Operations | Monitoring | ITSM | DevOps | Cloud

The Node.js service has just been containerized and deployed with a single fly deploy command across continents. Everything seems to be alright, but then a week later, a user messages you saying the app is slow. You run the fly logs command and scroll through some logs, and find nothing out of the ordinary. The Fly.io dashboard says the app is running and healthy, but something behind the scenes is slowing down the app, and you have no idea what. You don’t even know where to start.

We're big proponents of OpenTelemetery, which has quickly become a new unified standard for delivering metrics, logs, traces, and even profiles. It's an essential component of Alloy, our popular telemetry agent, but we're also aware that some users would prefer to have a more "vanilla" OpenTelemetry experience.

Digital employee experience doesn’t fail because of technology. It fails because of operating models. Many digital workplace leaders invest in visibility tools, dashboards, automation capabilities, and sentiment platforms. And yet, months later, they’re still stuck in reactive mode. Tickets are down slightly. Reporting is better. But the organization hasn’t fundamentally shifted.

Support teams frequently face vague customer reports and incomplete data but need to offer fast resolutions autonomously without escalating to developers. In this article, learn how to equip support engineers with tools to diagnose root causes in minutes, increasing self-sufficient issue resolution. We explore eliminating the ‘Reproduction Tax’ for ‘cannot reproduce’ bugs using runtime context to achieve technical certainty at scale.

Reducing Mean Time to Resolution (MTTR) in production systems requires understanding failure behavior in real time. While AI code agents significantly accelerated software development and deployment, incident resolution has remained constrained by incomplete pre-captured telemetry. AI SRE tools improve signal correlation, but MTTR reduction requires runtime-verified diagnosis that confirms execution behavior directly in production systems.

If you’re running Icinga in a mid-to-large organization, chances are your users and teams are already defined in LDAP or Active Directory. Manually re-creating contacts and contact groups in Icinga Notifications Web is tedious and error-prone, but thankfully, it doesn’t have to be that way. The Icinga Notifications Web REST API gives you everything you need to automate this synchronization. In this post, we’ll walk through how to build a reliable LDAP-to-Icinga sync using the v1 API.

In the world of modern microservices, scale is a necessary challenge. Enterprise service inventories start modestly with a handful of components, only to balloon to hundreds over time. Traditional monitoring approaches cannot support that weight. The more organizations build, the more work they create, often only to keep systems running.

Enterprise operations reached a point where complexity outpaced human interpretation and outgrew the capabilities of generic AI. As environments became more distributed and interdependent, every incident, anomaly, and degradation produced ripple effects across systems that require context, lineage, and reasoning. Yet most AI models were not built for this reality. They were trained for general knowledge tasks, not the deeply connected operational truths that define enterprise performance.