Systems | Development | Analytics | API | Testing

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.

The term “Behavior Driven Development”, or BDD, is an umbrella term for a wide variety of techniques. The general principles of BDD include reframing tests as descriptions of system behavior – “when I do one thing, I should see this result”. Historically, those descriptions were the output of a computer program. Over time, the uses of BDD shifted toward Gherkin, an English-like but precise language.

For decades, testing was about ensuring predictable outcomes in mostly static environments. Today, AI-driven applications are dynamic, adaptive, and sometimes unpredictable. That raises the stakes for quality engineering. When Gartner rolls out a brand-new Magic Quadrant, it’s a big deal to those of us in the software industry. We believe it signifies a new, innovative tech has officially arrived on the global stage.

BugSnag now supports applications built with the Vega Developer Tools from Amazon Devices & Services, giving developers immediate visibility into the stability of their TV-first apps. This launch is part of a deeper collaboration between SmartBear and Amazon, extending beyond tooling support into a broader partnership that accelerates software quality across the Amazon ecosystem.

The world is filled with products and services that power our everyday lives. As consumers, we all appreciate the benefits of a great product. But think back to the last time you used an app or website that was slow to load or failed to open. Experiences like that can leave a lasting negative impression and hurt a business’s reputation.

Managing a large suite of automated tests, especially across multiple tools, can be overwhelming. The SmartBear MCP (Model Context Protocol) Server centralizes orchestration, monitoring, and prioritization so you can keep pipelines fast, reliable, and easy to manage. The demo video below shows MCP in action, and the sections that follow explain how each capability can help you get more out of both open-source and commercial testing tools. Explore these docs to learn how to get started.

Over the weekend, I was looking to book flights for holidays. All excited, I was able to find a decent price for my family to head for some sunshine later this summer. I reached the payment stage, and that’s where the excitement and smile stopped. I was waiting for confirmation that my payment was finalizing, but then the system took a turn, and I was back on the booking page. Even more, I now had a higher price to pay for the flights. Did I continue with booking, NOOOOOO.

The shift is already happening. Over the past few years, you’ve invested resources in optimizing your APIs for human developers. Meanwhile, a new class of customers has quietly emerged – one that never takes coffee breaks, never sleeps, and processes information at superhuman speeds. AI agents are rapidly becoming the primary consumers of APIs, and this fundamental change demands a complete rethinking of how we design, deploy, and maintain our digital interfaces.