SmartBear launches Sources and Workflows to automate software testing with AI

What Sources and Workflows add to BearQ

SmartBear has introduced two new capabilities for its BearQ agentic quality assurance system: Sources and Workflows. According to SiliconANGLE, these features are designed to improve test coverage and automation in software development.

Sources integrates with Atlassian’s Jira and SmartBear’s own Reflect and Zephyr testing tools. It identifies gaps in test coverage by analyzing requirements, bug reports, and existing test results. This broader view helps teams avoid duplicate tests and ensures critical areas are addressed.

How Workflows enables automated testing

Workflows allows teams to automate test runs within continuous integration and delivery (CI/CD) pipelines or on a predefined schedule. As reported by SiliconANGLE, teams can chain multiple jobs together behind a single trigger, such as running regression tests or smoke tests.

The system introduces what SmartBear calls 'progressive autonomy.' This approach lets teams control how much of a workflow runs without human review, enabling a gradual transition to fully autonomous testing. Teams can adjust oversight levels based on their comfort and trust in the system.

Trust as a barrier to autonomous testing

A survey conducted by SmartBear, cited by SiliconANGLE, found that trust is the top barrier to adopting or scaling autonomous testing. Out of 1,436 respondents in the U.S. and U.K., 23% identified trust as their primary concern.

This finding aligns with the design of BearQ’s new capabilities, which emphasize gradual autonomy. By allowing teams to retain human oversight where needed, SmartBear aims to build confidence in automated testing systems.

BearQ’s evolution and adoption

BearQ was first introduced in March 2026 as an early-access tool for web applications built by midmarket software-as-a-service (SaaS) providers. According to SiliconANGLE, a September update integrated BearQ as an assignable agent within Atlassian’s Jira, expanding its utility for development teams.

SmartBear’s software, including its Swagger tools, is used by approximately 32,000 organizations. The company plans to add more integrations for Sources before the end of the quarter, further enhancing its ecosystem.

Context behind BearQ’s development

The rise of AI-driven development tools has accelerated the need for faster, more reliable software testing. Agentic systems like BearQ are designed to address this gap by automating complex workflows and reducing manual effort.

Other companies in the AI and enterprise technology space are also exploring agentic infrastructure. For example, IBM and CoreWeave have partnered to develop controls for AI agent workloads, while TOTVS has expanded its enterprise AI foundation to integrate operational data.

What this means

Lazyfounder analysis — our interpretation, not reported fact.

SmartBear’s launch of Sources and Workflows for BearQ reflects a broader industry shift toward AI-driven automation in software testing. For founders and operators, this signals two key trends:

First, the speed of AI-generated code is outpacing traditional testing methods, making tools like BearQ increasingly necessary. Teams that rely on manual or semi-automated testing risk falling behind in both velocity and quality.

Second, the emphasis on 'progressive autonomy' is a pragmatic response to the trust gap in autonomous systems. Founders should note that gradual adoption—rather than an all-or-nothing approach—may be the most effective way to integrate AI-driven tools into existing workflows. This also suggests that future AI products in QA, DevOps, or adjacent spaces may prioritize flexibility and user control to drive adoption.

Finally, the focus on integrations (e.g., Jira, Reflect, Zephyr) underscores the importance of interoperability. For startups building in this space, compatibility with established tools is likely a critical factor in winning over enterprise and midmarket customers.

Key takeaways

• AI-driven coding speed is increasing the need for automated, intelligent testing solutions like BearQ.
• Founders should evaluate how agentic systems can reduce redundancy in testing while maintaining control.
• Trust remains a critical factor in adopting autonomous tools—progressive autonomy may help teams transition gradually.
• Integrating testing tools with existing workflows (e.g., Jira) can streamline adoption and improve coverage.
• Midmarket SaaS providers may benefit most from early adoption of agentic QA systems.

FAQ

What is BearQ?

BearQ is an agentic quality assurance system developed by SmartBear. It uses AI to identify test coverage gaps and automate testing workflows in software development.

How does Sources work?

Sources integrates with Atlassian’s Jira and SmartBear’s Reflect and Zephyr tools to analyze requirements, bug reports, and existing tests. It identifies gaps in test coverage and helps reduce duplicate tests.

What does 'progressive autonomy' mean?

Progressive autonomy is SmartBear’s approach to allowing teams to gradually reduce human oversight in automated testing workflows. Teams can adjust how much of a workflow runs without review, based on their trust in the system.

Why is trust a barrier to autonomous testing?

A SmartBear survey found that 23% of respondents cited trust as the top barrier to adopting or scaling autonomous testing. Teams may be hesitant to rely on fully automated systems without assurances of reliability and accuracy.

Who is BearQ designed for?

BearQ was initially introduced for midmarket SaaS providers. It is now integrated into Jira and used by development teams across approximately 32,000 organizations that rely on SmartBear’s tools.

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