Operations | Monitoring | ITSM | DevOps | Cloud

For decades, telecom network operations have depended on traditional OSS tools – complex, services-heavy platforms that take years to modernize and even longer to deliver measurable business impact. This year at MWC, the leading OSS vendors showcased a variety of new AI extensions for their portfolios and marketed them as the fastest path to autonomous network operations. They are not.

Modern IT teams are experimenting with AI agents. But individual agents, working in isolation are not enough. To truly achieve Agentic IT Operations, organisations need a platform — one that coordinates, governs, and contextualises AI-driven actions across the entire IT landscape. That’s where Interlink Software comes in.

It is 2:13 a.m. A mobile alert fires due to elevated error rates on a customer facing API. You jump in. Metrics show a spike in auth failures. The service was not deployed recently. The code appears unchanged. But something is different.

When everything on the internet comes with an “AI-powered” tag attached and AI fatigue is in full gear, we come to the rescue with a list of tools and services for DevOps and SREs. No AI included. Twelve tools across infrastructure, security, observability, and incident management. Mostly open source. All of them solving specific problems without a chatbot in sight.

Spring is the perfect season to rethink how you manage your data center. Many operators struggle with outdated tools that slow down capacity planning and energy use optimization. Hyperview’s AI-powered, cloud-based data center infrastructure management (DCIM) offers clear real-time insights and agentless asset discovery to cut costs and carbon footprints.

This is a guest post from Phil James. Database teams are already juggling a lot. Monitoring the performance of complex, multi-platform estates takes expertise and focus — and that's before you factor in installing, maintaining, and updating the monitoring tooling itself. That's the tension we've been hearing from database teams for a while. The monitoring solution is supposed to reduce operational burden, yet the infrastructure that runs it adds more.

Bill has built something truly special. For nearly a decade, this has been the place where data center leaders move beyond the basics and tackle real challenges: driving adoption, demonstrating ROI, navigating organizational change, and turning infrastructure data into strategic advantage. Following in his footsteps is both an honor and a responsibility I don’t take lightly. What we’ll cover in this Edition: This isn’t a product demo.

This is a guest post from udara.ratnakumara. In a recent webinar I hosted with my colleague Chris Hawkins, Inside a DBA’s Day: What Really Happens and How to Stay Ahead, we talked through the realities of a typical DBA day and the practical ways teams can stay ahead of issues rather than constantly reacting. For many DBAs, the day doesn’t start with coffee. It starts with an alert. A report is suddenly slow. An application query is timing out.

--- ‍Key Takeaways --- AI can generate code in seconds. It still can’t ship software safely. That gap isn’t about model quality or prompt engineering. It’s about context, and most software organizations don’t have a system that accurately reflects how pipelines, services, environments, policies, and teams actually relate to each other. Without that context, AI doesn’t automate delivery. It amplifies risk.