Operations | Monitoring | ITSM | DevOps | Cloud

To monitor Celery beat, give each periodic task its own heartbeat URL and ping it from the worker when the task succeeds, with a task_success signal handler. If beat is down, the message sits in a queue no worker reads, or the task raises, the ping does not arrive and you get an alert. The trap is that beat only publishes messages: its log prints Sending due task on schedule whether or not anything ever runs the task.

Ever wondered what is actually using your network when it suddenly slows down? It could be a device sending too much data, a backup running at the wrong time, or an application taking too long to respond. Without visibility into network traffic, finding the real cause can take hours. That’s where network sniffing tools can help. They let you inspect network traffic, capture packets, and see what is happening between devices, servers, and applications.

A slow query can affect an application before the database team knows what is causing it. A blocked session, failed job, or growing database file can create more problems if no one spots it early. That’s where SQL Server monitoring tools come in. They help you track database performance, find slow queries, monitor waits and locks, and see what changed before a problem occurred.

Your agent has earned trust in shadow mode. Now it runs on its own: an alert fires, the agent starts, investigates and proposes a fix before anyone opens a laptop. Here is what it takes to make that safe, scalable and better every week. This is the third article in a series on taking an AI SRE agent from a weekend experiment to production. Part 1 built a local, read-only agent on a throwaway cluster and refined it against a synthetic eval set.

We built an AI agent on Qovery to investigate Sentry alerts before our team picks them up. Here’s how the workflow runs, what it delivers, and where engineers still need to step in. Rémi is a staff frontend engineer at Qovery. He writes about frontend architecture, developer experience, and building scalable UI systems for platform engineering tools.

If you’ve built your telemetry pipelines around OpenTelemetry Collectors, you’ve already invested in a collector distribution, YAML configuration, and a deployment model that fits your infrastructure. As that deployment grows, managing it means keeping shared configuration consistent, accommodating different workloads, and understanding whether your collectors are healthy. Fleet Management in Grafana Cloud brings those tasks together in one place.

Connect GitHub, let Harness generate your CI pipeline, then build and push a Docker image to DockerHub. No YAML required. Creating your first CI pipeline usually sounds simple until you actually start. You connect your repository, figure out what stages you need, configure the build steps, set up the runtime, add caching, testing, credentials, and then hope the first run actually works.

Deploy your first app with Harness CD in under 15 minutes. Learn how to build, configure, and execute a rolling deployment using a real sample application. TL;DR: Deploying shouldn't feel like crossing your fingers. With Harness CD, you set up your deployment once and get rollback and a full history of what shipped, without writing it all yourself.

Your coding agent can debug production issues now. Yes… Not just “help you debug.” Actually run the investigation… FOR YOU! In this new era of agentic development, speed matters. And in the old days (you know… last week or so) we used to investigate production bugs ourselves. Manually. Like humans. But for a lot of incidents, we don’t need to do all of that anymore.

Scroll LinkedIn for ten minutes and you'd think every engineering organization already runs an autonomous SDLC, with agents writing, reviewing, and shipping code while humans watch. Inside large enterprises, the picture looks different. Coding agents there have to work around sensitive customer data, recurring compliance audits, a larger attack surface, downtime that costs millions, and a CFO who wants to know what last year's token spend bought.