Systems | Development | Analytics | API | Testing

AI-powered automated test script generation is transforming how software teams approach quality assurance. By analyzing real user behavior, code changes, and system logs, these tools reduce the time and effort needed to create and maintain tests. This shift from manual scripting to AI-generated scripts helps teams keep test coverage in sync with rapid release cycles.

By now everyone is aware of the limitations inherent in generative AI and the AI agents that use it to complete their tasks, and the challenges involved in getting them enterprise quality. If you are planning to incorporate AI agents into your enterprise IT architecture, how are you planning to validate their quality and accuracy?

In Under the Hood with Go TLS and eBPF, I left socket tracking as an exercise for later. The example used bpf_get_current_pid_tgid() and explicitly excluded concurrent TLS operations. Capturing plaintext was enough for that post. With rustls, later arrived: I could read the HTTP payload perfectly and still attach it to the wrong TCP connection. That’s a frustratingly convincing failure. The request looks right. The response looks right. The application works.

Apple's iOS 27 release marks another significant shift in the mobile landscape. Beyond new features for iPhone users, it signals a familiar pressure point for QA and development teams: the clock starts ticking the moment the update goes live, and your apps need to be ready. Here's why that matters: nearly 80% of iPhone users adopt the newest iOS version almost immediately. That high adoption rate leaves little room for error. If your app breaks on the iOS 27 release, your customers notice fast.

Every iOS team is about to hit a testing cycle they can't skip. And waiting until production is the expensive way to find out.

Continuous validation is redefining performance assurance, moving beyond traditional, one-off testing. By embedding ongoing validation into CI/CD pipelines, teams can identify and address regressions as soon as they arise. This approach reduces production incidents and enables faster, more confident releases.

Performance testing for serverless is an ongoing process. The most reliable results come from repeated, targeted experiments that reflect real usage. By focusing on cold starts, concurrency, and external dependencies – and by leveraging advanced cloud testing platforms – you can deliver consistent, reliable performance as serverless adoption grows.

OpenTelemetry graduated from the CNCF in May 2026 as, in the foundation’s own words, the de facto observability standard. The JavaScript API package alone did 1.36 billion downloads in twelve months. That kind of win has a side effect nobody plans for. Once a wire format is everywhere, has a receiver for every source, a transform language, and an agent your platform team already operates, people start putting things on it that have nothing to do with knowing whether a service is healthy.