AI testing and agentic testing
AI testing applies artificial intelligence (AI) to automate software, applications, and system testing, significantly improving coverage, speed, and accuracy. Agentic testing not only applies AI, but also leverages AI agents to generate, execute, and adapt tests to enable complete, end-to-end testing across the full testing lifecycle.
- Transforming software testing with AI and agents
- Summary
- An AI-led transformation in testing
- Spotlight on AI testing
- What is AI testing?
- What capabilities does AI testing include?
- A deeper dive into agentic testing
- What is agentic testing?
- What capabilities does agentic testing include?
- What are the key benefits of agentic testing?
- How does agentic testing work?
- What has agentic testing newly enabled…or significantly enhanced?
- What are some common agentic testing use cases?
- What are best practices for implementing agentic testing?
- What does the future of agentic testing hold?
- FAQ
Transforming software testing with AI and agents
Traditional testing has struggled to keep up with expanding digital ecosystems and faster software release cycles. But two major advances—first, AI testing, and more recently, agentic testing—have transformed software quality assurance.
AI testing uses machine learning and other forms of artificial intelligence to automate time-consuming and laborious testing tasks like test design, test execution, and script repair. Using AI in this way augments testers’ efforts, helping them work more efficiently and effectively. AI-based testing approaches reduce testing costs and cycle time while expanding test coverage.
Agentic testing is the next leap forward in AI-powered testing. It uses AI agents to plan, run, and refine tests, as well as to diagnose root causes, suggest solutions, and apply fixes automatically. This approach enables continuous testing and improvement across the entirety of the enterprise software ecosystem.
Together, AI testing and agentic testing have transformed traditional testing’s manual, scripted, code-based process into an intelligent, adaptive, and continuous one that meets modern enterprises’ needs for faster, more complete, and more responsive software QA.
Summary
AI testing uses machine learning and other artificial intelligence to automate key parts of the testing process.
Enables AI-generated test cases, self-healing of scripts, and the prioritization of tests based on UI or code changes
Speeds up testing cycles, reduces costs, and improves test coverage and accuracy
Augments testers’ capacity with intelligent assistants for improved design, execution, and analysis of tests
Agentic testing uses autonomous agents for end-to-end software QA.
Puts autonomous AI agents to work generating, executing, and adapting tests—no manual scripting required
Adapts test logic in real time to changes in the UI, APIs, or workflows, reducing maintenance and test breakage
Enables tests to align with user intent, leveraging agents’ ability to reason about user behavior
Speeds up and reduces risks associated with large-scale testing across complex systems like ERP platforms or dynamic AI applications
Elevates testers’ role from authoring tests to providing oversight, analysis, and agent guidance
An AI-led transformation in testing
From the old manual and scripted paradigm, testing has rapidly evolved: first to an automated, AI-assisted approach, and now, to an agent-powered, autonomous capability. Each leap forward has advanced testing’s speed, coverage, and resilience.
Traditional testing
Human testers design, execute, and evaluate test cases manually. Scripts and data are handcrafted. Regression testing is repetitive and slow. Testers do most of the work—writing scripts, executing, maintaining, and updating tests, and analyzing results.
Limited coverage, slower feedback
Significant effort expended to update tests when software changes
Manual testing struggles to keep up with rapid software expansion and faster change cycles
AI testing
Humans remain in control, but AI-powered test suites support the process. ML models generate test cases, heal broken scripts, analyze results, and suggest priorities.
AI augments testers, helping to create, maintain, and optimize tests.
Automation tools make running tests and analyzing results faster and simpler
Predictive analytics identifies risky areas
Self-healing automation reduces maintenance
Testing becomes faster, smarter, and more complete by augmenting testers’ efforts through AI and automation
Agentic testing
AI agents plan, execute, and refine tests with minimal human input. Agents collaborate, learn from outcomes, and adapt tests in real time. AI agents act as testers.
Agents explore new paths and scenarios automatically
Specialized agents (for planning, executing, evaluating, and repairing) work together
Continuous testing occurs across complex systems
Testing becomes self-improving, always on, and adaptive to change by bringing in virtual testers operating across the entire testing lifecycle
Spotlight on AI testing
AI testing augments testers’ capacity and speeds testing time by providing AI-powered automation tools to transform manual testing processes.
What is AI testing?
AI testing applies artificial intelligence and machine learning to the software testing process, making it possible to automate what formerly required extensive manual effort. It helps QA teams design, execute, and maintain tests faster and more intelligently, improving coverage and reliability.
Unlike traditional scripted testing, AI testing can identify patterns, generate test cases automatically, and adapt tests as applications change. By learning from code, user behavior, and past test results, AI testing accelerates software quality assurance and reduces the human effort required to keep pace with continuous delivery.
What capabilities does AI testing include?
Advanced AI testing technologies assist with test design, maintenance, and analysis.
AI-assisted test design
AI testing tools can automatically generate and update test cases from requirements, user stories, or application data to keep tests aligned and current with evolving test criteria.
AI-enabled automation and maintenance
Machine learning enables capabilities like self-healing scripts, dynamic test prioritization, synthetic data generation, and visual validation.
AI-driven insights and optimization
Testing tools can analyze large volumes of test results to detect patterns, predict defects, and identify high-risk areas.
A deeper dive into agentic testing
While AI testing added capacity and coverage, agentic testing has multiplied it many times over. Below, we take a closer look at what it is, how it works, what it can deliver, and how testing teams can unleash its power.
What is agentic testing?
Agentic testing is a cutting-edge testing approach that leverages AI-powered agents to independently analyze, execute, and refine testing workflows. Unlike traditional AI testing, which focuses on automating specific tasks, agentic testing usess AI agents to actively engage with software—learning, adapting, and responding to real-world scenarios instantaneously.
By integrating large language models (LLMs) like ChatGPT and generative AI (GenAI), agentic testing can rapidly simulate diverse real-world conditions and evaluate applications with greater intelligence and adaptability.
What capabilities does agentic testing include?
AI technologies typically include these broad capabilities:
Agentic test design
AI agents can complete many test design tasks, including generating manual tests from requirements such as user stories and transactions. In addition, AI agents can assess quality-checking requirements to ensure clarity, completeness, consistency, and other quality measures.
Agentic test automation
Leading agentic testing technologies enable users to convert manual tests into coded, low-code, or even no-code automated UI or API tests. Additional capabilities include support for generating test data, refactoring test automation, fixing validation errors, generating expressions, performing fuzzy verifications, auto-healing tests during run time, and more.
Agentic test management
The best agentic testing technologies typically will support test management by providing users with real-time, actionable insights into test results. Additionally, they will allow users to apply important manual test cases from external software such as Microsoft Excel, and to search test projects using natural language.
Agentic testing allows testers to focus on shaping intelligent systems rather than writing scripts.
What are the key benefits of agentic testing?
Agentic testing brings significant benefits to a wide range of groups within an organization.
For QA teams, test engineers, and QA leaders
Agentic testing significantly improves the reach, productivity, and capacity of the testing function, transforming testing from a functional necessity into a source of strategic advantage.
With agents taking on laborious and repetitive tasks like test case creation and execution, testers now have the time and headspace to leverage their critical thinking skills, creativity, and problem-solving capabilities, while keeping pace with today’s rapid, unceasing release cadences. They can focus on solving higher-value problems, developing new testing strategies, and expanding coverage to include critical edge cases. Enterprise resilience gets a boost, with faster response and continuous improvement.
At the same time, agentic testing simplifies the testing environment itself. By consolidating fragmented tools and integrating directly with existing DevOps systems, agentic testing eliminates the inefficiencies of tool sprawl. Testing teams gain a unified workspace for managing tests, data, and analytics, reducing overhead and making it easier to deliver high-quality applications faster.
For software engineering and DevOps teams
Agentic testing strengthens the entire software delivery pipeline by making quality assurance proactive, adaptive, and continuous. Autonomous test agents validate applications in real time, reducing operational risk and improving release confidence.
By continuously monitoring changes in workflows, APIs, and application logic, agentic testing identifies vulnerabilities and performance issues early—long before deployment. Intelligent, self-healing test scripts automatically adjust to UI or code updates, preserving stability across environments and minimizing maintenance effort.
Real-time test execution provides instant feedback to developers, accelerating defect resolution, and shortening release cycles. For complex enterprise systems like SAP, Salesforce, and Workday, these autonomous capabilities ensure that even intricate, business-critical workflows are thoroughly validated and ready for deployment.
For DevOps teams, agentic testing turns quality assurance into a continuous process rather than a final checkpoint. It can deliver faster, safer releases through automated validation and feedback loops integrated directly into CI/CD pipelines.
For IT leaders
By turning quality assurance into a continuous, self-adapting process, agentic testing helps IT leaders ensure consistent quality across systems, proactively manage operational risk, and strengthen governance. It identifies potential failures early, prevents service disruptions, and maintains business continuity—building enterprise resilience into the software delivery lifecycle. Continuous, AI-driven testing provides real-time visibility into software health, enabling faster, data-driven decisions about deployment readiness while improving return on investment across the technology stack.
How does agentic testing work?
1. Automated test case generation
Test case creation is one of the most time-consuming and error-prone aspects of traditional testing. But now, instead of spending countless hours creating test cases, agentic AI systems leverage the power of artificial intelligence—including foundational LLMs like OpenAI’s ChatGPT and machine learning algorithms—to do the heavy lifting.
These AI agents analyze an application's requirements and automatically generate comprehensive test cases, covering even the most intricate workflows and unexpected edge cases. Testers now have the capacity to focus on higher-value tasks like analyzing results and refining the application itself. This end-to-end approach streamlines the entire testing process, boosting operational efficiency and ensuring thorough coverage for even the most complex software ecosystems.
2. Fuzzy verifications
Traditional testing often relies on binary pass/fail outcomes, which don’t account for nuanced scenarios in real-world environments. Agentic AI systems go beyond this binary approach with "fuzzy verifications." This means they can assess outputs not just for technical correctness, but also for their accuracy and relevance within the specific context.
This is crucial for testing AI applications where flexibility and nuanced understanding are key. For example, if an AI-powered chatbot provides a slightly different answer than expected, fuzzy verification can determine if the response is still accurate and helpful within the given context. This approach helps organizations identify subtle vulnerabilities in outputs, leading to better decision making and continuous optimization of their applications.
3. Self-healing test scripts
Frequent updates and changes to applications often disrupt traditional testing processes, requiring constant manual intervention to update test scripts. With agentic testing, that's a thing of the past. AI agents automatically detect changes in an application—such as updates to APIs or modifications to the user interface—and automatically adjust the test scripts accordingly.
This self-healing capability ensures seamless process automation and minimizes the time and effort required for test maintenance. Testers can focus on more strategic tasks, knowing their test scripts are always up-to-date and ready to go.
4. Dataset integration and evaluation
Whether it's customer data, market trends, or sensor readings, data is the lifeblood of many applications. Agentic testing recognizes this and places a strong emphasis on validating how applications interact with these datasets.
AI tools meticulously evaluate datasets for biases, anomalies, and vulnerabilities, ensuring they meet the highest standards for accuracy and integrity. For example, healthcare applications that process sensitive patient data require rigorous testing to ensure compliance with regulations and minimize risks. Agentic testing helps ensure these applications handle sensitive data with precision and reliability.
5. Continuous learning and optimization
Agentic testing offers a unique and powerful ability for ongoing assessment and improvement of tests. AI agents continuously analyze test outcomes, detect recurring patterns, and refine test logic based on previous results and real-world performance. Over time, this creates a self-improving test cycle that autonomously enhances tests’ coverage, accuracy, and resilience.
What has agentic testing newly enabled…or significantly enhanced?
Agentic testing brings testing into a new era of intelligent autonomy, where AI agents plan, execute, and refine tests across complex systems with minimal human effort. Some notable leaps forward:
Automation of the full test lifecycle
Agentic testing fulfills the long-standing goal of continuous, autonomous quality assurance. Beyond automating individual steps, AI agents plan, prioritize, execute, and refine tests in a closed-loop cycle. They learn from each run, analyzing results, adapting strategies, and optimizing for greater speed and precision. The result is an evolving system that continuously improves coverage and quality with minimal manual input.
Scalability for diverse applications
Agentic systems scale not only in capacity but also in intelligence. They understand the specific requirements of different domains, from regulated financial platforms to dynamic AI applications, and adapt testing methods accordingly. This domain sensitivity allows consistent, high-quality results across small internal tools and globally distributed enterprise systems alike.
Adaptive learning and self-optimization
Each test cycle feeds data back into the agent network, allowing models to recognize patterns, predict issues, and autonomously adjust testing logic. Over time, this continuous learning builds a smarter testing ecosystem that preempts failures and improves both accuracy and efficiency.
Autonomous orchestration and governance
Agentic testing enables collaborative, multi-agent orchestration in which agents delegate, synchronize, and execute tests in parallel for faster outcomes. Enterprise-ready guardrails ensure that this autonomy operates transparently, with built-in auditability and governance so teams can track and trust every agent action.
Comprehensive validations across ecosystems
Modern enterprise environments are interconnected by APIs, data pipelines, and automation workflows. Agentic testing coordinates multiple specialized AI agents to validate end-to-end behavior across these systems, ensuring that integrations remain stable and that performance, data flow, and business logic stay aligned across the full digital ecosystem.
What are some common agentic testing use cases?
- Enterprise applications
- E-commerce
- Finance
Ensuring continuous high performance of enterprise applications
Enterprise systems like SAP, Salesforce, Workday, and Oracle power mission-critical operations, but also present significant testing challenges due to their complexity and frequent updates. Agentic testing automates the entire testing lifecycle, from generating test cases to verifying outputs, ensuring these systems perform seamlessly across real-world scenarios.
For example, agentic testing can automatically create test cases from application requirements, significantly reducing the time and effort involved in manual test creation. AI-powered agents adapt to system changes, self-healing scripts as updates are rolled out.
Optimizing customer experiences in e-commerce
In e-commerce, customer experience is everything. Every interaction, from browsing to checkout, needs to be seamless and enjoyable. Agentic testing helps ensure this by rigorously evaluating every element of your e-commerce platform.
Imagine a chatbot struggling to keep up with a surge in customer inquiries during a major sale. Agentic testing prevents this by validating its performance, ensuring it can handle real-time interactions with thousands of customers simultaneously. Furthermore, it evaluates the effectiveness of personalization engines, ensuring they deliver accurate and relevant product recommendations that drive conversions.
Ensuring security and compliance in finance
Applications that handle sensitive financial data, from fraud detection to risk assessment, must operate flawlessly. Agentic testing provides the level of accuracy and reliability that the finance industry demands.
AI testing simulates real-world vulnerabilities to make sure fraud detection algorithms are bulletproof. It also performs fuzzy verifications to validate the accuracy and compliance of outputs, particularly for regulatory reporting and risk management workflows. This approach minimizes vulnerabilities, enhances operational efficiency, and ensures that financial applications deliver secure and trustworthy results.
What are best practices for implementing agentic testing?
Start testing early in development
It’s more effective to embed agentic testing into the earliest stages of software development. This proactive approach aligns well with agile and DevOps methodologies, ensuring testing is an integral part of the development journey, not an isolated task. For example, when deploying enterprise applications like SAP or Salesforce, early integration allows businesses to validate workflows and decision-making processes as they evolve. This not only accelerates time-to-market but also reduces the need for costly and time-consuming rework later in the development cycle.
Take advantage of APIs and open-source tools
Why reinvent the wheel when you can use tools already out there? Integrating APIs and open-source platforms into agentic testing makes the process faster and more efficient. APIs enable seamless connections between agentic AI systems and existing application ecosystems, while open-source tools provide ready-to-use functionalities that cut down development time. And because these tools are flexible, they can be customized to fit businesses' specific needs, yielding more control over the testing process.
Keep monitoring and validating
Enterprise applications don’t exist in a vacuum—they operate in dynamic environments where inputs and workflows are constantly changing. That’s why continuous monitoring is essential. It helps detect performance drifts early, so businesses can fix issues before they escalate. Real-time validation offers instant feedback, highlighting areas that need tweaking.
Build collaboration into the process
Agentic testing isn’t a one-person job. It requires human oversight and input from developers, testers, data scientists, and even end users. When everyone works together, the testing process becomes more comprehensive, covering every angle from technical functionality to real-world usability. Collaboration also fosters accountability, ensuring that everyone stays aligned and invested in the outcome. Plus, fresh perspectives from different teams often lead to better solutions.
Set clear goals and measure progress
Testing without clear objectives is like setting off on a journey without a map—you might get somewhere, but it’s probably not where you want to be. Defining what success looks like, whether it’s hitting a specific accuracy rate or improving response times, gives teams clear targets to aim for. Tracking progress with measurable metrics ensures testers can see what’s working and what needs adjustment. Aligning these goals with broader business priorities helps ensure testing efforts have real impact.
What does the future of agentic testing hold?
The world of artificial intelligence is evolving at breakneck speed, and agentic testing is evolving right alongside it. As AI agent systems become more advanced and woven into critical workflows, testing tools and methodologies are rising to meet these new challenges.
Keeping up with next-generation AI models
Next-gen AI technologies like advanced LLMs and generative AI are rewriting the rules of what’s possible. However, they also demand smarter ways to validate their performance and functionality. Agentic testing will evolve to tackle these challenges head-on. Future tools will focus on testing models dynamically, ensuring they can produce context-aware, nuanced outputs across different use cases. With AI systems increasingly influencing real-world decisions, addressing bias will also be a top priority, equipping testing frameworks to identify and correct imbalances for more ethical and fair outcomes.
Pushing the limits of automation
Process automation has always been a cornerstone of agentic testing, but the future promises even more power and precision. Testing algorithms will become smarter and more accurate, reducing errors and delivering results faster. Adaptive testing tools will respond to evolving datasets and system updates automatically, saving teams from tedious manual adjustments.
Building trust in the future of AI
Agentic testing is poised to become the gold standard for quality assurance. By tackling the unique demands of next-gen models, embracing ecosystem complexity, and advancing automation, it will remain a critical part of how businesses build and deploy AI solutions. The ultimate goal isn’t just ensuring that applications and software work—it’s about making sure they perform ethically, reliably, and at their best, no matter what the future holds.
As testing grows more complex, agentic testing offers a scalable, adaptive model that keeps pace with modern development.
FAQ
Q: What is AI testing?
A: AI testing uses artificial intelligence to augment test teams’ capacity and coverage by automating and optimizing many of the laborious parts of the software testing process.
Q: How does AI testing improve traditional automation?
A: Traditional automation executes predefined scripts that must be manually updated when systems change. AI testing introduces adaptive capabilities that can automatically generate test cases, identify and repair broken scripts, prioritize tests based on risk, and create synthetic data for complex scenarios. These capabilities reduce maintenance effort and improve reliability.
Q: What is agentic testing?
A: It’s a new form of testing that uses autonomous AI agents to design and execute software tests.
Q: How does AI testing relate to agentic testing?
A: While AI testing automates and enhances specific tasks, agentic testing introduces autonomous agents that can plan, execute, and refine tests independently, making testing more adaptive and continuous.
Q: How is agentic testing different from traditional test automation?
A: Traditional automation runs predefined scripts. Agentic testing allows agents to generate and adjust tests in real time.
Q: What are the main benefits of agentic testing?
A: For testing teams and IT leaders, it expands capacity, strengthens resilience, reduces operational risk, and ensures consistent quality across systems. For development and DevOps teams, agentic testing accelerates delivery with continuous validation and faster feedback loops.
Q: Why does agentic testing work so effectively in today’s high-change environments?
A: Agents have the ability to detect UI or API changes automatically and immediately apply self-healing logic to keep tests from breaking.
Q: Can agentic testing be used with large enterprise systems?
A: Modern agentic testing capabilities are designed for complex, high-change systems like SAP, Salesforce, Workday, and modern cloud apps.
Q: Is agentic testing only for AI- or LLM-based apps?
A: No—while well-suited for dynamic AI apps, it also strengthens testing across any application that benefits from continuous, adaptive QA.
Q: Does UiPath offer agentic testing capabilities?
A: Yes. UiPath provides agentic testing capabilities through UiPath Test Cloud.
Q: Do I need to replace my current test tools to use UiPath Test Cloud?
A: No, Test Cloud supports agentic testing natively and integrates with your existing DevOps ecosystem.