philosophies. On one side, we have the long-term support (LTS) approach: stay on a battle-tested version and backport specific security fixes.

It’s 2:47 a.m. A P1 alert fires. The on-call engineer opens ilert, sees the AI has already investigated, and is presented with three remediation options. What happens next is the moment we obsessed over. ‍ Most AI tooling at that moment hands the engineer a numbered list in a chat window and waits. The engineer reads, selects mentally, types a reply, and the agent resumes.

AI coding agents like Claude Code and Codex are becoming a real part of developer workflows. They don't just write code, they call APIs, interpret responses, and take action based on what they find. That means the quality of your API responses directly affects how useful an agent can be. We've shipped a series of improvements to the Oh Dear API with this in mind. Every change helps humans too, but we specifically optimized for how agents consume and reason about data.