Operations | Monitoring | ITSM | DevOps | Cloud

Cloud resilience often feels like an uphill battle. When you’re overseeing hundreds of applications across different providers, identifying potential failure points manually is nearly impossible. You’re left trying to find the needle in a haystack—a needle that could take down your entire application at any moment. To truly protect your uptime, you have to break the cycle of reactive troubleshooting.

New Nexthink Infinity innovations empower IT to govern AI, automate remediation, and troubleshoot digital workplace issues faster, enabling faster action, smoother experiences, and stronger business outcomes. The future of IT requires more than actioning tickets. Modern IT teams are expected to safely enable AI, modernize infrastructure, improve employee productivity, and drive business outcomes in increasingly complex environments.

For IT teams supporting VDI environments, the hardest part of a support ticket is rarely the fix itself – it's figuring out where the problem actually lives. And in most cases, first-level support engineers don’t have access to both the present and historic VDI-specific insights needed to triage the problem, so these VDI tickets are quickly escalated to the VDI team.

This is the third post in our DRIVE Deep Dive series. We've covered Delivery and Reliability so far; this post takes on Initiatives. For the complete model, download the full DRIVE framework.

Europe is home to some of the world's most privacy-focused cloud storage providers, offering secure alternatives to services operated by large US technology companies. Whether you're looking to protect personal files, store sensitive business documents, or keep your data under European jurisdiction, choosing the right provider can make a significant difference to your online privacy and the security of your personal information.

AI agents are part of the modern development workflow. They write code, review pull requests, generate tests, call tools, interact with MCP servers, and help developers move faster. But behind every useful agent, there is something just as important as the model itself: the context that tells the agent how to behave. That context can include skills, prompts, instructions, hooks, commands, scripts, references, and MCP server definitions. In a small project, managing these primitives is pretty simple.

Edwin AI and Red Hat Ansible Automation Platform help ITOps teams move from correlated alerts and root cause analysis to governed, auditable remediation. When an outage starts, the first alert is only the first artifact. The harder work follows: grouping related signals, separating symptoms from cause, identifying the affected service, and deciding whether the next action is safe to run.

The original case for multicloud still holds — but the conditions around it have shifted significantly. Here's what you should be asking before your next cloud decision. Most organizations didn’t set out to have a multicloud strategy. They just accumulated one – a mix of providers, tools, and integrations built up over time as individual workload decisions compounded into something that now requires a strategy of its own.

A new report from LeadDev and Harness makes one thing clear: AI coding tools have fundamentally changed how much code organizations are producing. What has not changed nearly fast enough is how that code gets released. The State of AI-Driven Software Releases 2026 report, based on responses from 500424 engineers across industries and company sizes, puts real numbers behind a problem that engineering leaders have been feeling for a while. AI is accelerating the code creation side of the SDLC.