Operations | Monitoring | ITSM | DevOps | Cloud

There’s a new question teams are asking. How can we prevent AI agents from deleting production. When Cursor deleted PocketOS’s entire production database in nine seconds, the agent wasn’t malfunctioning. It had full technical capability, but it was inferring operational authority from static code rather than live environment state. That gap between capability and context is the root cause. This article breaks down exactly how that happens, and what runtime visibility does to stop it.

Over the past decade, cloud computing has evolved from an emerging technology into the foundation of modern digital infrastructure. However, the latest industry research shows that the industry has now crossed a critical threshold. The conversation is no longer about whether to adopt cloud, cloud-native technologies, or AI. Instead, it has shifted toward operational efficiency, economic predictability, and infrastructure at scale.

SQL Prompt's April releases focused on SSMS 22.5 compatibility, a new AI transparency hub, and dark mode readability improvements.

Data Center Infrastructure Management (DCIM) software has matured considerably over the past decade. Deployments are faster, interfaces are easier to use, integrations are deeper, and organizations across industries are seeing real, measurable results. According to Gartner, DCIM software has reached a critical inflection point in the Hype Cycle: the Plateau of Productivity.

In the evening of May 5th, 2026, large parts of the German web briefly went dark. For a few hours, anyone trying to load a.de address through a major DNS resolver got errors instead of websites. Bahn.de, Amazon.de, and Spiegel.de were among the affected. Major brands like Telekom, DHL, and Sparkassen felt it too, along with hosting providers Hetzner, Strato, and Ionos.

meshIQ v12.1 transforms middleware management with petabyte-scale data processing and agentic AI. The new intelligent launchpad, simplified onboarding, and context-aware safeguards move teams from reactive monitoring to proactive, AI-driven operations across the enterprise.

Cloud cost data is most useful when teams can adapt it to their own reporting and planning needs. In addition to viewing cost breakdowns, FinOps teams often need to calculate forecasts, reshape datasets, and present tailored views to finance and leadership teams. In many workflows, those steps happen outside the observability platform. Once the data is exported, it quickly becomes outdated and requires repeated manual updates.