Operations | Monitoring | ITSM | DevOps | Cloud

Picture this: You’ve spent hours perfecting your React component. The animations are smooth, the responsive design works flawlessly, and you’re ready to test the user flow. You click “Submit” and… nothing happens. Or worse, you get a cryptic CORS error. The problem? Your backend isn’t running. Again.

In the early hours of December 12, 2025, Trello experienced a disruption that affected teams around the world. Users began reporting that boards would not load, workspaces were inaccessible, and error messages appeared without warning. For a period of time, Trello’s official status page continued to show normal operations, even as real world usage indicated otherwise.

Customer logs data is always messy. Being (and building!) an observability platform, we get to see all the beautiful, creative ways it can be messy, every single day. And yet, our customers expect, quite fairly, I might add, perfect query results and peak performance. Info SigNoz is an open-source observability platform that can be your one-stop solution for logs, metrics and traces.

OpenTelemetry backends provide storage, analysis, and visualization for telemetry data (traces, metrics, logs). This guide lists available OpenTelemetry-compliant backend options, categorized by use case: APM platforms, storage backends, visualization tools, and distributed tracing systems. For detailed comparison, see OpenTelemetry Backend Comparison.

If there was ever a year for AI observability, it was 2025. Vendors released assistants to cover a variety of use cases. Coralogix released the first agent (distinct from assistants!), Olly, an autonomous, multi-agent observability platform. The direction of travel is clear, but many vendors and users are about to run into some significant problems with their data layer.

2025 was a stark reminder of why reliability is so critical in the tech sector. The year wrapped up with multiple high-profile outages across several major cloud providers, costing companies around the world billions of dollars. Building resilient systems has never been more of a priority, especially as we move into the era of agentic AI.

Imagine this. A product team rolls out a change to improve reliability. The deployment succeeds. Traffic grows. Weeks later, cloud costs increase, and the finance team asks what changed. No one can point to a single decision or owner. This situation is common in cloud environments. Infrastructure scales automatically, and costs are shaped by technical choices made across engineering, data, and product teams. Most organizations review cloud spending after it has already occurred. Ownership is unclear.

When something breaks in a modern digital service, customers feel it instantly. Pages stall, requests time out, and carts are abandoned, while frustration grows long before a root cause is identified. What the world never sees is the engineering effort required to keep these systems healthy in the first place. Site Reliability Engineers (SREs) carry that responsibility every day.

As enterprises face increasing pressure to manage vast and complex IT environments, the demand for faster and more efficient IT management is rising. Traditional operating methods are proving insufficient, making the adoption of Agentic AI essential for organizations aiming to achieve truly autonomous IT operations. This innovative technology enhances decision-making and enables businesses to remain agile in a rapidly evolving digital landscape.

Connecting day-to-day development work to real user outcomes can be challenging. As a result, engineers and product teams often struggle to effectively prioritize projects together. While the goal of improving user experience (UX) is the same, each team relies heavily on different—and often siloed—forms of monitoring to understand their app, creating a disconnect in metrics and visualizations that can be hard to communicate.