Operations | Monitoring | ITSM | DevOps | Cloud

Temporal lets you orchestrate complex, reliable workflows, but when something breaks or slows down, the built-in dashboards only give you a list of events and some basic filters. You can see what happened and filter by attributes like workflow type or namespace, but you can't drill deeper. There's no way to jump straight from a metric spike to the exact trace or log line you care about.

There’s a problem in engineering culture that we don’t talk about enough: observability is an afterthought. It’s treated as tooling, not thinking. As a checkbox, not a habit. And that mindset gap creates real consequences: longer outages, frustrated teams and massive business costs. Atlassian’s Incident Management for High-Velocity Teams overview cites a 2014 study by Gartner, that the average cost of IT downtime is $5,600 per minute.

The last few months have been wild. Some of the busiest of my life, actually: For context: I’m Canadian, and all of this happened during the continued threats of annexation. All this to say, it’s been rough. I anticipated this would be a challenging time and that I would be exhausted. So, the plan became: do all the demanding things, take my sabbatical in May, and use April as an ‘in-between’ period with a bit less pressure.

For today’s enterprises, ensuring optimized network connectivity and robust network security represent key imperatives. Given that, it makes sense that there’s rapidly growing use of solutions like secure access service edge (SASE). In fact, the SASE market is expected to grow to $5.9 billion by 2028. SASE delivers converged network and security capabilities. SASE is a cloud-based offering that is primarily delivered on an as-a-service basis.

Message queues quietly power a lot of what happens behind the scenes in distributed systems. RabbitMQ is no exception—when it’s working, you don’t notice it. But when it’s not, things break in ways that are hard to trace. This guide walks through what you need to monitor in RabbitMQ, how to set it up, and how to troubleshoot when things go wrong—so you’re not stuck guessing when messages go missing.

There are many vendors, Honeycomb included, where actions on the application can emit a web request that goes to another service for coordination or tracking purposes. Many vendors have pre-built integrations, but some have a fallback that says “Custom Webhook” or similar. If you’re looking to create a full picture of your request flow, you would want these other services to show up in your trace waterfall.

Can your AI tools really work together, or are they still stuck in silos? With Google’s new Agent-to-Agent (A2A) protocol, the days of isolated AI agents are numbered. This emerging standard lets specialized agents communicate, delegate, and collaborate—unlocking a new era of modular, scalable AI systems. Here’s how A2A could transform your workflows, and why making it observable is just as important as making it possible.

Imagine this: Your IT team has invested in cutting-edge observability tools to keep systems running smoothly. But does that imply you are following observability best practices? As your business grows, so does the flood of logs, traces, and metrics—along with a skyrocketing cloud bill. What started as a way to gain better visibility is now a major expense, and suddenly, you’re asking: Are we paying too much for too little value? This challenge is becoming all too common.