Agentic testing is one of those phrases that has started appearing everywhere. There’s an underlying assumption that everyone already knows what agentic workflows are, but many don’t. So what does it mean? And more importantly, does it actually matter right now?
The way most people have been using AI for testing is handy for a quick script stub or some test data ideas, but it’s a long way from an industrialised solution. For most teams, “AI testing” still sounds like a future promise rather than a secure, business-ready solution embedded in how their organisation works. It feels like something happening on the fringes, or more realistically, on a tester’s personal chatbot.
Test data has always been one of the slowest, least glamorous parts of software testing. It is rarely strategic work, but it holds everything up. No matter how good your test plan is, weak data can make the whole exercise unreliable.
If you’re looking for a new performance tool or new to performance testing, it can be a tough subject to get your head around. I’ve been involved in the industry for 3 decades, and during that time, it has evolved massively. Increasingly, I talk to people at companies who’ve never
JMeter often becomes the default because it looks free. There is no license fee, and it appears flexible enough to do almost anything. But JMeter’s ease is often a myth. In reality, it is rarely the most sensible or low-cost choice.
In the last main Testing Times edition (April fools day), I argued, quite ludicrously, that testing is a waste of time. That it slows releases, costs money, and ruins everyone’s fun. Judging by the comments, a few readers took it a bit too literally. So let’s be serious for a minute.
DevOps Aviator brings generative AI into software delivery to help test teams move sooner, reduce manual effort, and get answers faster. It is part of the broader Aviator suite: a set of AI capabilities embedded across OpenText products.
Let’s be honest, testing is what teams do when they don’t trust their developers. It’s a tax on speed, a relic from waterfall days, and a crutch for people afraid to ship. It just slows down releases, kills creativity, and wastes budget that could be better spent on another sprint.
You walk into a room where people are talking about the exact problems you wrestle with: tricky deployments, clunky processes, and how to test faster. Sometimes, the right conversation with the right person is enough to unlock a solution or a possibility you hadn’t even considered.
With 26.1, OpenText is giving you something concrete: Python‑based automation, AI‑assisted verification, and cloud labs that fit into your existing CI/CD. This turns functional testing from a separate QA activity into a shared capability that developers, SDETs, and testers can all contribute to.
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