SmartBear styles itself as more than just a code testing company; its founders say it spans deep test automation, API lifecycle management and observability capabilities to create application integrity for continuous, measurable software assurance. The company has now announced its agentic QA system, BearQ, is now available as an assignable agent in Atlassian Jira.
Talking about how it helps teams build, test, and ship quality software at AI speed and scale, SmartBear says that as developers build governed agent loops in Jira to scale their work safely, BearQ brings that same discipline to testing by embedding directly into the Jira workflows teams already use to assign work and keep plans coordinated.
With BearQ for Jira, users can support their AI software development lifecycle (SDLC) by autonomously testing the software as it’s being developed to close the loop between the code an agent writes and the quality an agent validates.
“Jira is where work gets defined, and assignable agents help carry it out across the SDLC,” said Alan Braun, head of product ecosystem at Atlassian. “BearQ is built specifically for testing, exploring the application the way a person would, rather than solely reviewing the code underneath it. As testing grows more complex, that’s what helps teams ship faster and safer.”
Test, as you are, as you were
BearQ tests applications as they are, not against outdated scripts or specs. Rather than reviewing code line by line, BearQ explores the running application the way a real user would, clicking through flows, exercising edge cases, and validating behaviour across the full app, not just the lines that changed. Customers are seeing huge wins.
“BearQ has become a valuable part of our testing process. It helps uncover gaps, edge cases, and potential risks across our application that can easily be missed in traditional exploratory testing. By broadening our test coverage and surfacing new test ideas, it has strengthened both the quality and efficiency of our testing efforts. Its exploratory power has made testing not just more thorough, but genuinely more enjoyable,” said Beth Barton, Lead, Product Coordination & Quality Assurance at Simon Property Group.
Braun points out that, as an assignable agent, BearQ embeds testing into the AI SDLC workflows teams have in Jira. When assigned a work item, BearQ will understand the context of new application capabilities, validate, and adapt testing around real user journeys, reducing the script maintenance that slows QA down.
Teams set the autonomy level to meet their desired level of human oversight. As a result, QA scales to keep pace with AI development, but teams retain control, and BearQ agents act as QA teammates while humans review what gets trusted to agents.
“Developers are using AI to create and ship code faster than ever, but testing is falling behind because it’s often manual and constrained by brittle automation that cannot keep pace. Not only is there more to test, but it’s increasingly hard to know what to test,” said Dan Faulkner, SmartBear CEO. “BearQ addresses those challenges so QA can keep up with today’s fast-moving codebases. We’re pleased to deepen our work with Atlassian and Jira.”
Workflow transitions with zero manual invocation
As with other agents in Jira, users will be able to assign work to BearQ, mention it in comments to get help, and add it to workflow transitions with no manual invocation and no context switching. BearQ assignable agents will find what scripts can miss and fix what slows QA down by turning testing into a living, learning system that evolves with applications. Customers can ask BearQ to record the tests and results in Zephyr, SmartBear’s testing system of record for Jira.
BearQ launched in March as SmartBear’s agentic QA system to help teams keep up with AI-code development and ensure application integrity, meaning that applications work as intended, all the time.