Systems | Development | Analytics | API | Testing

10x faster coding. Same QA. Until now. You're coding faster with AI. But if your agents are siloed and disconnected, those gains aren’t yet showing up in your quality or delivery – it's getting lost in the handoffs. Join SmartBear for a live demonstration of the connected agentic SDLC. See how BearQ extends beyond creating tests to coordinating quality work across Jira, GitHub, and AI-powered workflows – detecting issues, triggering the right next action, and carrying context from one agent to the next.

Watch this video to see how see how SmartBear BearQ keeps bug reports and test results in sync with Jira, Linear, and Azure DevOps at AI speed and scale, with no manual copy-pasting. BearQ’s agentic QA system connects to the tools your team already relies on, so its three built-in agents can pull in context and push results right where your team lives.

Wondering which of your AI-driven tests need attention right now? Watch this video to learn how SmartBear BearQ spots flaky tests and failure trends the moment they happen, so you always know exactly where to focus. Each report summarizes what happened and surfaces the issues BearQ detected, whether while exploring your app or running a scheduled test. Drill into failed tests to see exactly which runs broke and why, track trending results over time, and review open issues found across both passing and failing runs.

In this video, you’ll see how SmartBear BearQ's QA agents catch bugs, UI and UX issues, and accessibility gaps automatically as they explore or test your applications, zero test script writing necessary. Whenever the Explorer Agent investigates your app or the Tester Agent runs a test, the QA Lead Agent is watching in the background for anything that looks wrong. Each suspected issue arrives with a clear title, a description, expected versus actual behavior, steps to reproduce, and a link to the evidence that shows exactly when it was found.

Can you trust AI agents to run your tests without losing control of your release process? In this video, you’ll learn how, with SmartBear BearQ’s agentic QA system, the answer is yes, because you decide exactly how much autonomy the agents get. This enables you to adapt QA to fast-moving AI codebases, all with human oversight.

Rovo, Atlassian’s AI assistant, can help you generate test cases directly inside Jira through the SmartBear Zephyr Agent for Rovo. This demo offers a practical look at AI-powered testing with Rovo in Jira, from requirements to reviewed test cases, all within the Jira experience, without switching tools.

Not everyone who needs a read on release readiness lives in Jira. This video demonstrates how the SmartBear MCP Server brings trusted Zephyr test data into Claude, so anyone who needs access to testing data gets it without switching context. A QE lead checking coverage, a release manager confirming a ship date, a developer following up on a fix, and any stakeholder who just wants a fast answer has what they need.

In this video, you'll learn how SmartBear Swagger Agents, built right into SmartBear Swagger Studio, cut the process of manual API governance reviews and documentation from weeks to minutes, generating API definitions that are specific to your APIs and compliant with your organization's governance rules from the start.

What actually makes you trust the software you ship? Proof. As AI changes how software gets built, teams need more than faster delivery in a crowded market where new tools and solutions are constantly emerging. Trust has to be earned. They need application integrity: confidence that what they're shipping works as intended.

Think you know the rules of shipping software with AI? Myth or fact? Test your instincts and see what it takes to maintain application integrity at the speed of AI.