Operations | Monitoring | ITSM | DevOps | Cloud

ServiceNow is one of those platforms that looks simple from the outside but turns into a labyrinth the moment an organization starts customizing it. What begins as a service catalog or an HR portal quickly evolves into a tangle of custom tables, UI policies, business rules, Flow Designer actions, and scripted REST endpoints. None of this is bad. In fact, it’s the whole reason companies choose ServiceNow: you can build anything.

To have a fast and reliable experience digitally you would need to do more than resolving issues. This is why people prefer synthetic monitoring which simulates real user actions with regular intervals. Using this method, businesses can detect performance shortcomings and any technical issues. From testing website load to full flow checkout, everything can be tested before users face any issues.

Modern IT teams know the story by heart: uptime dashboards look green, the public website is fast, yet somewhere inside the corporate network, the finance team can’t submit purchase orders and the factory floor’s ERP terminals are frozen. What broke isn’t the internet—it’s the internal backbone. These internal systems—SAP, Oracle, Microsoft Dynamics, homegrown ERPs, HR and payroll platforms—keep the business running.

Synthetic Monitoring is a proactive approach to testing a website or web server to ensure that digital services stay available, responsive, and functional at all times. Instead of waiting for real users to encounter a problem, synthetic monitoring uses automated scripts to imitate user interaction, such as visiting pages, submitting forms, or performing transactions from multiple global locations.

When an application slows down or fails, pinpointing the cause isn’t always simple. Is it a backend regression, a misbehaving API, or a bottleneck somewhere deep in the network? Without full visibility, teams waste precious time troubleshooting across disconnected tools and layers. Datadog Synthetic Monitoring now supports Network Path to help you proactively identify whether user-facing issues stem from your code or from the underlying network.

GraphQL isn’t just another API protocol—it’s a new layer of abstraction. It collapsed dozens of REST endpoints into one flexible interface where clients decide what data to fetch and how deep to go. That freedom is a gift for front-end teams and a headache for anyone tasked with reliability. Traditional monitoring doesn’t work here. A REST endpoint can be pinged for uptime.

Testing ecosystems contain massive amounts of data, including outlined test scenarios, prerequisite configurations, and the tests themselves. As a result, these ecosystems are prone to data sprawl. This makes it difficult to prevent configuration drift and quickly spin up new tests, especially at the frequency needed to support a fast-growing application. Teams can handle these challenges by treating their tests as part of their application infrastructure.

We've been running Playwright in production since the beginning. Today, we're going all in. When we first launched Browser Checks with Playwright support, we proved something critical: the most popular test automation framework since Selenium isn't just for testing—it's the foundation of modern production monitoring. But that was just the beginning. Today, we're announcing Playwright Check Suites—our bet on the future of monitoring and the most significant evolution in Checkly's history.

A 200 OK doesn't always mean everything is okay. You've probably seen it: your health check endpoint returns success, but your users are staring at an error page. Maybe the database connection pool is exhausted, or a critical downstream service is timing out, but your API dutifully returns 200 because technically it responded. This is the reality of monitoring HTTP endpoints in production—status codes alone don't tell the whole story.

A synthetic test is only useful if you can trust what it’s telling you. When one fails, the reason may not be obvious. Was the application updated? Did the test change? Or both? As more people contribute and refine the same test, it becomes harder to understand what changed or restore a working version. Without clear visibility into those updates, teams can spend more time tracking down the cause of a failure than resolving it.