Operations | Monitoring | ITSM | DevOps | Cloud

One of the most frustrating challenges I faced as a network engineer was troubleshooting problems that had already disappeared. A user would call and report that an application was performing terribly. A site would become unusable for several minutes. Voice calls would break up. File transfers would suddenly slow to a crawl. Then, by the time I started investigating, everything would be working normally again.

DNS Check watches its own infrastructure with several layers of monitoring, including DNS Check itself and third-party uptime monitoring services. One of those vendors polls a private health endpoint on our servers every five minutes, from probes around the world. That endpoint runs a set of internal checks (disk space, mail delivery, background job queues, DNS resolvers, and more) and returns an error if any of them fail. If it errors or stops responding, the vendor alerts us.

Most people investing in software factories fail to close the loop. Agents write, review, test and ship code, but then the factory loop ends. What happens after deploy? Nobody knows.

A customer messages support: checkout feels slow today. You open Scout, find the endpoint, open a trace, spot the query, copy it into Claude, and ask what’s wrong with it. Each step is quick, but together they add up to a lot of tab switching to answer one question. Scout Monitoring is now available in Claude’s connector directory. Connect it once and Claude can look at your production data directly.

TL;DR Logz.io AI Observability provides end-to-end tracing for AI agents and LLM applications. Built on OpenTelemetry, it lets you trace every request from prompt to backend service, visualize multi-step agent execution, isolate root causes across models and infrastructure, and track token usage per step, all alongside your existing logs, metrics, and traces.

A checkout request leaves the browser and touches seven services before anything comes back. Seven dashboards light up. Every one of them reports a healthy number for its own service, and every one of them is telling the truth about a different slice of the work. Nobody measured the request. That gap is what pushed tracing in beside metrics and logs as a core signal of observability. In this blog, you will: By the end you will open a trace and say where the time went.

Why does a checkout or login take several seconds when CPU and memory look normal on every server? In microservices, one click can travel through a long chain of components before the user sees a response. Any one of them can hold the request up, and server graphs won't tell you which. Logs show what each service recorded. Metrics give you averages. What's missing is the path one request took and the place it lost time, and Jaeger tracing records exactly that.