Agentic test automation is a fundamental shift in how we test. Instead of depending on static, hand-written scripts that must be continually updated, agentic systems analyze apps, plan testing strategies, execute tests, and adapt to changing code—largely on their own. Agentic test automation In this blog post, we’ll look at agentic test automation. We’ll cover what it is, how it improves traditional test automation, the skills needed in order to move to the agentic world, how to navigate the pitfalls of agentic automation, and some of the tools that you can use. What is Agentic Test Automation? Agentic test automation is a type of software testing where AI (often powered by large language models) plans, executes, and adapts tests autonomously. Agentic test automation Unlike traditional automation that relies on static, hand-written scripts, agentic systems can understand context, analyze changes in real time, and decide what and how to test, all on their own. This often means broader test coverage, better and faster detection of defects, and less maintenance. Large Language Models (LLMs) play a big role here. LLMs can understand application context and user intent, interpret the purpose and meaning of different components, and focus on what’s most critical. This means they can not only help create and adapt tests, but they can also identify edge cases and scenarios that conventional automation will probably overlook. Agentic test automation can be seen as the pinnacle of the test automation spectrum: Manual scripts - require constant maintenance and breaking with UI changes. Relies on manually crafted scripts that are brittle and fail when applications change or evolve. These scripts require constant maintenance and updates whenever the user interface or application structure changes, making them time-consuming and expensive to maintain over time.
AI-assisted tools - intelligent locators and visual recognition that still need human oversight.  While AI-assisted tools can introduce intelligent locators, visual recognition, and reduced maintenance requirements to help identify elements more reliably and adapt to minor UI changes, they still require human oversight and predefined test cases, and don't fundamentally change the need for human-defined test strategies. (examples include Applitools Visual AI or Mabl)
Agentic automation - autonomously explores applications and discovers edge cases without constant oversight, as seen with platforms like Tricentis Tosca and qTest, which support scalable, agentic workflows with features like model-based automation and broad test management across environments. Manual scripts - require constant maintenance and breaking with UI changes. Relies on manually crafted scripts that are brittle and fail when applications change or evolve. These scripts require constant maintenance and updates whenever the user interface or application structure changes, making them time-consuming and expensive to maintain over time. Manual scripts AI-assisted tools - intelligent locators and visual recognition that still need human oversight.  While AI-assisted tools can introduce intelligent locators, visual recognition, and reduced maintenance requirements to help identify elements more reliably and adapt to minor UI changes, they still require human oversight and predefined test cases, and don't fundamentally change the need for human-defined test strategies. (examples include Applitools Visual AI or Mabl) AI-assisted tools Applitools Visual AI Mabl Agentic automation - autonomously explores applications and discovers edge cases without constant oversight, as seen with platforms like Tricentis Tosca and qTest, which support scalable, agentic workflows with features like model-based automation and broad test management across environments. Agentic automation Tricentis Tosca qTest However, agentic test automation is not a panacea just yet. You can think of agentic test automation as shifting QA focus away from people testing and to people giving oversight of independent and strategic AI agents. Skilled and thoughtful QA engineers are still needed for high-level oversight and to ensure the agentic automation operates effectively and within policy. Essential Skills for QA Engineers in an Agentic World So if the testing world is moving more towards agentic AI (with human oversight), what skills do QA engineers need to adapt? Prompt engineering: The ability to communicate clearly with agents becomes essential, as engineers need to articulate test objectives and quality criteria (through prompting) in ways that guide automated decision-making.
Strategic thinking: Rather than focusing on writing detailed test scripts, QA engineers should cultivate strategic thinking about test coverage. They need to understand which areas require attention and how to evaluate the comprehensiveness of agent-generated tests.
Model oversight: QA engineers will need to be active in oversight. They must evaluate when the AI’s reasoning is sound, catch false positives or hallucinations, and know when to step in.
Integrations: QA will be responsible for making sure the agents have access to context. Source control, CI/CD systems, and design documents are necessary to help agents understand when a test is truly a failure. Tools such as Tricentis’ Model Context Protocol (MCP) is an example of how teams can allow AI agents to directly interact with testing frameworks.
Accountability: When the final results come in, QA engineers will still be responsible for the results. They need to embrace accountability for all agent-generated tests, ensuring that results meet the same quality standards as human-created tests, even if they didn’t manually create them. Prompt engineering: The ability to communicate clearly with agents becomes essential, as engineers need to articulate test objectives and quality criteria (through prompting) in ways that guide automated decision-making. Prompt engineering: Strategic thinking: Rather than focusing on writing detailed test scripts, QA engineers should cultivate strategic thinking about test coverage. They need to understand which areas require attention and how to evaluate the comprehensiveness of agent-generated tests. Strategic thinking: Model oversight: QA engineers will need to be active in oversight. They must evaluate when the AI’s reasoning is sound, catch false positives or hallucinations, and know when to step in. Model oversight: Integrations: QA will be responsible for making sure the agents have access to context. Source control, CI/CD systems, and design documents are necessary to help agents understand when a test is truly a failure. Tools such as Tricentis’ Model Context Protocol (MCP) is an example of how teams can allow AI agents to directly interact with testing frameworks. Integrations: Tricentis’ Model Context Protocol (MCP) Accountability: When the final results come in, QA engineers will still be responsible for the results. They need to embrace accountability for all agent-generated tests, ensuring that results meet the same quality standards as human-created tests, even if they didn’t manually create them. Accountability: embrace accountability It’s also critical for QA professionals to stay current with emerging AI models and testing frameworks. While newer models are often faster and more cost-effective, stability and alignment with company workflows matter more than novelty. QA engineers will need to understand and implement this balance. AI models and testing frameworks Navigating the Pitfalls of Agentic Test Automation Of course, as with all new technologies, agentic test automation comes with pitfalls. Trust calibration is key. QA engineers must have robust verification protocols to ensure accuracy and reliability, especially in the beginning when engineers are still fine-tuning agent prompts.
In the early learning phases, agents may frequently generate false positives. QA engineers will need to provide careful oversight and tuning to avoid wasted effort.
QA maintenance duties will shift from labor-intensive script updates to configuring agent parameters and guardrails. This is a challenge, but it can be made easier by platforms like Tricentis Tosca/ qTest or Applitools Execution Cloud (focused on self-healing infrastructure), both of which simplify maintenance with built-in agentic workflow controls and model-based automation.
Even as agents take on more testing responsibilities, human-in-the-loop validation remains vital for safeguarding critical workflows and ensuring decisions align with enterprise priorities.
Flaky tests are the bane of high-quality testing. Agentic test automation can quickly generate a lot of tests. If some of those are flaky, the value is greatly diminished, productivity will drop, and trust in the overall QA process will fall. Make sure to help the LLM weed out flaky tests.
Finally, agentic AI systems might generate many low-value tests with coverage overlap. And this can be quite expensive both in time and money. As part of human oversight, QA engineers will need to carefully monitor, and budget, their tests. Trust calibration is key. QA engineers must have robust verification protocols to ensure accuracy and reliability, especially in the beginning when engineers are still fine-tuning agent prompts. Trust calibration In the early learning phases, agents may frequently generate false positives. QA engineers will need to provide careful oversight and tuning to avoid wasted effort. false positives QA maintenance duties will shift from labor-intensive script updates to configuring agent parameters and guardrails. This is a challenge, but it can be made easier by platforms like Tricentis Tosca/ qTest or Applitools Execution Cloud (focused on self-healing infrastructure), both of which simplify maintenance with built-in agentic workflow controls and model-based automation. configuring agent parameters and guardrails Even as agents take on more testing responsibilities, human-in-the-loop validation remains vital for safeguarding critical workflows and ensuring decisions align with enterprise priorities. human-in-the-loop validation Flaky tests are the bane of high-quality testing. Agentic test automation can quickly generate a lot of tests. If some of those are flaky, the value is greatly diminished, productivity will drop, and trust in the overall QA process will fall. Make sure to help the LLM weed out flaky tests. Flaky tests Finally, agentic AI systems might generate many low-value tests with coverage overlap. And this can be quite expensive both in time and money. As part of human oversight, QA engineers will need to carefully monitor, and budget, their tests. low-value tests with coverage overlap Getting Started: Practical First Steps Getting started with agentic test automation is best approached incrementally. First, begin by experimenting with low-risk regression suites or exploratory tests in non-production environments. This allows teams to validate agentic outputs alongside legacy automation.
It’s also wise to constrain the agent’s initial autonomy to specific features or flows, making oversight manageable and learning outcomes clear.
Finally, take full advantage of agentic test automation and incorporate automatic root cause analysis when tests fail. First, begin by experimenting with low-risk regression suites or exploratory tests in non-production environments. This allows teams to validate agentic outputs alongside legacy automation. experimenting with low-risk regression suites It’s also wise to constrain the agent’s initial autonomy to specific features or flows, making oversight manageable and learning outcomes clear. constrain the agent’s initial autonomy to specific features or flows Finally, take full advantage of agentic test automation and incorporate automatic root cause analysis when tests fail. incorporate automatic root cause analysis Agentic Testing with Tools/Platforms Agentic testing can be wildly successful for metrics—and can be made easy to implement with the right tools. Platforms such as Tricentis Tosca and Mabl have already shown strong results in improving key metrics. For example Tricentis (which can automatically generate test cases using agentic AI and natural language prompts) has shown up to a 85% reduction in test creation and 60% increase in productivity by automating complex regression suites across thousands of test cases with agentic orchestration. Tricentis Tosca and Mabl have already shown strong results in improving key metrics But when using these tools, QA engineers should continually compare new agent-generated results with existing baselines to be sure their tests are accurate and on-task. Use success metrics like expanded coverage, higher bug detection rates, and reduced maintenance time to guide adaptation. A structured and monitored onboarding process helps QA teams build confidence, understand limitations, and embrace agentic automation and the right tools. continually compare new agent-generated results with existing baselines Conclusion Agentic test automation marks a transformative leap for QA teams, shifting the focus from manual scripting and maintenance to strategic oversight and collaboration with AI agents. By embracing new skills, teams can unlock better test coverage, improved metrics, and streamlined workflows. And as agentic systems mature, QA teams that embrace and prepare for the shift will move successfully from manual work to orchestrating AI. Have a really great day!

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Demystifying Agentic Test Automation for QA Teams

Demystifying Agentic Test Automation for QA Teams

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