SmartBear is bringing autonomous software testing directly into Atlassian Jira with a new integration for BearQ, its AI-powered testing agent. The idea is straightforward: instead of requiring QA teams to leave Jira, manually prepare test scripts, or switch between multiple tools, BearQ can now be assigned work directly from an existing Jira issue and begin testing the running application from there.
The timing is significant because software development is changing quickly. Coding agents and AI-assisted development tools are helping engineering teams create and ship software faster, but testing often remains a bottleneck. QA teams are still expected to validate growing volumes of functionality while dealing with manual processes and brittle automated tests that can require constant maintenance. SmartBear is positioning BearQ as a specialist agent designed specifically to help close that gap.
Rather than focusing only on source-code changes, BearQ evaluates the actual application from a user's perspective. It explores live workflows, tests functionality across the running system, looks for edge cases, and checks whether the application behaves as expected. This makes the agent closer to an autonomous exploratory tester than a traditional code-analysis tool.
Testing the Application the Way a User Experiences It
One of BearQ's main differences is that it tests running software rather than relying entirely on static specifications or existing test scripts. Traditional automated testing is extremely useful, but scripted tests can become outdated as interfaces and workflows change. If the test still expects the application to behave exactly as it did several releases ago, teams may spend significant time repairing the automation instead of finding new problems.
BearQ approaches the application more dynamically. The agent navigates workflows, interacts with the interface, tries different paths, and evaluates how complete user journeys behave. That means it can potentially identify issues outside the exact lines of code changed in a recent commit, which is important because a seemingly small change can sometimes affect other parts of the application indirectly.
This broader approach also gives QA teams another layer of exploratory testing. Human testers routinely discover defects by trying combinations or user behaviours that nobody originally anticipated, and BearQ is intended to extend that type of exploration at machine scale. It does not eliminate the need for human testers, but it can help teams cover more of the application before the release reaches production.
Jira Becomes the Starting Point for Autonomous QA Work
Integrating BearQ directly with Jira is arguably just as important as the testing technology itself. Jira is already where many development teams define features, bugs, tasks, priorities, and release work, so adding an assignable testing agent there avoids creating another disconnected workflow. A developer or QA engineer can assign a ticket to BearQ in much the same way they would assign work to another team member.
The agent can interpret the technical scope of the Jira issue and then adjust its testing behaviour around the feature being developed. Instead of requiring somebody to manually create a new test script every time requirements change, BearQ can dynamically explore the relevant user journey based on the issue context. This reduces some of the maintenance burden traditionally associated with automated testing.
Team members can also mention BearQ within Jira discussions when they want testing assistance. The agent can be inserted into automated ticket transitions as well, allowing organisations to make testing part of their existing workflow without asking engineers to run separate commands or move between systems. In practice, this means BearQ can become another participant in the software development lifecycle rather than a standalone tool that only QA specialists know how to operate.
The Agent Does More Than Review Code
Atlassian's Head of Product Ecosystem, Alan Braun, described the value of BearQ as its ability to explore software much like a person would rather than focusing exclusively on the code beneath it. That distinction matters because code can be technically correct while the final user experience is still broken.
An application may pass unit tests but fail when several services interact. A form may technically submit successfully but become unusable when a user enters unexpected information. A feature may work perfectly on the happy path while failing in less obvious situations that were never written into the original test plan.
BearQ is designed to look for those kinds of problems across the live application. By navigating complete workflows, it can generate new test ideas and uncover gaps that traditional automation may not cover. Simon Property Group's Beth Barton said the agent had helped its QA process identify potential risks and edge cases that could otherwise be missed during conventional exploratory testing.
That type of coverage becomes increasingly important as applications grow more complicated. Modern systems often combine web interfaces, APIs, authentication services, third-party integrations, databases, and cloud infrastructure, so verifying only one component at a time does not always reveal whether the full experience is working correctly.
Autonomy Does Not Mean Removing Human Oversight
SmartBear is also emphasising that BearQ's autonomy can be controlled rather than simply turned loose inside a development pipeline. Organisations can configure different levels of oversight depending on how much authority they are comfortable giving the agent.
Development managers can define thresholds where a human needs to review or approve decisions before the workflow continues. This is particularly important in enterprise environments where automated testing results may influence whether software advances toward production. An autonomous agent can accelerate the process, but teams still need confidence that important decisions remain visible and auditable.
This governed approach reflects a broader trend in agentic AI. Giving software agents more independence can improve productivity, but completely removing human review may introduce unnecessary risk, especially when the system is interacting with business-critical applications. Configurable autonomy lets teams experiment with increasingly automated QA while retaining checkpoints around higher-risk decisions.
Over time, organisations may become comfortable automating routine validation while keeping human review for unusual failures, security-sensitive workflows, or major releases. That allows the testing process to become faster without turning the release pipeline into an opaque black box.
Zephyr Keeps the Testing Record Inside Jira
BearQ also integrates with Zephyr, SmartBear's testing system of record for Jira. Test sequences and execution results generated by the agent can be written directly into Zephyr repositories, preserving a structured history of what was tested and what happened.
That becomes particularly valuable for teams operating in regulated or heavily audited environments. Autonomous testing may speed up QA, but organisations still need evidence showing which tests were performed, which results were observed, and whether issues were resolved before deployment. Automatically capturing those records means speed does not have to come at the expense of traceability.
Keeping the information inside the existing Jira and Zephyr environment also reduces fragmentation. Instead of test results living in one AI system while requirements and defects live somewhere else, teams can maintain a more complete connection between the original work item, the tests performed against it, and the resulting QA record.
This can also make release reviews easier. Product managers, engineers, QA leads, and compliance teams can work from the same source of information rather than trying to reconcile separate reports immediately before deployment.
AI-Generated Code Is Increasing Pressure on QA
SmartBear originally introduced BearQ in March against the backdrop of increasingly capable coding agents. The fundamental concern is that AI can now help engineering teams produce software more quickly, but every additional feature and code change still needs to be tested.
If development speeds up dramatically while QA remains largely manual, the imbalance simply moves the bottleneck further down the pipeline. Teams may generate more software than they can confidently validate, eventually forcing either slower releases or greater risk.
Traditional test automation helps, but it also has limitations. Automated scripts need to be created, maintained, and updated whenever an application changes. In fast-moving software environments, the test suite itself can become another body of code requiring significant engineering effort.
An autonomous exploratory agent offers a different model. Instead of requiring every test path to be predetermined, the agent can inspect the running software and adapt its approach based on what it encounters. That does not make scripted tests obsolete, but it can complement them by exploring areas the original automation never anticipated.
QA Teams Could Spend Less Time Maintaining Scripts
One of the more practical benefits could be reducing routine test maintenance. QA engineers often spend considerable time repairing selectors, updating scripts, and modifying automation after relatively small interface changes. That work is necessary, but it does not necessarily improve coverage or uncover new defects.
BearQ's ability to dynamically understand a feature and navigate the application could shift more QA time toward analysing risk and investigating meaningful problems. Instead of maintaining hundreds of scripted steps simply to keep automation running, teams can use the agent for exploratory work while retaining deterministic automation where repeatability is essential.
The strongest QA environment will probably combine several approaches. Unit and integration tests remain important for validating specific technical behaviour, scripted end-to-end automation is valuable for predictable critical paths, and autonomous agents can provide broader exploration around those established checks.
Human testers then provide the judgement required to understand whether something that technically works actually makes sense for the person using it.
Final Thoughts
SmartBear's integration of BearQ with Jira is an interesting example of how AI agents are moving from experimental assistants into practical engineering workflows. Instead of asking QA teams to adopt an entirely new environment, the agent can be assigned directly inside the system where development work is already being tracked.
Its value also comes from testing the running application rather than concentrating solely on source code. By exploring real workflows, generating additional test ideas, and examining edge cases across the application, BearQ can help fill the gap between rapidly accelerating development and increasingly stretched QA resources.
The inclusion of configurable human oversight and direct Zephyr reporting is equally important. Autonomous testing becomes much more useful in an enterprise setting when teams can control how much authority the agent receives and maintain an auditable history of what it did.
As coding agents continue increasing the speed at which software can be produced, QA will need to evolve alongside them. The interesting question is no longer simply whether AI can write more code. It is whether testing systems can become intelligent enough to keep pace without sacrificing the confidence teams need before that code reaches production.
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