Systems | Development | Analytics | API | Testing

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.

In September 2025, we brought AI Engineer to Paris for a two-day conference with 700+ attendees, 25 sponsors, over 47 talks cross 5 tracks. This is the third post in a 4-part series on the event: If you missed AIE Paris, you’ll be able to watch the replays on the Koyeb YouTube channel. Main Stage Day 1 Main Stage Day 2 If you’re not familiar, the team behind the AI Engineer has been running AI Engineer conferences in the US for the past three years.

At WSO2, our content is grounded in original thinking and hard-earned lessons from building real enterprise systems with our products. We apply first principles to shape our perspective on our strategy and forward thinking. No model can replace this experience. AI is valuable when used by thoughtful authors, but alone it does not deliver meaningful value to our readers, customers, or community.

Our latest research paints a concerning, albeit solvable, situation of blame and misalignment.

By now, most teams experimenting with AI-augmented testing have started with narrow, tactical use cases: writing test cases faster, summarizing logs, or tagging defects. These are useful — and they build trust in the tech. But true value emerges when you stop thinking of agents as plug-ins, and start thinking of them as a virtual QA team, a set of coordinated roles that evolve how testing is done, how it’s governed, and how it delivers value across the delivery lifecycle.