Systems | Development | Analytics | API | Testing

The AI revolutionized coding, but software testing hasn’t caught up. Until now. Meet BearQ: QA built for the age of AI. BearQ introduces a new paradigm of autonomous, agentic quality assurance. Instead of static scripts and brittle frameworks, BearQ’s specialized AI agents – the QA Lead Agent, Tester Agent, and Explorer Agent – work continuously to: Testing was a static checkpoint. Now it’s a living, learning system that ensures application integrity.

Poorly documented or inconsistent error models lead to brittle clients and unreliable automation. Whether you're building APIs for human developers or AI agents, proper error handling is crucial for automation and reliability. In this guided tutorial, SmartBear Solutions Engineer Rosemary Charnley demonstrates how to design robust error models in OpenAPI specifications using Swagger Studio.

API design, functional testing, and governance shouldn’t live in silos. In this demo, Product Owner Wojciech Nowacki walks through a practical, end-to-end workflow that connects: You’ll see how API definitions created in Studio feed directly into automated functional testing ensuring style compliance, functional correctness, and governance checks across the full API lifecycle. Perfect for API platform teams, architects, and developers looking to unify design and test automation.

As AI agents increasingly interact with APIs, design clarity and structured metadata matter more than ever. In this focused demo, Senior Solutions Engineer Mairtín Conneely take us through how to use Spectral rulesets in Swagger Studio to automatically enforce AI-ready API design standards across your OpenAPI definitions. This video covers:What “AI-ready” API design meansCreating custom Spectral rulesImporting governance rules into Swagger StudioRunning automated AI-readiness checksScaling API quality with governance automation.

Create a single mobile test that runs reliably on both iOS and Android - without building separate tests per platform or relying on brittle, platform-specific locators. In this high-level demo, we use SmartBear Reflect’s vision-based AI to record a typical workflow in a sample coffee app, where each step is backed by visual context and intent. Then we run the same test across a mix of Apple and Android devices, including an iPhone, to show how Reflect adapts to the environment at runtime and helps reduce flakiness and false positives.