Systems | Development | Analytics | API | Testing

As AI-powered agents begin to play a more active role in quality engineering, regulated industries especially in BFSI, healthcare, and government face a critical question: How do you scale agentic QA without losing control, visibility, or auditability? This blog explores how to make agent-augmented QA compliant, explainable, and trustworthy, not just in spirit, but in process, tooling, and evidence. We’re not talking about testing AI systems.

Drone technology — also known as unmanned aerial vehicle (UAV) and unmanned aircraft system (UAS) technology — is expanding rapidly across commercial, industrial, and defense sectors. As these systems become more complex, they increasingly rely on sophisticated software for aerial embedded systems.

2025 will be remembered as the year AI wrote poetry, passed medical exams, and stumbled with spectacular blunders. For every impressive breakthrough, there was an equally impressive facepalm.

Some topics are just too trendy to overlook, and AI in testing is definitely one of them. A few weeks ago, we shared a blog post introducing the integration between an MCP server and OctoPerf, highlighting the many benefits it brings. To illustrate this in action, we recently hosted a webinar led by Thomas Pitteman, performance testing expert at Adeo and OctoPerf power user.

Replaying traffic is a core tool in your toolbox when you need to reproduce a tricky bug or validate how your app behaves. Traffic replay is especially valuable for testing complex software applications that rely on APIs and microservices, where integration and functionality must be thoroughly validated.

A few years ago I was on call during the Super Bowl. At the time I was working for an observability vendor and one of our customers had an outage caused by a surge in user traffic. But our monitoring system didn’t have enough data to know what went wrong and I sat on a call for 2 hours painfully listening to them spinning up more servers and trying to catch up with the user load.