Operations | Monitoring | ITSM | DevOps | Cloud

Artificial intelligence has moved far beyond experimentation. In 2026, AI systems are embedded into customer support workflows, clinical decision support tools, fraud detection engines, and internal copilots across nearly every industry. Adoption is accelerating quickly. According to McKinsey, 23% of organizations are already scaling agentic AI systems, while another 39% are actively experimenting with them. Yet the path to reliable production AI remains uncertain.

By 2026, the GPU shortage isn’t a supply-chain hiccup anymore. It’s baked into the system. Even after pouring billions into CapEx, most enterprises still want 40% more GPU capacity than they actually have. And it’s not because they’re chasing moonshots. Technology companies are training foundation models while serving inference for millions of users on the same clusters. AI labs are juggling fine-tuning, evaluation, and real-time experimentation side by side.

Agentic observability is the instrumentation and correlation needed to explain and control agent behavior across multi-step workflows. Legacy observability focuses on runtime health and service behavior. You monitor metrics like CPU usage, memory, latency, and error rates to confirm that applications and infrastructure are functioning as expected. When a workflow degrades, the proximate cause is often a crash, timeout, permission error, or resource constraint.

This post introduces a six-level maturity model that defines what true autonomy looks like in IT operations, from basic AI chat interfaces to fully coordinated agent ecosystems. ITOps teams have more automation tooling than ever, and yet incident response still depends heavily on human judgment to hold it together. Alerts fire, engineers dig through dashboards, context gets assembled by hand, and someone at the end of the workflow makes the final call.

Rails applications have a specific set of performance challenges that make monitoring genuinely useful rather than just box-checking. ActiveRecord is convenient to use and also convenient to accidentally write N+1 queries with. Memory bloat in long-running processes, particularly when Sidekiq or Action Cable is involved, is a recurring production problem for a lot of teams. Background job performance tends to degrade quietly until it becomes noticeable.

In March 2026, CloudZero’s Ben Austin, Director of Product Marketing, sat down with Ray Rike, Founder and CEO of Benchmarkit, to walk through findings from FinOps in the AI Era: A Critical Recalibration, a joint survey of nearly 500 organizational leaders on how they’re managing or, rather, struggling to manage AI costs.

Oh Dear has always been a desktop-first tool. If you checked your monitors on your phone, you'd get the full desktop layout squeezed into a tiny screen, with lots of horizontal scrolling and tiny tap targets. That's fixed now. Every page in the app works on mobile.

SQL Prompt's February releases focused on compatibility and stability, with a critical fix for SSMS 22.3 users and several features graduating from experimental to fully supported.

In microservices and cloud-native environments, vulnerabilities buried in transitive dependencies or runtime behaviors can go undetected for weeks. During that time, your attack surface keeps expanding and production systems remain exposed. The longer remediation is delayed, the greater the risk of exploitation, compliance failures, and operational disruption.

Increasingly, enterprises are interested in improving their level of control over their data, achieving digital sovereignty, and even building their own sovereign cloud. However, this means moving beyond thinking about just where your data is stored to thinking about the entire data lifecycle. In this blog, we cover the differences between data residency and data sovereignty, how confidential computing works to enhance the security of your data, and can support you in achieving digital sovereignty.