Operations | Monitoring | ITSM | DevOps | Cloud

AI agents for SREs were supposed to slash mean time to resolution and eliminate alert fatigue. Instead, most teams got expensive, unreliable tools that burn through tokens without delivering insights. But what if the problem isn't the AI models themselves? Recent benchmarking reveals the real bottleneck: context engineering. When we tested our context engineering approach against conventional methods, the results were dramatic: Scroll down for our benchmark results to see the full comparison.

A Kubernetes cluster can generate more than 1,400 metrics out of the box. That’s a lot of numbers to sift through, especially when you’re troubleshooting a production slowdown in the middle of the night. The key is knowing which metrics tell you the most, with the least noise. These are the signals worth paying attention to when you need answers fast.

APM tracing records the complete execution path of a request as it travels through your system, including database queries, external API calls, cache lookups, message queue events, and inter-service requests. Each step is captured with precise start and end timestamps, duration, and context such as service name, operation name, and relevant attributes. This lets you pinpoint where latency or errors originate without piecing together metrics and logs manually.

The OpenTelemetry Gateway (OTel Gateway) is a centralized service that collects, processes, and routes telemetry data—metrics, traces, and logs—across your infrastructure. In a typical setup, each service pushes telemetry directly to an observability backend. While this approach works well for small environments, it becomes increasingly difficult to manage as systems grow.

When your Python app starts slowing down, maybe queries are taking longer, memory keeps creeping up, or API calls are lagging—basic server metrics won’t tell you why. You need to see what’s happening inside the application itself. That’s the role of Application Performance Monitoring (APM). It gives you a breakdown of database queries, external API calls, memory usage, error rates, and more, so you can connect the dots between code and performance.

Database monitoring transforms from a reactive troubleshooting exercise into a proactive optimization strategy when you have the right tools and approaches in place. This blog shares practical ways to choose monitoring solutions, set up observability for different database platforms, and design workflows that scale in modern distributed systems.

That 3 AM alert is never fun. Your heart races as you try to figure out what broke this time, and how fast you can fix it. But with an incident response in place, that panic turns into a calm, step-by-step fix. It helps you handle everything, from a server crash to a security breach, in an organized way. In this guide, I’ll walk you through what exactly an incident response is, why you need it, its key components, and how to build one.