Operations | Monitoring | ITSM | DevOps | Cloud

Understanding a service outage is easier when you can see what it’s connected to. That’s why we’re introducing Relationships for Service Monitors, one of the most requested features from StatusGator’s hundreds of enterprise IT teams. You can now explore related services directly from the Service Details page by opening the Relationships dropdown.

TL;DR: Modern monitoring platforms face a “workflow problem”: engineers are drowning in telemetry but lack tools that connect detection to resolution, often leading to fragmented, manual incident investigations. Most organizations have mastered data collection but fail at incident response. Engineers waste precious time manually stitching together logs, metrics, and traces across siloed tools. The Solution: Workflow-driven monitoring acts as a guide, not just a dashboard.

Network performance metrics are real-time measurements of how data moves across your network, from speed and capacity to delay, loss, and reliability. Network performance metrics are the diagnostic layer between your infrastructure and your users. They explain why things are slow, dropped, or unreachable by capturing everything from how fast packets travel and how much bandwidth is in use to how often data gets dropped or delayed.

An AI coding agent holds many conversations at once. Not only is the user prompting it, the agent also talks to the IDE, showing diffs and asking before it touches a file. At the same time it talks to tools, pulling a failing build or querying a database. Two open protocols standardize those conversations. This guide compares ACP vs MCP in practical terms: what each protocol does and when each applies. ACP (Agent Client Protocol) connects a code editor to an AI coding agent.

Monitoring load is not always steady. A team might add a new batch of checks or run several ad hoc tests during a rollout. When that happens, your Private Location agents need to pick up more work at once. If there aren’t enough agents available during a burst, checks start piling up in the queue, which can delay or disrupt check execution. But solving this by running a high number of agents around the clock has the opposite problem: most of that capacity sits idle until the next busy period.

A lesson we’ve learned in experimentation at Datadog is how easy it is to fall into interpretative pitfalls even when following rigorous conventions. For example, consider an experimentation program that appears to do everything right on the surface.

This is a guest post by Dan Mindru, a Frontend Developer and Designer who is also the co-host of the Morning Maker Show. Dan is currently developing a number of applications including PageUI, Clobbr, and CronTool. It feels like with every release, we are walking a tightrope. We need to keep our app lightweight, stable, and performant, all the while depending on APIs that can shift at any moment (without warning, too!).

Self-healing ITOps helps restore services faster by combining AI-driven analysis, automation, and recovery validation. Organizations have invested heavily in monitoring, observability, and AIOps. These platforms are effective at identifying issues, but incident resolution is often still a manual process. Engineers still need to investigate alerts, determine the appropriate remediation, and verify that services have recovered.

Split network operations into a planning agent and an executing agent and you have an elegant design on paper. One agent reasons about what should change and validates it. The other carries it out. The elegance is real, and so is the structural consequence: the split puts the entire weight of judgment on the planner. A plan built on a partial view, then executed precisely and at machine speed, is more dangerous than a cautious human who would have hesitated at the part that did not add up.

Liftoff, a mobile advertising company, processes 1.5 trillion bid requests every month. Their platform touches 275 million unique devices daily across 150 geographies. At that scale, the proxy layer is a core part of the business. For years, Liftoff relied on a managed enterprise proxy vendor. It worked, until it didn’t.