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.

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

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.

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.

In recent years, AI technologies have evolved rapidly, and these capabilities are now accessible through APIs, making it easier for developers and enterprises to integrate AI into their applications. Whether it's natural language processing, image generation, or text summarization, AI APIs have become essential for modern applications.

You’re presenting AI-generated analysis in your quarterly strategy meeting. The slides are polished, the insights look solid, and you’re ready to move the conversation forward. Then the CFO leans forward: “Where did this number come from? I reviewed this data last week and something doesn’t add up.”

Your CEO just announced the company’s AI-first strategy and the product team is shipping AI features faster than ever. Marketing is promising intelligent automation to customers, while the QA team is left wondering how to actually test this stuff. Every QA team is grappling with the same challenge as AI becomes the default solution for everything from customer service to content generation.

APIs and AI are on a collision course. For decades, APIs have been the foundation of digital reliability: deterministic systems where you send a request, get a predictable response, and trust that what’s defined is what will happen. AI doesn’t play by those rules. Large language models and AI agents operate in probabilities. They don’t just follow contracts; they interpret them. They learn, infer, and sometimes hallucinate.

That’s a wrap on API Summit 2025! At our eighth annual event, the brightest minds in the worlds of APIs and AI gathered in New York City to define the next chapter of digital innovation. We're entering an era where APIs are not just connecting services but connecting intelligence. APIs are the neural pathways of this new AI world, where agents will reason, act, and collaborate through endpoints. At this year's API Summit, we saw how quickly this vision is becoming reality.