SmartBear Doubles Down on AI Testing, Channel Services

SmartBear is expanding its AI-driven testing capabilities across its platform, positioning channel partners to capitalize on growing demand for quality assurance in AI-powered development environments.

The updates, which span API testing, UI automation, and test management, reflect a broader shift in enterprise software development: as AI accelerates code creation, it is also introducing new risks around quality, governance, and compliance.

In an interview with Channel Insider, Joe Tong, SVP of global channel partnerships at SmartBear, said the company is aligning its platform and partner strategy to help enterprises—and their IT providers—close that widening testing gap in 2026.

SmartBear expands AI-driven testing across SDLC

At the center of the release is a push toward what SmartBear describes as “application integrity,” or continuous assurance that software performs as intended, even as AI accelerates development cycles.

A key addition is a new agentic capability in the Reflect test automation platform. Developers and QA teams can generate automated tests directly within their development environments, using contextual inputs such as existing test assets, reporting data, and development history. 

Tong told Channel Insider the tooling is a component of SmartBear’s overall promise to its partners and customers to enable AI adoption within software development workflows without compromising security and governance guardrails.

“We want to enable our customers to keep pace with the rate of change in the industry right now while ensuring that all compliance needs are met,” Tong said. “This is a practical way to expand a team’s abilities and still ask, ‘is this workflow ready,’ but do so efficiently.”

This approach is designed to reduce the friction of building automation frameworks from scratch and speed adoption among enterprise teams.

Agentic AI brings automation into development workflows

SmartBear is also embedding AI into test management workflows through new Rovo agent skills for Zephyr. These capabilities enable natural-language queries within Atlassian Jira, allowing teams to assess test coverage, review execution data, and evaluate release readiness more efficiently.

For QA teams under pressure to keep pace with AI-generated code, these tools aim to shorten feedback loops and improve visibility into testing gaps.

Tong told Channel Insider the company works extensively within the Atlassian ecosystem, and Tong himself worked for Atlassian for several years. 

“SmartBear is in a really unique place in the market right now because we have such deep relationships in the ecosystems that our customers and partners are in,” Tong said, noting that in addition to Atlassian, the company also works closely with AWS and others, including SAP and Google Cloud.

On-prem AI capabilities target regulated environments

Beyond cloud-based workflows, SmartBear is extending AI functionality into on-premises tools, a move likely to resonate with partners serving highly regulated industries.

Updates to ReadyAPI introduce natural-language AI test generation for complex API workflows, while TestComplete adds enhanced AI-based object detection to improve automation reliability in rapidly changing applications. 

These capabilities are designed to operate within secure, local environments while maintaining enterprise governance and compliance controls.

Channel partners see growing AI testing services opportunity

SmartBear positions the release as a response to mounting pressure on QA teams. According to company research, 70% of testing and quality leaders say software quality is already declining as AI accelerates code creation, and 68% expect testing bottlenecks to increase.

For MSPs and IT resellers, that gap represents a growing services opportunity. Enterprises adopting AI-driven development are increasingly looking for partners to help integrate testing tools, establish governance frameworks, and modernize legacy QA processes.

SmartBear works with a variety of partners, from resellers and integrators to technology alliance partners and distributors. The company recently formalized a relationship with Carahsoft to drive public sector adoption in the U.S.

Tong says those partners increasingly need vendors who can support AI initiatives at scale.

“AI is more than top of the fold, it’s the whole newspaper now,” Tong said. “We’re enabling our partners to speak into the business needs their customers are facing.”

SmartBear aligns channel strategy with hybrid AI adoption

Tong said that SmartBear is committed to bringing its channel partners the right technology, in addition to programmatic aspects of channel go-to-market strategy familiar across the industry, to ultimately align with the unique needs of individual customers.

At the same time, Tong added, he is aware of the rate of change and pace of innovation pushing everyone to move faster. 

“I really think we’re going to step back at the end of the year and go, ‘wow, that was a whole lot of change,’ and I think that’s a really exciting opportunity for all of us,” Tong said.

As AI-driven development continues to outpace traditional QA processes, vendors like SmartBear are betting that channel partners will play a central role in bridging the gap.

For MSPs, integrators, and resellers, that means moving beyond tool deployment toward higher-value services, including AI governance, testing automation strategy, and lifecycle optimization.

With additional AI enhancements planned later this year, SmartBear is signaling that testing, and the partners who support it, will be critical to ensuring enterprise AI initiatives scale securely and effectively.