Operations | Monitoring | ITSM | DevOps | Cloud

As enterprises accelerate digital transformation, technology performance has become inseparable from business performance. Customer experiences, revenue streams, and operational efficiency increasingly depend on the reliability of complex, distributed systems. In this environment, full-stack observability is no longer a technical aspiration — it is a strategic necessity.

Fresh insights from SHARE Orlando reveal mainframe modernization isn't about replacement—it's evolution. From hybrid architectures to AI-driven automation, enterprises are transforming legacy systems into agile, integrated platforms while preserving core reliability.

On March 2, 2026 at 23:30:24 UTC, we experienced an issue where the Zoom AI scribe was unable to join calls, rendering Zoom meeting transcription unavailable for all users. On March 2, 2026 at 23:30:24 UTC, we experienced an issue where the Zoom AI scribe was unable to join calls, rendering Zoom meeting transcription unavailable for all users. The issue persisted from approximately February 28 through March 5, 2026.

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.