Operations | Monitoring | ITSM | DevOps | Cloud

The 2025 DORA State of AI-Assisted Software Development Report delivers a critical insight for technology leaders: AI is fundamentally an amplifier, not a solution. It magnifies the strengths of high-performing organizations with robust observability while exposing the dysfunctions of struggling ones. For organizations that have rushed to adopt AI coding assistants all while expecting immediate productivity gains, this finding demands a strategic pivot.

Your checkout flow starts in an AWS Lambda function, calls a payment service running on EKS, then triggers notifications through Azure Functions. Three different compute platforms, two cloud providers, one distributed trace that you can't see. Cloud providers want you to use their native monitoring tools. AWS pushes X-Ray and CloudWatch. Azure promotes Application Insights and Azure Monitor. These tools work well within their ecosystems but lock you into vendor-specific implementations.

Your production checkout flow just started returning 500 errors. Six microservices handle checkout. Logs show errors in three of them. Which service broke? Which error happened first? What caused the cascade? Traditional debugging doesn't work. You can't attach a debugger to production. Searching logs across six services gives thousands of lines with no obvious connection. By the time you correlate timestamps and trace IDs manually, customers have abandoned their carts.

IT leaders know the drill. An alert storm rolls in and the tickets pile up. Your team scrambles to piece together root causes before service degradation kicks in. But the firefighting rages on, even when you have enough dashboards, monitoring, and alerts to light up a Christmas tree. Enterprise leaders need to quit burning budget on shiny dashboards that look good in the boardroom but do nothing to stop outages in the real world.

The world of software development has fundamentally changed. We've moved from monthly releases to continuous delivery measured in minutes, and the rise of AI means velocity is no longer just a goal—it's a requirement for survival. But this relentless speed has exposed a critical flaw in how we approach observability. The industry relies on a "store first, ask questions later" model where you collect every log, metric, and trace, and then hope to find the root cause when something breaks.

OpenMetrics and OpenTelemetry are popular standards for instrumenting cloud-native applications. Both projects are part of the Cloud Native Computing Foundation (CNCF) and aim to simplify how we generate, collect and monitor services in a modern cloud-native distributed application environment. Let's have a look at how both the standards are aiming to help solve the observability conundrum.

Site Reliability Engineering has emerged as one of the most sought-after careers in tech, combining software engineering expertise with operational excellence. SRE engineers ensure that critical systems remain reliable, scalable, and performant while enabling rapid feature development. With the global SRE job market projected to grow by over 25% in 2025, skilled professionals in this field command competitive salaries and enjoy diverse career opportunities across industries.