Systems | Development | Analytics | API | Testing

As software teams move faster across APIs, testing, and observability, keeping application integrity intact is harder than ever. Join SmartBear product leaders for a Now / Next / Later look at how we’re evolving our platform to help teams build, test, and operate software with confidence. What you’ll get from this session: Get a clear view of where SmartBear is headed and how these capabilities come together to help your teams scale quality alongside velocity across the SDLC.

Modern APIs aren’t just read by developers anymore; they’re also interpreted by tools and AI agents. In this video, Solutions Architect Joe Joyce walks through how to enrich an OpenAPI definition in Swagger Studio with meaningful metadata such as descriptions, summaries, operation IDs, tags, schemas, and examples. You’ll see step-by-step how these additions help tools and automated agents better understand API intent, purpose, and semantics. This turns your OpenAPI definition into a contract that scales beyond documentation.

In this demo, you’ll see how Reflect and QMetry work together to connect automated testing with test management. In this short walkthrough, test execution from Reflect flows directly into QMetry, giving your team better visibility, reducing manual effort, and helping you move faster without losing control of quality. If you’re looking to scale testing while keeping everything organized and traceable, this integration is built for you.

Testing teams need to know if they’re ready for a release. Getting answers within Jira, however, often means jumping between multiple screens and reports. In this demo, see how you can query your test data with SmartBear MCP for Zephyr to get insights directly from your testing system of record, so you can make faster, more informed release decisions. From within AI tools like Copilot, Claude, or VS Code, you’ll learn how you can.

Asked a question in our BearQ livestream? We’ve got your answers. We received 100+ questions during the event and couldn’t get to all of them live, so we pulled together the most common ones and answered them here. In this video, we break down what BearQ can test, how it handles authentication and complex workflows, how the AI works behind the scenes, how it fits into your existing tools, and even how to get early access.

Missed the live event? Here’s a quick look at what we unveiled. AI has fundamentally changed how applications are built, creating a growing gap between development velocity and your ability to validate what’s being built. That’s why SmartBear delivers application integrity for the AI era – ensuring continuous, measurable assurance that your software just works as intended, with governance to operate at AI speed and scale.

AI has transformed software development, dramatically increasing velocity. The challenge now is maintaining quality at that speed. Engineering leaders across the industry are searching for a real answer. On March 18, we’re unveiling our solution. Join our livestream for an exclusive product reveal featuring special guest, John Romero. A legend in the industry and the perfect voice to help us unveil what we've been building.

Agent-powered code generation is happening at unprecedented speed, creating a growing gap between development velocity and your ability to validate what's being built. This leaves organizations unsure if their applications are doing what's intended or missing what's required. That's why SmartBear delivers application integrity for the AI era – ensuring continuous, measurable assurance that your software just works as intended, with governance to operate at AI speed and scale.

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.