Operations | Monitoring | ITSM | DevOps | Cloud

In the dynamic world of IT, change is the only constant. What used to be manual, repetitive, and slow has now become quick, automated workflows that run faster than ever before. IT process automation, previously known as “runbook automation,” has evolved significantly. It started as a simple concept and has become essential in modern IT operations. But how did this transformation happen, and why is real-time automation now imperative for businesses to thrive?

2025 will continue the theme of powerful business and technological transformation under a never-ending barrage of cyber threats that are spawning faster and evolving in more complex ways than ever. To ward off an increasingly sophisticated community of threat actors, we’ll need to innovate faster than the speed of the threat landscape — but how?

Implicit list iteration in CFEngine is quite a unique and novel feature. Today we look at a practice example showing how lists can improve the readability and maintainability of your policy. A novel feature in CFEngine is how a list variable is iterated when referenced as a scalar ($(variable)). Let’s take a look at a contrived example.

In recent years, hyper-automation has emerged as a buzzword in the world of business process optimization. Promising a fully automated, intelligent enterprise, hyper-automation leverages technologies like artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to streamline operations and enhance efficiency. But while it often sits at the center of these conversations, RPA’s limitations have become increasingly apparent.

I recently had a conversation with one of our customers at AWS re:Invent that made me rethink the true value of automation. This customer told me, “You’ve automated so much for us that we almost forget about Qovery. Nobody even asks how to do things anymore.” That statement stuck with me.

High-performance computing (HPC) configuration has become paramount for AI workloads. Companies investing in AI workloads often find themselves with predictable problems: Lots of nodes that are hard to integrate and harder to manage together, under-utilized resources, deployment struggles, inconsistent configurations, and drift.

In today’s digital-first world, organizations are constantly pressured to deliver faster, more reliable, and secure network services. Yet, with a staggering 65% of enterprise network activities still performed manually, the journey toward network automation is more critical than ever.

In today’s fast-paced digital environment, Network Operations Centers (NOCs) are critical for maintaining uptime, optimizing performance, and ensuring seamless communication. For NOCs to excel, tracking the right Key Performance Indicators (KPIs) is essential. These metrics guide decision-making, highlight inefficiencies, and keep networks resilient.

As the demands of modern software development continue to grow, so too does the complexity of DevOps. To keep pace, many organizations are turning to AI-powered assistants that help manage infrastructures, streamline pipelines, and automate repetitive tasks. These assistants are known as copilots, and they promise to enhance productivity while reducing operational overhead. But not all of them are created equal.