Operations | Monitoring | ITSM | DevOps | Cloud

Grok structures logs. Context engineering connects systems. AI explains behavior. For years, Grok patterns have been the workhorse of the SRE world. Built on regular expressions, Grok helps teams extract structure from unstructured logs. As we explored in "Do You Grok It?", Grok is the key to turning messy log lines into usable fields. It's why our Grok Pattern Reference remains one of our most-visited resources — SREs are hungry for structure.

Most IT teams don’t lack AI policies. They lack policies that survive a Git push. In many organizations, AI governance is a paper tiger. There are comprehensive documents outlining data usage, approved models, and risk management. On an auditor's desk, these policies look complete. But inside the workflow, the reality is different. AI tools are being embedded directly into IDEs, CI pipelines, and internal automation scripts.

AI agents are quickly shifting from experimentation into day-to-day operations. That shift is showing up in the data. McKinsey’s latest State of AI research highlights both broader AI use and the growing focus on “agentic AI,” even as many organizations still struggle to scale safely. For mid-market IT teams, agents can feel like the unlock: automate repetitive workflows, reduce backlog pressure, and deliver more output without expanding headcount.

The future of ITOps automation is better control over what AI agents can see, share, and do. AI automation in ITOps is expected to resolve incidents, reduce operational load, and operate with limited human involvement. Those outcomes depend on systems that can take action, not just surface insight. Agentic AI enables that shift. AI agents can correlate signals across tools, update tickets, trigger remediation, and coordinate workflows without waiting for instruction.

The promise of Artificial Intelligence in Site Reliability Engineering (SRE) is seductive: an autonomous system that never sleeps, instantly detects anomalies, and fixes broken infrastructure while humans focus on high-value work. However, the gap between a demo-ready chatbot and a production-grade Autonomous AI SRE is vast. In complex, noisy environments like Kubernetes, a “naive” implementation of Large Language Models (LLMs) is not just ineffective, it can be dangerous.

Tags have long been the backbone of cloud cost visibility and governance. They help teams understand who owns what, where spend comes from, and how infrastructure maps back to the value the business delivers. However, AI workloads have altered that model, and exposed the limitations of traditional AI tags in the process. In fact, many of the most expensive AI operations don’t run on taggable cloud resources at all.

Since 2016, when Microsoft announced its intention to make Linux a first class citizen in its ecosystem, Canonical and Microsoft have been working hand in hand to make that vision a reality. Ubuntu was among the first distributions to support the preview of SQL Server on Linux. Ubuntu was the first distribution offered in the launch of Windows Subsystem for Linux (WSL), and it remains the default to this day. Ubuntu was also the first Linux distribution to support Azure’s Confidential VMs.

This report presents status data for eight major AI systems during January 2026: Anthropic, Cohere, DeepSeek, Google Gemini, Groq Cloud, OpenAI, Perplexity, Replicate, and xAI. The data includes official incidents reported on provider status pages and detection information from IsDown's monitoring system.

Complex environments don’t fail because teams lack data. They fail when teams can’t trust what the data is telling them. There are too many signals, too little time, and too much risk riding on every decision. That’s the reality Skylar Advisor is built for: delivering guidance teams can verify, so they can act faster without gambling on opaque, black-box answers.