Historically, the biggest bottleneck in software delivery was writing code. Generative and agentic AI coding tools have solved this problem by dramatically speeding up the rate at which developers can produce code.
But this presents another problem: Validating the code. As modern AI accelerates and democratizes coding, businesses must ensure that they can bring the same efficiencies to ensuring trust and release confidence in code.
Fortunately, AI can help here, too, by transforming software testing and Quality Assurance (QA). While the practice of automating software testing is not new (QA teams have been doing it for years), AI presents opportunities to take testing automation to entirely new levels – and, in the process, ensure that the entire software delivery pipeline can move at the speed of AI.
The limits of traditional test automation
For years, it has been commonplace for QA engineers to automate software testing routines. They do so by writing scripts that evaluate how applications behave under various configurations. This approach makes it feasible to test applications across multiple operating systems, browsers, mobile devices and other platforms quickly and scalably.
But there are limits on how much efficiency QA teams could achieve using conventional test automation tools. By the same token, there are limits on the ability of testing processes to keep pace with software delivery and ensure that businesses could validate new code as quickly as they wrote it.
This was because, under a conventional QA strategy, teams perform many key aspects of the software testing process manually:
- Defining test cases, the set of criteria that testing tools evaluate.
- Designing test workflows, such as what to test first and which tests to run in parallel to other tests.
- Defining selectors, which automated testing tools used to identify elements (like UI components) within software applications.
- Setting validators, which determine the criteria that an application must meet to “pass” a test.
- Writing scripts that define the steps to carry out during automated testing.
- Updating test automation scripts whenever applications or testing requirements changed.
In short, in the traditional world of software testing and QA, automation injected efficiency only into the software testing process itself. Designing, setting up and reacting to tests still required a great deal of manual work. In fact, these processes required so much work that testing was historically cyclic, rather than continuous, because the cost of creating and maintaining coded automation made continuous validation impractical.
From test automation to agentic software testing autonomy
AI agents, however, offer opportunities for achieving radically new levels of efficiency, speed and scalability in the realm of software testing. This is because AI software testing agents can do things that traditional test automation tools just can’t. Examples include:
- Asking agents to generate test cases by reviewing functional requirements, documentation or user journeys.
- Instructing agents to fix broken test flows automatically by, for instance, redefining selectors in response to changes within an application’s UI.
- Validating outputs in a probabilistic way. This is a powerful method for determining whether an application has met test criteria in complex scenarios where success can’t be defined in terms of simple metrics.
- Exploring application states that engineers may have overlooked or not thought to test.
Capabilities like these go far beyond automating just the software testing process. They bring QA teams much closer to true, end to end test automation, enabled by AI agents that are able to carry out complex tasks on an autonomous or semiautonomous basis.
What’s more, by freeing QA engineers from the tedium of writing tests, agentic software testing also allows them to operate more strategically. They can focus on system validation risk coverage, governance and release trust instead of spending their time creating and maintaining scripts.
How AI powered test automation benefits organizations
The advantages of an approach like this extend far beyond saving QA engineers time and toil. It also significantly speeds up the overall testing process. This is critical because it ensures that software testing routines can keep pace with software delivery cycles that are also growing increasingly fast. In a world where engineers are expected to release software updates weekly or even daily, speeding up test cycles with help from AI agents is essential.
Indeed, when teams adopt agentic testing strategies, it becomes possible to make testing truly continuous. This idea has been tossed around for years, but it has historically been challenging to put into practice because traditional test automation technologies weren’t efficient or scalable enough to enable engineers to test code as quickly as they could write it.
AI agents have changed this, allowing QA teams to move from a model where they test software periodically or incrementally, to one where they continuously validate digital experiences.
Put another way, whereas test automation simply helped execute tests faster, AI helps organizations understand, in virtual real time, whether software systems truly work, even if the code within those systems changes constantly. That’s a much bigger shift.
Making the shift to agentic software testing
The question facing organizations isn’t whether AI agents can accelerate and help scale their software testing routines. The obvious answer is that they can.
Instead, the question is how businesses can take advantage of agentic testing. Adopting software testing platforms that feature agentic capabilities is part of the solution, but it’s not the full story.
Ultimately, shifting to agentic testing requires changing not just tooling but also culture. Software developers, QA engineers and organizations as a whole must pivot to a mindset where they treat quality validation as a continuous, ongoing process. Instead of thinking “we’ll know if our systems work when we next run a set of tests,” the mindset should be “we know things work because we validated every component of our systems the moment we built it.”
This change in culture surrounding application quality, reliability and operational confidence is where AI’s impact on software testing will be most profound. AI is poised to breed a new level of trust in software systems, while simultaneously speeding up the software delivery process. That’s worth at least as much as the efficiency and productivity gains that AI agents bring to software testing.
