Operations | Monitoring | ITSM | DevOps | Cloud

Two of our most powerful checks just got a lot easier to set up. Application Health and Scheduled task monitoring used to mean wiring up an endpoint or pings by hand. Now an AI prompt does that part for you.

As AI coding assistants dramatically inflate PR counts, commit frequency, and lines of code, the limitations of individual output metrics have never been more apparent. A developer can now produce significantly more lines per session, but higher volume doesn’t guarantee that the code is stable, maintainable, or successfully running in production. GitClear analyzed over 200 million lines of code and found that code churn nearly doubled following widespread AI adoption.

Hybrid cloud has reshaped the way enterprises build, run, and troubleshoot digital services. Applications now stretch across on-premises infrastructure, cloud platforms, regional services, interconnects, and distributed dependencies that change constantly. Operational complexity has expanded with that footprint, yet many observability practices still reflect assumptions from an earlier era of simpler architectures and clearer boundaries. That gap shows up fast during an incident.

AppSignal now supports Render's Metrics Stream. Configure it once in your Render workspace and Render forwards OpenTelemetry metrics to the AppSignal Collector. From there, the metrics show up in your AppSignal app as host metrics and automated dashboards. You only have to set up the stream once per workspace.

Industrial teams keep telling us the same thing: the data is there, but the stack to act on it isn’t. PLCs, CNCs, SCADA systems, vibration sensors, and quality stations all generate high-frequency telemetry that gets stranded in proprietary historians or stitched together with point integrations nobody wants to own. By the time anyone looks at it, the moment to act has passed.

CIOs are under pressure from every direction. Budgets remain tight, geopolitical uncertainty is forcing organizations to rethink resilience, and workforce expectations continue to evolve. At the same time, AI is accelerating a broader shift across enterprise IT – changing not only how organizations operate, but also the skills and roles they will increasingly depend on. The question is not whether AI will reshape IT teams, but how quickly organizations can adapt to these new ways of working.

The infrastructure industry spent two decades chasing a single pane of glass. The future looks different: domain-expert AI platforms that reason deeply within their own data, connected through tool chaining when problems cross boundaries.

Performance testing is critical to ensure your applications stay reliable under load, but writing the scripts themselves often feels like a chore. Most engineers already know the scenario they want to test; the hard part is translating that intent into a working performance test. Even experienced developers who use k6 can lose time looking up syntax, configuring load stages and thresholds, or debugging boilerplate code before they can run a meaningful test.

Cloud and SaaS spending continues to grow across teams, services, and providers, changing too quickly for retrospective cost management workflows to keep up. Finance and engineering leaders often rely on last month’s reports or manually maintained spreadsheets, which don’t reflect current usage. As a result, teams lack context on how spend is trending and often discover budget overruns only after they’ve occurred.