New updates across API testing, UI automation and test management aim to help developers and QA teams generate tests faster, spot gaps earlier and keep pace with the surge in AI-written code.

SmartBear is expanding AI across the full software testing lifecycle, rolling out new capabilities for API testing, UI test automation and test management in its SmartBear Application Integrity Core suite, as companies look for ways to prevent quality from slipping in the age of AI-generated software. For users, the pitch is straightforward: less time building tests manually, faster visibility into release risk, and more reliable automation in environments where applications are changing faster than traditional QA workflows can handle.

The company’s latest release adds AI and agentic features to human-led testing rather than forcing customers into a one-size-fits-all autonomous model. That matters in a market where many vendors are selling AI primarily as a replacement layer. SmartBear is instead positioning its tools as a bridge between manual testing, assisted automation and fully autonomous testing, giving teams more flexibility depending on their maturity, compliance needs and internal appetite for change.

One of the most notable additions lands in Reflect, SmartBear’s test automation platform. Developers and QA engineers can now generate automated tests directly from their coding environment through the SmartBear MCP server. The differentiator here is context. Instead of creating tests in isolation, the system can draw on existing test assets, reporting, shared visibility and development history to create context-aware tests. For users, that could reduce one of the biggest barriers to automation adoption: having to start from scratch every time a team wants broader coverage.

SmartBear is also pushing deeper into the Atlassian ecosystem with new Rovo agent skills for Zephyr. Inside Jira, QA teams can use natural-language queries to evaluate test coverage, search test executions and assess release readiness. In practice, that means less jumping between dashboards and less manual digging for signals about what is ready to ship. For teams under pressure to move quickly, the benefit is not just convenience but prioritization: identifying gaps sooner and focusing effort where testing risk is highest.

Another area where SmartBear is trying to stand apart is enterprise readiness. While many AI testing competitors are focused heavily on cloud-first workflows, SmartBear says it is bringing AI capabilities to on-premise tools for desktop testing and secure local environments as well. That includes natural-language AI test generation in ReadyAPI for complex multi-step API tests and enhanced AI-based object detection in TestComplete. For large enterprises in regulated industries, that could be a meaningful advantage, offering AI acceleration without requiring teams to move sensitive workflows out of tightly controlled environments.

The broader market context helps explain the timing. SmartBear said a recent study of 273 software testing and quality decision-makers found that 70 percent are concerned quality is already suffering as AI speeds code creation, while 68 percent worry that faster AI development will create testing bottlenecks. The company is betting that those concerns will translate into demand for tools that do not just generate more code, but help teams verify that code at the same pace.

“SmartBear is firing on all cylinders to enable QA teams to move faster and improve application level testing,”

said Vineeta Puranik, SmartBear’s CPTO.

“We see some teams racing toward fully autonomous solutions like BearQ, and others deploying AI-enabled tools to complement human-directed automation or even manual workflows. We meet customers where they are on their AI journeys by helping teams adopt AI confidently, scale testing effectively, and maintain application integrity as software delivery accelerates.”

That “meet customers where they are” message is central to SmartBear’s positioning. The company recently launched BearQ, its fully autonomous testing product, and is now broadening the rest of its portfolio with AI-infused features. The result is a more comprehensive strategy than competitors that are concentrated only on autonomous agents, only on test management, or only on developer-side tooling. SmartBear’s argument is that modern teams need a connected testing layer across the lifecycle, with AI available in different forms depending on the job.

Chris Lewis, CEO of Praecipio, an Atlassian-focused consulting firm and SmartBear partner, framed the release as a practical response to what enterprises are actually asking for.

“Organizations are looking for practical ways to apply AI across their software delivery lifecycle,”

he said.

“Capabilities like these from SmartBear help teams uncover testing gaps and act on them quickly, exactly the kind of innovation we help our clients operationalize.”

For users, the biggest takeaway is that SmartBear is not selling AI as a future promise. It is packaging it into concrete workflow improvements: faster test creation, more intelligent coverage analysis, better release-readiness insight and stronger automation for teams that cannot compromise on governance. As AI-generated code continues to accelerate software delivery, vendors that can help customers keep quality from becoming the bottleneck may find themselves in a strong position. SmartBear is clearly aiming for that opening, and it says more product enhancements are on the way later this year.

Image: YouTube video SmartBear (screenshot)