Operations | Monitoring | ITSM | DevOps | Cloud

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.

Fat bills from observability vendors and tons of not-so-insightful telemetry data have turned out to be a very common issue today. This often leaves teams having to explain the lack of clear ROI, despite the growing costs. If you’re using OpenTelemetry to record your observability data, there are some practical methods you can apply to keep those costs from piling up.

Figuring out why things break still sucks. We’ve got all the data: metrics, logs, traces, but getting to the actual root cause still takes way too long. Observability tools show us everything, but they don’t really tell us what’s wrong. So why do we even need to automate root cause analysis? First, time. Outages are expensive. And if your system has hundreds or thousands of services, digging through everything by hand just takes way too long.

For DevOps teams managing mission-critical databases, SQL Server observability is a fundamental capability that provides comprehensive insight into database performance and health. Effective observability practices enable teams to identify potential issues before they impact end users and provide the context necessary to resolve problems efficiently. SQL Server observability involves collecting and analyzing metrics, logs, and traces to build a complete picture of database behavior.

I want to know what users are doing in my application. A distributed trace is the best way to show the data flow of one user interaction through my application, but it isn’t sufficient to show the overall user experience.

The HTTP Content-Security-Policy response header is used to control how the browser is allowed to load various content types. It is used to control which URLs, fonts, images, scripts, and more can be loaded onto the page. It’s a great defense against XSS (cross-site scripting), clickjacking, and cross-site vulnerabilities. The header can also specify a URL that will be used to send reports on violations of these properties.

Ever feel like you’re flying blind in your AWS environment? You’re not alone. In the sprawling universe of microservices, containers, and serverless functions, trying to troubleshoot without proper observability is like trying to find a bug in a datacenter… with the lights off… while wearing sunglasses.