Operations | Monitoring | ITSM | DevOps | Cloud

Load balancers are the primary entry points to distributed applications. By strategically directing the flow of incoming web traffic to specific endpoints, load balancers help optimize throughput and ensure the horizontal scalability of applications. In modern systems, load balancers often do more than their name suggests: Beyond basic load distribution, they analyze requests and route traffic based on a wide range of variables, such as client identity.

Modern organizations often operate from multiple locations. From retail stores to global enterprises, many companies rely on distributed wired and wireless networks to keep business-critical applications online. Aruba Central provides a centralized, cloud-based platform for managing that infrastructure at scale.

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.

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.

Coroot is an open source observability tool that uses eBPF to collect telemetry directly from applications and infrastructure. One of the things it does is capture L7 traffic from TLS connections without any code changes, by hooking into TLS libraries and syscalls. Works great for OpenSSL. Works for Go. Then rustls enters the picture and everything stops being obvious. With OpenSSL, everything is nicely wrapped: From eBPF’s point of view this is perfect: Everything happens inside one call.

This blog is based on the webinar, “AI panel: The next wave of AI technology”. You can watch the full recording by clicking here! The pace of AI innovation is reshaping research, business, and everyday life. However, as breakthroughs in Large Language Models (LLMs) and high-performance computing accelerate, they bring new technical challenges around scale, efficiency, and reliability.

For years, our annual State of Digital Operations report has been the industry benchmark for understanding how organizations manage incidents, build resilience, and evolve their operational practices. Each year, we survey hundreds of business and operations leaders worldwide to capture the challenges, priorities, and emerging practices shaping digital operations.

Teams continuously deploy programmable telemetry pipelines to production, without having access to a dry-run mode. At the same time, most organizations lack staging environments that resemble production – especially with regards to observability and other platform-level services.

In the shift left era where it feels like we’re pushing everything as far to the start of the SDLC as we can, it may seem counterintuitive to shift anything right. That is, however, exactly what I suggest when it comes to generating metrics. How far you go to the right of the SDLC is a much more nuanced question and is dependent on a lot of factors, and on what metrics you’re talking about.

Artificial intelligence recently took a major leap from “saying” to “doing.” Instead of simple back-and-forth chats, we’re now allowing automated AI processes to take action on our behalf—from responding to emails to building and deploying complete applications. This shift from “assistant” to “actor” can make applications more capable, but it also creates additional failure modes.