Operations | Monitoring | ITSM | DevOps | Cloud

Synthetic Monitoring is a proactive approach to testing a website or web server to ensure that digital services stay available, responsive, and functional at all times. Instead of waiting for real users to encounter a problem, synthetic monitoring uses automated scripts to imitate user interaction, such as visiting pages, submitting forms, or performing transactions from multiple global locations.

In 2025, DevOps continues to grow and change quickly, helping teams deliver software faster and more securely. But as systems become more complex with microservices, cloud platforms, and AI-driven tools, new challenges arise. Teams now need to balance speed with security, manage too many tools, control rising cloud costs, and still maintain high-quality software. This is where Application Performance Monitoring (APM) becomes essential.

Everyone's talking about how AI is transforming software development. Teams are shipping more code, deploying more frequently, and getting features to market faster than they could a year ago. The productivity gains are real. But we kept hearing a different story from engineering leaders. Yes, velocity is up. But incidents are climbing, resolution times are getting longer, and code review processes are struggling to keep up.

The digital landscape has transformed dramatically, and with it, the demands on our systems have grown exponentially. Traditional monitoring tools struggle to provide sufficient insight into complex, distributed, cloud-native environments. Observability is the answer, moving beyond merely knowing "what" is happening to understanding "why" it's happening, and its impact on user experience and business outcomes.

Active Telemetry is the evolution in how organizations collect, process, and use observability data. In traditional observability, telemetry is passive: systems emit logs, metrics, and traces that are stored and visualized after the fact. This model worked when systems were simpler and changes were predictable. But in today’s world with distributed microservices, Kubernetes, and AI workloads, passive telemetry can’t keep up. Active Telemetry changes that.

Some of the team at Sentry spent this past quarter fixing bugs, more than 800 to be exact. Among them was a complex issue causing transaction spikes in our own Sentry project. Using Sentry Logs, we were able to investigate, trace the root cause, and resolve the problem.

There’s a viral saying folks use about flaky crushes, spouses, and forgetful friends: "if he wanted to, he would." The idea is straightforward: when someone cares, they make the effort. As it turns out, the same principle applies surprisingly well to AI. Systems, like people, have things they "want" to do. Each model has patterns of reasoning and synthesis it performs naturally.

Some parts of a system don’t lend themselves to quick instrumentation changes. You might have a production binary that hasn’t been rebuilt in years, or a stack made of several languages where each team manages telemetry differently. In those situations, getting consistent signals often means touching code you’d rather leave alone or coordinating updates across many services. OpenTelemetry eBPF Instrumentation (OBI) approaches this from the kernel side.

Go’s os.Root feature, introduced in Go 1.24, is designed to improve filesystem security by restricting operations to a specific directory, making it much harder for attackers to exploit path traversal vulnerabilities.