The information era has made it impossible for enterprise domains to remain isolated. Every aspect and component is now interconnected—each vector plays a crucial role in business strategy and value creation. Contrary to popular belief, human talent is more vital than ever. Technological advancements alone cannot provide sufficient leverage without the insight and expertise of knowledgeable professionals.
But how do these changes affect the field of test automation? In the past, AI tools were far from being the ultimate solution for test automation. While they offered some utility, they lacked flexibility and couldn’t address major challenges. The introduction of LLM-based tools, however, brought new capabilities to AI in automation testing. This article explores the benefits, challenges, and future prospects of AI in software testing automation to help testing engineers understand its potential and navigate emerging concerns.
What is AI in test automation?
AI in test automation covers the utilization of artificial intelligence for enhancing and accelerating the process of software testing. This improvement allows testing teams to cover a wider range of tasks within the same amount of time and automate repetitive steps that don’t necessarily require human intervention.
To understand the importance of AI in automation testing, one should look back at the brittle foundation of test automation.
Manual testing
(1990-2000)
- 100% human participation
- Documentation-based processes
- Release cycles up to 12 weeks
Script-based automation
(2000-2010)
- Expert dependency for test creation
- Script-based framework
- Record-and-playback tools
Automation frameworks
(2010-2020)
- No-code/low-code solutions
- Adopting DevOps and integrating CI/CD
- API testing
AI-powered automation
(2020-now)
- Automated test creation and maintenance
- Self-healing and dynamic adaptation
- Intelligent optimization
Starting in the 2000s, the first steps toward test automation revolved around script-based frameworks that were overly complex, hard to scale, and required consistent support from tech professionals. The efficiency of such frameworks was 30% at best—mostly because maintaining them took more time than developing solutions—which caused a lot of skepticism about their long-term value.
The next stage of testing automation addressed these concerns and vulnerabilities, resulting in the era of mature automation frameworks—codified test cases, executed and integrated into CI/CD pipelines. This era led to the rise of Playwright, Selenium, and Cypress frameworks and up to 70% testing efficiency. Codeless solutions considerably reduced framework complexity while increasing scalability, while integration with other development workflows allowed for a smoother and more resultative process. While this approach became a breakthrough for engineers, it quickly revealed its weak side once user interfaces evolved.
Traditional test automation challenges
Script vulnerability
Front-end changes could lead to multiple tests breaking.
Complexity
Test creation and maintenance required the involvement of engineer teams.
Demanding maintenance
Test maintenance and fixing tests took more time than creatine new tests and completing QA tasks.
False alarms
Test failures were caused not by bugs, but irrelevant issues such as environment and timing.
Limited functionality
Tests lacked flexibility and were incapable of intelligent evaluation.
With time, test flakiness and vulnerability to factors unrelated to bugs led to negative outcomes: productivity was dropping, trust was eroding, and frustration kept accumulating.
The emergence of AI and ML in test automation addressed a wide range of these issues, allowing engineers to design advanced testing systems that offered greater awareness, visibility, and self-healing, which replaced fickle single selectors with multiple attribute analysis.
How does test automation benefit from AI testing tools?
Previously, test automation tools with AI offered predictable improvements, such as greater data understanding, earlier bug detection, and optimized test creation speed.
While it was a considerable improvement compared to the previous manually managed test automation era, the arrival of test automation with generative AI and agentic AI considerably expanded technology’s capabilities.
Previous limitations
LLM-powered test automation
Low flexibility
- Limited functionality
- Inability to work with complicated tasks
- Incapacity to process many variables
Context-aware code generation
- Greater agility
- Building from code samples, documentations, or requests
- Chaining multiple steps within a single instruction
No major issue coverage
- Inability to identify logic flaws or usability problems
- Bad fit for unconventional test cases
- Simple UI testing only
Business flow understanding
- Advanced understanding of business flow and user interactions
- Sophisticated detail and vulnerability identification
- Fast and detailed UI testing
What makes agentic AI so efficient, is the advanced NLP technology that powers its algorithms and allows for more in-depth analysis, better task comprehension, and improved reactivity.
Recognizing intent
- Identifying the goals and intentions of users
- Awareness of objects (buttons, profiles, results)
- Differentiating between conditions
- Understanding assertions (“should contain”, “must be redirected”)
Understanding context
- Understanding the current status of the application
- Differentiating between various permission levels and user roles
- Following the business logic
- Awareness of data dependencies
Intelligent step planning
- Navigating the best sequences
- Identifying elements
- Building optimal wait strategies
- Independent error processing
Such robust capabilities of agentic AI tools allow developers to further accelerate test creation, reducing hours-long tasks to seconds, and empowering teams to focus on strategic, high-value decision-making.
In our practice, we developed a test case that worked with hundreds, thousands of records from various data sources. Initially, processing data for just one hour took up to two hours. Naturally, analyzing 24 hours’ worth of data was even more time-consuming—I had to wait at least five hours. The test case also couldn’t verify thresholds (transition dates) because the system aggregating the records potentially contained undetectable errors.
We were preparing to migrate to the Azure Databricks platform, which was designed to aggregate raw data, consolidate it into different formats, and store it.
Before proceeding, we needed to test this functionality. While rewriting the test case for Databricks, we encountered an issue: the test took too long to differentiate data by source (laptops vs. mobile devices).
I initially applied multithreading and managed to reduce the processing time from one hour to 30 minutes. Still, I wondered if further optimization was possible. At that point, we had access to Copilot. After experimenting with its features, I decided to apply it to the test case. Copilot successfully optimized the function, and I monitored its progress at every stage using breakpoints. The test case was completed in just 1.5 seconds, with only a few logical errors. After a couple of iterations, I had a flawless test case.
Given the impressive result with one hour of data, I wanted to see how the tool would handle 24 hours. To my surprise, the algorithm analyzed all the data in just two seconds. This scenario also revealed some errors—mostly on the platform developer’s side. On our end, the code was flawless. Thanks to Copilot’s test automation, we were able to process days’ worth of data, including transition dates and other threshold values, which significantly improved our workflow and eliminated much of the waiting and frustration from our routines
Self-healing process doesn't rely on wildcards or presets. Instead, it launches a full-scale investigation. Whenever an object can't be identified due to a property name or value change, the AI testing tool extracts the object, similar objects, their values, and property names and puts them into temporary storage.
Common applications of AI in test automation
The strongest point of test automation with agentic AI is how you can use the full capacity of the technology. For example, one of the tools we work with is Copilot, which can be integrated with ChatGPT, Gemini, or Anthropic. In this manner, we can work with the framework, write test cases, and automate them at the same time. This is a brand-new approach to end-to-end automation.
The injection of LLM into software testing with AI led to two major developments. The first is the significant improvement of outcomes delivered by AI and ML in test automation. The second is the emergence of intelligent testing assistants. It makes sense to explore both of these enhancements in greater detail.
Improving outcomes
Agentic AI in automation testing further advances the positive impact of artificial intelligence on the testing process by injecting it with greater intelligence and adaptability, and by removing even more friction from each step.
The key to such a change lies in the very capability of agentic AI—its agents. Not bound to predefined parameters, unlike traditional AI, agentic AI tools can make autonomous decisions, which makes them a good fit for complex testing scenarios and expands their potential in existing application:
- Writing use cases
Compared to previous iterations, agentic AI software test automation tools can be used to their full test case generation capacity. To write a working and functional test scenario, an LLM needs only context (for instance, product requirements) and directions through the right questions and queries.
Such an approach makes test case generation a lot more intuitive because models can translate plain English queries into code.
Working with an LLM-powered Copilot offers you three modes: edit mode, where your code base is; communication mode; and agent mode. Agent mode is the one responsible for generating test cases after getting context and requirements from you.
This code can then be interpreted into an autoscript, which will be implemented into the framework of choice.
These capabilities allow engineers to spend considerably less time on code editing, testing, and automation without compromising quality. However, it’s worth noting that even with such enhancements, agentic AI in software test automation is not capable of fully writing and executing test cases on its own—especially highly complex ones. The more sophisticated the software is, the harder it is for the model to adapt, and the greater the probability of inconsistencies, errors, and other issues.
For that reason, writing test cases with AI test automation tools requires constant checks and adjustments from experienced testers who are closely familiar with the code and logic of the software and, therefore, can fill in the gaps and refine outcomes.
I used Copilot for writing a file for one of our projects. I wrote simple instructions in English, while Copilot translated them into script and code. While I had to personally review the results, adjust variables, names, and remove logical errors, the code was overall functional—and it took me much less time to prepare a fully polished file as a result.
- Web interface testing automation
User experience design can also benefit from agentic AI automation testing. The goal of UI testing is to validate the visual design, functionality, usability, consistency, and performance of a web interface.
AI vision can further enhance the recognition of necessary locators, evaluating interfaces not from the human eye perspective, but from the digital view. With the help of test automation with AI, engineers can give such a tool a detailed HTML instruction with the legacy test scenario—and it will generate a similarly detailed test case that covers all the requirements.
With agentic AI, we can go beyond the surface level of UI testing automation and go straight to the elements that matter to software development engineers in test—accelerating test requirements and reducing operational expenses.
What makes agentic AI testing tools for UI such valuable assets for UI developers is their ability to make decisions that save computing loads, preserve more energy for optimization, and keep tests consistent with legacy data.
AI in test automation: how it works in UI testing?
Retrieval-augmented generation (RAG)
Accessing logs, historical data, and past test scenarios for more efficient test validation and checking results against established benchmarks.
Large file handling
Processing large volumes of files with legacy test scenarios for greater coverage and systematic test execution.
Test scenario storage
Preserving computational resources by storing successful text scenarios for further use or reference in similar cases.
Development-to-production acceleration
Reducing manual effort and securing timely updates through automated rapid testing.
Compliance support
Documenting every step and detail during testing progress for improved optimization and compliance.
- API testing
API testing automation used to be a challenge for testing engineers due to the limited number of tools with the necessary capabilities. More traditional tools proved to be too primitive and had basic functionalities that couldn’t deal with complicated test scenarios and authentication mechanisms—which, in turn, didn’t cover the needs of teams and did little to optimize time and effort. Aside from persistent test flakiness, API testing automation struggled with other complications:
Complicated scripts
Demanding script writing and maintenance. Expert intervention is required from beginning to end.
Complex APIs
Testing consistency is hard to maintain due to the complexity of modern authentication mechanisms and payloads.
Uneven test contribution
QA analysts and other team members without necessary technical expertise struggle to navigate the testing process and participate in creation.
Agentic AI for API automation testing resolves these issues by turning complex and script-heavy processes into more intuitive routines.
API testing comes with clear endpoints and structure, which makes it even easier to apply agentic AI for API automation testing. An LLM can generate and optimize code based on the endpoint description and requirements. This test case can be scaled and used as a baseline for negative and positive tests.
The technology’s ability to interpret plain text test scenarios into script offers a number of improvements to API testers, provides robust support to QA analysts, and takes the pressure off vetted engineers—letting the whole team contribute to test creation and ensuring perfect interactions between all app components.
Testing assistance
The emergence of agentic AI in automation testing potentially equipped every tester with an intelligent helper capable of much more than record-and-playback tools. With its conversational features, agentic AI became a strategic advisor and a reviewer for experienced engineers whenever they needed fast and relevant feedback on their code, chain of thoughts, or ideas.
- Personal code optimization
The context-awareness of LLM-based AI test automation tools expanded their arsenal of applications to something entirely new—intelligent assistants for engineers. In that capacity, AI in automation testing can collaborate with engineers by reviewing parts of their code and providing suggestions for improvement or filling in the missing gaps.
The speed at which AI processes information and its robust knowledge base generate relevant and actionable suggestions, which significantly empower decision-making and problem-solving.
There is such a technique as rubber duck debugging—when programmers imitate a conversation with an inanimate object (usually, a rubber duck), describing their code out loud, going through each moment step by step. This practice helps engineers analyze their code and identify potential mistakes or issues. In a way, AI in automation testing becomes an advanced version of rubber duck debugging because a programmer can now show their code to an LLM and get a round of reviews and suggestions depending on the context and instruction
- Test idea generation
Testing ideas are an important part of early software testing. Leveraging AI test automation tools allows experienced engineers to boost their brainstorming sessions. Such efficiency stems from agentic AI being able to analyze and compare previous data, compare suggested ideas to real numbers, and give engineers a comprehensive breakdown or write an approximate scenario. With AI working as a smart assistant—validating or evaluating test ideas—engineering teams gain more confidence in certain test scenario ideas or become able to identify potentially flawed ideas earlier.
Of course, you should remember that your goal here is not to make AI give you a correct answer. As an engineer, you need information and perspective—and AI agents provide it. They reduce hours and days of research to minutes, which helps you try out various ideas without compromising your schedule.
- Data processing
Data is the foundation of every software project—and of every stage within it. For instance, mismanagement of data is one of the most common causes of test automation failure. Therefore, the more information is available at every testing stage, and the more effort is invested in processing that information, the more predictable and satisfying the end results are. From consolidating sprint stories to evaluating risks, agentic AI in software testing automation acts as a helper—gathering raw data, mapping it, and converting it into the format necessary for the specific test scenario. Additionally, an LLM model can be trained to enhance control over the use of data during test creation, reducing the risk of human error.
AI in test automation: augmenting data management
Dependencies reduction
Generating mock data for increasing data reliance while ensuring independence from external systems.
Data collision prevention
Securing smooth parallel tests run by generating unique GUIDs, timestamps, and other identifiers.
Reliable test environment support
Ensuring test scripts create and clean up specific data before and after test execution.
AI in test automation: How will it impact jobs?
The immense benefits of test automation with generative AI also raise concerns about the role and impact of testing engineers in the process. At least 20% of testers anticipate that AI will overtake their duties entirely in the future. The US Department of Energy’s Oak Ridge National Laboratory also made a rather intimidating prediction about AI potentially replacing software engineers by 2040. Nevertheless, the latter study dates back to 2017, which prompts a good, detailed look at the AI journey so far.
So, what is the actual status of humans and AI in automation testing?
Despite the anxiety, the general feedback has been quite positive. The growing presence of artificial intelligence in testing automation isn’t the result of C-suite demand—it stems directly from the needs of teams.
There is a false assumption that AI in testing automation can be autonomous or that teams that use AI for QA automation no longer need senior engineers. Meanwhile, the truth is the exact opposite: AI in test automation only works when it’s used by experienced senior testing engineers who know their code and have enough expertise to identify potential errors or view AI suggestions from a critical perspective.
Nearly 76% of professionals from North America, Asia-Pacific, and Europe admitted to using AI-powered tools and QA with AI in their work because it allows them to optimize their time and provides them with more opportunities for growth.
Teams that removed their performance barriers were able to scale their quality of work by modernizing their workstyle—from tools and practices to testing. As a result, it allowed for greater confidence and provided more visibility into potential improvement areas.
Despite its obvious benefits and strong points, agentic AI testing tools can’t automate 100% of testing—and neither should they. The purpose of technology is not to be a replacement, but to enhance the work of your company’s professionals and provide them with more productive and opportunity-rich environments.
There is an argument that AI in software test automation covers a wide range of tasks usually performed by QA teams (document analysis, test case creation, and test execution), which should be a reason for concern.
QA with AI assists QA teams with transitioning these tasks from tedious and long-term activities into intuitive, data-rich, real-time operations. Even though agentic artificial intelligence can learn from patterns and make decisions based on its conclusions, it can’t strategize, imagine, or assume a fully human point of view—and without these abilities, no test will be a success.
At the end of the day, AI is not a separate entity, but a database powered by algorithms—it can quickly find necessary information, alert testers to inconsistencies, or imitate steps, but it lacks critical thinking and the ability to step outside programmed boundaries.
The biggest danger so far is not AI replacing testing engineers, but testing engineers becoming too comfortable with using AI. The key to professional growth is constant practice and dealing with challenges. However, when you have an assistant providing you with code suggestions and ideas, you need to maintain strict discipline in order to rely on your own knowledge and experience instead of continuously counting on AI to nudge you in the right direction.
AI in test automation: Future, trends, and adoption tips
Without a doubt, agentic AI is the future of test automation. It’s agile, convenient to work with, and it can be applied to a wider range of test scenarios. But to make the most out of this future, decision-makers should invest in human AI skills and practices as thoroughly as in the technology itself.
New developments and technological advancements will make software testing more important—and more challenging—than ever before. Testing engineers and QA teams are bracing for fast-paced environments, growing risks, and increasing software complexities, so any technology capable of shouldering that workload becomes a powerful asset. In the area of testing automation, agentic AI has all the capabilities to become a game-changer for software testers and QA analysts.
But with any new tool come new approaches and responsibilities—and the novelty of artificial intelligence in test automation will entail numerous changes in practices and routines.
How can testers and executives prepare?
- Ensure seamless communication
Transparency is the lifeblood of change—and it should be maintained before, during, and after adopting agentic AI automation tools. Testers need to be educated on how AI is going to assist them with their routines, QA analysts need to be onboarded for AI-assisted decision-making, and everyone needs to be consistently updated on guidelines and security frameworks. While this practice is rather simple, it can prevent numerous complications and issues, such as team resistance and misuse of AI technology. What is even more important, testing teams must be in consistent collaboration with data scientists who build and fine-tune AI models and implement AI tools. Direct communication between these experts will ensure that models are in touch with the goals, needs, and pain points of testing teams. - Adapt shift-left testing
When the pace is so fast, testers and QA professionals can’t afford to wait in a queue—they need to be involved in the process as early as possible. A recent test automation trend, shift-left testing, is a methodology that addresses numerous development issues—for example, untimely delivery of changes and failure to meet deadlines—by placing the testing process in the early stages of product development instead of testing an already finished solution. This approach to testing challenges the conventional linear view of product development and low QA team engagement. It also accelerates the entire product development process due to faster implementation of changes and early issue identification. - Adopt an AI-realistic mindset
Agentic AI in test automation is not a miracle solution. It is an addition to the existing toolbox and needs the right skills and the right environment to work properly. Executives who try to build new processes around the tools instead of building them around professionals end up sabotaging their digital transformation goals. To avoid that, they must acknowledge the flaws of AI and the necessity for human oversight in testing automation.
No matter how advanced, AI will always make mistakes. It doesn’t know your solution’s code better than the engineers who wrote that code. It doesn’t know your enterprise better than you do. Trusting it too much without checking will do the opposite of reducing time and cost because you’ll end up spending hours and resources on fixing errors. So, make sure you and your teams are always in control of the decision-making—ensure that all suggestions are always reviewed and verified by engineers and that final products are always refined and polished by human touch.
- Partner with trusted professionals
Lack of necessary AI skills remains a glaring issue and one of the most common AI adoption challenges. When it comes to implementing AI for test automation and ensuring its fit with a team’s needs, there is value in collaborating with external experts who can provide the perspective, insights, and guidelines needed to cover each milestone of the adoption process.
If you want to amplify your product development with AI tools, let’s chat! At Trinetix, you’ll have your unique requirements analyzed and explored with qualified software engineers and AI professionals, so you could maximize the value of innovation and see that every benefit of agentic AI makes the perfect fit for your testing and QA flows.
