Operations | Monitoring | ITSM | DevOps | Cloud

Explore how AI-driven automation is reshaping IT service management for a faster and smarter enterprise in 2026.

For many software teams, documentation is written after features are built and design decisions have already been made. When that happens, questions about how a feature is understood or used often don’t surface until much later. A little over one year ago, our team began to recognize this pattern in our own work. Features generally functioned as intended but were difficult to use or explain. Documentation lagged behind releases.

Healthcare is shifting fast, and artificial intelligence is no longer a future concept sitting in research labs or pilot programs. It’s already embedded in clinical workflows, operational systems, and patient interactions, often in ways that feel subtle, uneven, and sometimes uncomfortable.

With modern tooling and agentic coding assistants, straightforward bugs are almost a relief. If a test can catch it, or a user can reproduce it, chances are you can squash it quickly. The harder category — and the one worth writing about — are the bugs where everything looks correct. Your code runs, no exceptions are thrown, your debug statements confirm the right functions fire at the right times, and yet nothing works.

The error monitoring scene has changed a ton over the past few years. We've gone from basic exception tracking to fully integrated platforms that correlate errors with performance metrics and logs. We’ve even got AI-powered debugging! But in the midst of the AI explosion, some things remain unchanged and most teams are still drowning in data with little actionability.

We’ve all had that moment where it seems like you've solved your company's biggest engineering challenges after a weekend of hacking something together. Your prototype is so good, you feel, that the obvious next steps are to build a slide deck, rally the team around your work, and prepare the ticker tape parade for your hero's welcome. Jeff Schnitter, a Solution Architect at Cortex, knows this roller coaster of experience all too well after his time at Workday.

OpenTelemetry almost always works beautifully in staging, demos, and videos. You enable auto-instrumentation, spans appear, metrics flow, the collector starts, and dashboards light up. Everything looks clean and predictable. However, production has a way of humbling even the most carefully prepared setups. When real traffic hits, and it always spikes sooner or later, you start seeing dropped spans.

Since 1999, when NVIDIA invented the GPU (graphics processing unit), the demand has “skyrocketed”. At CES 2026, CEO Jensen Huang announced their latest GPU, named after Vera Rubin. This follows on from the announcement of their Blackwell lineup only two years ago. Through this blog, we’ll explore what the industry knows about the Vera Rubin so far. Plus, we will take a look at some specs in comparison to the NVIDIA B200 from the Blackwell lineup.

Most CEOs can recite their quarterly benchmarks and revenue down to the decimal point, but ask them about their organization's cyber risk exposure, and the answers become more vague. It's not that today’s CEOs don’t care about security — cybersecurity ranks among the top concerns for boards and executive teams. The problem runs deeper: a fundamental breakdown in how security risks are explained to business leaders that overlooks the impacts on their business outcomes.

Infrastructure as code (IaC) is the act of writing infrastructure configurations as code so they can be understood, repeated, and enforced with less manual effort. IaC is also a powerful way to convert institutional knowledge into technical knowledge. It’s a far-reaching and essential part of managing infrastructure at scale, with benefits that have expanded to platform engineering, security and compliance, network administration, and so much more.