AI testing agents: How AI is being used to fix software testing problems

Software applications are constantly changing, with updates and new features being rolled out more frequently. But every change can create new challenges for teams responsible for ensuring that applications continue to work as expected.

Traditional automated tests can break when an application's interface, buttons, or workflows change, often requiring engineers to spend additional time fixing scripts. AI testing agents are emerging as one approach to addressing these challenges, with tools designed to generate test cases, interpret requirements, and adapt to changes in applications. TestGrid is among the firms developing this technology through its AI testing agent, CoTester.

How AI testing agents differ from traditional automation

Conventional automation tools generally follow predefined instructions and scripts. When an application changes, those instructions may no longer work, requiring quality assurance (QA) teams to update the scripts manually. AI testing agents, by comparison, are designed to interpret testing requirements and respond to changes based on what they observe during execution. The approach shifts part of the testing process from manually writing and maintaining scripts towards systems that can generate test logic and adapt to supported application changes. However, the extent to which these tools can reduce maintenance depends on the application, the complexity of its workflows, and the capabilities of the testing system.

Turning requirements into test cases

One of the time-consuming stages of software testing is converting product requirements into executable test cases. QA engineers typically review user stories, acceptance criteria, and specification documents before writing scripts to check whether an application behaves as expected.

TestGrid's CoTester allows teams to upload or link stories from JIRA. According to the company, the agent interprets the requirements and generates test scripts within minutes. The platform is also designed to consider the context of the product and existing QA workflows when creating tests. This approach is intended to reduce the amount of manual work involved in preparing test cases, although the company has not provided independent comparative data establishing how much time teams save across different projects.

What happens when an application changes?

Changes to an application's interface can affect automated tests, particularly when buttons, element identifiers, or workflows are modified. TestGrid's AgentRx feature is designed to address this issue through what the company describes as self-healing capabilities.

According to TestGrid, AgentRx can identify supported interface changes and automatically adjust locator logic and test scripts during execution, including when applications undergo structural modifications or UI redesigns.

TestGrid says the system can adapt to changes in layouts, labels, attributes, dynamic UI elements, and an element’s visual appearance.

AgentRx uses a multimodal Vision Language Model (VLM) to analyse application screens, taking into account visual layouts, on-screen text, and structural elements.

This allows it to recognise interface elements even when their presentation or underlying structure has changed and to rewrite broken locator logic during execution.

The effectiveness of this type of self-healing still depends on the nature of the interface changes and whether the intended behaviour of the application remains consistent.

Why human oversight remains important

Despite the growing use of AI in software testing, human review remains relevant, particularly in enterprise environments where test results can affect release decisions.

TestGrid says CoTester includes human-validation checkpoints to allow testing teams to review and validate the agent's work.

The platform also supports scheduled test execution, including nightly builds, weekly regression testing, and checks before major releases.

Harry Rao, founder and CEO of TestGrid, said the objective was to reduce repetitive work rather than remove engineers from the testing process.

"We built CoTester with the understanding that enterprise teams are not looking to remove humans from the testing process. They are looking to remove repetitive, low-value work so their engineers can focus on judgement calls that genuinely require human input," Rao said.

Can AI testing agents learn over time?

Another area being explored by AI testing platforms is the use of feedback from previous testing cycles.

TestGrid says CoTester is designed to learn from tasks and feedback provided by testing teams, with the aim of refining its decisions and reducing test failures caused by inconsistent execution.

The changing role of software testing

The development of AI testing agents reflects an effort to address some of the maintenance and workload challenges associated with conventional automation.

For businesses releasing software more frequently, the ability to generate tests from requirements and respond to interface changes could become an additional part of quality engineering workflows.

However, AI testing agents do not eliminate the need for test planning, validation, or human judgement. Their role will depend on how reliably they handle application changes and how effectively teams can integrate them into existing development and release processes.