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Discover how AI Agentic workflows are transforming test automation with AI agents, faster decisions, and real-time adaptability.
In today's digital world, speed and intelligence in your tech stack are just as important as in your people. As businesses look to automate complex processes and scale their operations, a new type of AI is rising to the rescue: Agentic AI. And Agentic AI works particularly well with workflows, including in your test automation. Let's dive into how AI agentic workflows work, how it's redefining software testing and business processes, and improving customer satisfaction.
Agentic AI is an intelligent system of bots (or AI agents) that can independently gather data, make decisions, and learn from the outcome, streamlining complex, multi-step processes. These specialized agents each have a very narrow scope that makes them hyper-precise in their role, and they work together to make up the larger Agentic AI network that works on complex tasks. Unlike traditional AI, or even language models, Agentic AI doesn't rely on human intervention or input to function. These AI-driven processes result in complete autonomy in both decision-making and task execution, which is exactly what makes these agent frameworks so powerful, propelling their use beyond repetitive tasks.
Unlike traditional automation that must follow a set pattern and rules (like those that use Robotic Process Automation), AI agentic workflows are agile, multi-agent systems with capabilities well beyond simple handling repetitive tasks. The intelligent agents that make up the system can make decisions, coordinate with other agents, and execute tasks without a human having to approve each action or wrestle with a complex task. There are several core components of these workflows that ensure the right outcome is produced each time.
It starts with AI agents gathering data from many different sources like APIs, user commands, external tools, environmental feedback, and more. On a self-driving car, this may look like sensor data and camera input, while in test automation, this information can come from extracting DOM elements, network logs, or test execution data.
Once all the data has been gathered, the agents take a collaborative approach, sharing and evaluating data with each other and working together to ensure the best problem-solving and multi-agent collaboration. In test automation, one agent might share expected functionality that was gathered from tester-input requirements, while another focuses on UI element behavior. In a self-driving car, a sensor might pick up a red light changing to green, but the camera sees a pedestrian still crossing the road. This agent-to-agent processing is a key component of what makes multi-agent systems so dynamic and adaptive.
Now it's time to make a decision. All the multi-agent collaboration from the previous step will be run up the ladder to the top of the multi-agent system. The agents in the self-driving car system will see the green light, but they will also know about the pedestrian. The decision is made: the car can't move, or it will hit the pedestrian. In software testing, the decision might be a little less dire, but it is still important. The system will know that the expected behavior is that a sign-up button will produce a confirmation message, but the actual result is that nothing happens.
Finally, it's time for action. In the case of the self-driving car, if the agentic system has taken the wheel (literally), the action might be a simple, routine task of waiting for the pedestrian to finish safely crossing. But if the system is in the backseat while the driver is in control, this may turn into a more complex goal, involving instant response time and activating the brakes if the driver takes off when the light changes. In the case of the broken button, finding the right action can be a more complex task. The correct response might be clicking a few different areas on the button. Maybe only the dead-center is picking up input, or maybe only the left half of the button is functioning. If that's the case, then the system can flag the error and continue the test instead of breaking altogether. But if the entire button doesn't work, then it's time to stop the test, send an alert, execute another test, or escalate the issue to a person (depending on the criticality of that button). These workflows can break down even the most complex test scenarios into manageable steps to ensure consistent and accurate execution.
One of AI's core components is its ability to learn and improve. It can, of course, learn from human input and review, but these workflows can also learn from the outcomes. Maybe the result successfully completed the workflow, but the outcome wasn't entirely desirable. If more input is needed, a new workflow can use a natural language model to write up the results, send it to their human overseer, and learn from their feedback. This continuous loop reinforces learning in real time and allows systems to get smarter with every run.
In addition to people not having to deal with strenuous or complex tasks, there are many benefits from implementing agentic workflows for your test automation that can have a long-term impact.
One of the great things about AI is that it never sleeps. Continuous Testing is made even easier by agentic workflows that are always on and decision-makers who don't need their coffee to get going in the morning. These autonomous systems reduce the amount of manual intervention required, leaving testers to focus on more human-oriented tasks, like exploratory testing. By catching bugs as fast as possible when they appear, customer satisfaction will skyrocket.
With each test execution, the AI agents gather and analyze data, enabling them to make increasingly informed decisions. The feedback and learning process allows for faster, more accurate, and data-driven decision-making. This also reduces the likelihood of errors.
AI agents can instantly adapt to new input, failures, or significant changes, keeping the testing process agile and responsive. For instance, instead of a traditional testing framework failing when a page takes too long to respond, a more intelligent workflow can factor in network activity and know what an acceptable response time looks like and when to raise a failure. This kind of real time adaptability is what makes Agentic AI such a powerful tool for modern testing teams.
Agentic workflows are intentionally modular to facilitate scalability. Each single agent is given a specific, focused function, making it easy to scale the system horizontally by simply adding more agents to handle increased demand. This makes it even simpler to add more workflows to handle the testing needs of large, complex applications. Even single agents can be replicated and repurposed across projects, providing reusable, powerful tools (and cost savings) that can be integrated into your broader strategic planning efforts.
AI agentic workflows are the next step in AI-powered test automation, creating intelligent, autonomous, and adaptive systems that can function without constant human oversight. For QA teams, this results in faster releases, tighter feedback loops, fewer bottlenecks, and a better user experience. While Agentic AI hasn't fully reached test automation, Virtuoso QA is closer than ever to autonomy. Chat with our expert team to both get a closer look at the future of test automation and see how AI can streamline your testing today.
