Agentic quality assurance: A guide to AI-driven QA

Quality assurance is changing. As software systems become more complicated and companies release updates faster, the old ways of testing software aren’t keeping up.

Agentic AI is helping quality assurance keep pace by allowing testing systems to make smart decisions on their own instead of relying only on fixed instructions.

In this guide, you’ll learn what agentic quality assurance means, why it is important, best practices, and how teams are using it in real-world situations.

After all, as Jensen Huang, CEO of NVIDIA, says, “AI agents are the new digital workforce…The IT department of every company is going to be the HR department of AI agents in the future.” (CES 2025 keynote).

What is agentic AI and its role in quality assurance?

TL;DR: Agentic AI uses autonomous systems that analyze applications, generate tests, and decide what to test based on goals and risk.

Agentic AI refers to AI systems that can work on their own to reach specific goals, make decisions, and change their approach without needing constant human guidance.

In quality assurance, this means AI systems can study how an application works, find missing test areas, create test cases, and prioritize based on risk and impact.

Unlike traditional automations that follow fixed scripts, agentic AI understands the situation and decides what to test, when to test it, and how deeply to check for possible problems.

Unlike traditional automations that follow fixed scripts, agentic AI understands the situation and decides what to test, when to test it, and how deeply to check for possible problems.

Agentic quality assurance vs traditional and AI-assisted quality assurance

TL;DR:

To understand where agentic QA belongs, you need to see how it is different from earlier approaches.

Traditional QA

Traditional quality assurance depends on people and running test cases by hand, where human testers plan scenarios, run the tests, and record the results. But even though it can be detailed, it takes a lot of time and does not scale well with today’s fast-moving software development.

AI-assisted QA

AI-assisted quality assurance uses machine learning to help with specific tasks like suggesting a line of code or helping identify a failing element, but it still works under human guidance and supports the existing process. The human remains the primary driver of every action.

Agentic QA

Agentic quality assurance works on its own by studying the code and environments, understanding how the application is built, creating and running tests, reviewing the results, and adjusting its approach—all with very little human help.

The main difference between AI-assisted QA and agentic QA is independence. That’s because agentic AI decides on its own what to test based on its goals, limits, and what it learns from the application or product.

Why agentic quality assurance matters

TL;DR: Agentic QA helps teams handle complex systems by prioritizing high-risk areas, adapting tests to changes, and reducing maintenance work.

Software is becoming so complex that it is harder for people to test it properly, especially with modern applications that use microservices, APIs, cloud systems, and continuous delivery pipelines.

Agentic quality assurance addresses this by

Scaling intelligently

Agentic AI can review large codebases and focus testing on the most important parts, like high-risk areas and sections that were recently changed.

Adapting to change

When code is updated, agentic AI reviews the risk again and automatically updates the tests to match.

Reducing maintenance burden

In cases of, say for instance, when the interface of an application changes, agentic AI understands how the application already works and will adjust the test on its own instead of making you fix a test that broke.

How agentic quality assurance works in practice

TL;DR: Agentic QA operates in a continuous loop where teams provide goals and the AI plans, executes, analyzes, and improves tests.

Agentic QA works in a continuous cycle, in which it receives input, plans what to test, runs the tests, learns, and delivers insights from the results.

Intent input phase

This is the stage in which the product manager, business analyst, or developer provides a clear testing goal—in simple, natural language—based on specific user stories or feature requirements.

Planning phase

After user input, the agent comes into play. It studies the code, structure, and requirements to find key parts and understand how they connect. Then, it spots areas that could cause problems.

Execution phase

In the execution phase, the agent creates and runs test cases in different environments. It then gathers the results, and if it finds something unusual, it automatically creates more tests to investigate further.

Adaptation phase

Finally, the agent reviews the test results. Then, the agent updates what it knows about the application. Next, the agent improves its testing approach. As a result, the agent repeats this cycle continuously as it adjusts to every change and deployment.

Agentic AI is changing the way teams handle quality assurance by making testing smarter and more independent.

Real-world applications of agentic AI in quality assurance

TL;DR: Agentic QA can automate regression testing, focus on high-risk changes, and help organizations release updates faster.

Agentic AI is changing the way teams handle quality assurance by making testing smarter and more independent.

Let’s see how this works via an example.

Use case: Accelerating regression testing in fast-moving ERP release

Problem

A manufacturing enterprise updated its ERP system biweekly but spent twenty-five or more hours per cycle on regression testing, delaying deployments and risking production issues.

Solution

They added agentic quality assurance to their CI pipeline using simple natural language instructions. That way, the AI could automatically create tests for new ERP workflows, focus on high-risk code changes, and fix unstable test elements on its own.

Outcome

As a result, regression testing dropped from over twenty-five hours to under eight hours.

More issues were caught before production, releases became more predictable, and the QA team had more time for strategic quality planning. (And to learn about faster, risk-based testing and how to increase risk coverage to 90%+, read more about Tricentis Tosca.)

Measuring the value of agentic quality assurance

Some of the key metrics teams should measure include

1. Test coverage: The percentage of code, features, and user flows that are tested automatically.
2. Defect escape rate: The number of issues that reach production compared to the number caught during testing.
3. Testing efficiency: How much time the agent, along with the QA engineers, spends creating, running, and maintaining tests.
4. Maintenance reduction: Teams using agentic AI can reduce manual test maintenance work by 30% to 50% within the first year.

Getting started with agentic AI in quality assurance

TL;DR: Organizations can start with small pilot projects, set clear goals, integrate existing tools, and gradually expand adoption.

Transitioning to agentic QA does not mean you have to throw away your current infrastructure and systems. Here are some ways you can get started at your own pace.

Define goals and scope

Set clear goals and decide what you want the agentic AI to achieve. Then begin with one focused area, like API testing, instead of trying to change everything at once.

Establish baselines

Set a starting point by recording your current test coverage, execution time, and defect rates so you can measure improvement later.

Integrate with existing tools

Connect agentic AI to your current CI/CD pipelines, test management tools, and quality platforms so it works together with the systems you already use.

Start small and iterate

Start with a small pilot project and let the agentic AI prove its value. After that, you can gradually expand its use across the organization based on what you learn.

Best practices and considerations for agentic QA

TL;DR: Teams should maintain human oversight, ensure data quality, monitor AI decisions, and apply governance controls.

To maximize the value of agentic quality assurance, you’ll want to keep some best practices in mind. Here are a few of those.

1. Maintain human oversight

Ensure the QA leaders and engineers regularly review the agentic AI’s testing strategies, validate its results, and give feedback to keep it aligned with the business goals.

2. Ensure data quality

Keep historical test results and defect records clean and accurate because the agentic AI relies on this data to make smarter testing decisions.

3. Monitor AI decisions and hallucinations

Monitor hallucinations by regularly reviewing and validating the agentic AI’s outputs to ensure its test cases and recommendations are accurate and reliable. If you don’t do this, things can get off the rails.

For instance, in one recent case, Summer Yue, a safety and alignment leader at Meta Superintelligence, reported that her email AI agent decided to start deleting her entire inbox, causing her to run to her computer to force quit everything.

This was a clear example that these agents’ outputs and actions must be carefully monitored.

4. Scale securely

Use model context protocol (MCP) to control how the agent accesses test data, workflows, and systems. Ensure the agent operates within approved permissions and governance policies.

Ensure the QA leaders and engineers regularly review the agentic AI’s testing strategies, validate its results, and give feedback to keep it aligned with the business goals.

How agentic technology strengthens quality assurance outcomes

Agentic AI helps QA engineers and developers manage complex applications with many services and frequent updates.

It is always analyzing code and test data to spot risk and adjust testing. As a result, teams release faster with more confidence, spend less time fixing tests, and more time on innovation.

How Tricentis supports agentic AI-driven quality assurance

TL;DR: Tricentis supports agentic AI-driven testing through tools like Tosca, qTest, and SeaLights.

Tricentis is leading the way in bringing agentic AI into enterprise testing through its AI-powered quality platform and MCP, making testing more autonomous and connected.

Tricentis solutions like Tosca, qTest, and SeaLights enable agentic AI to connect seamlessly with testing workflows, making quality assurance more independent, smarter, and more efficient.

Conclusion

TL;DR: Agentic QA enables autonomous and adaptive testing that improves software quality and delivery speed while still requiring human governance.

Agentic quality assurance is a way of testing software where agentic AI systems can plan, run, adjust, and improve tests on their own with very little human help.

This makes testing work better than traditional or AI-assisted quality assurance in today’s fast-moving and complex software systems.

When agentic AI is used with proper human oversight and connected to existing tools, it helps teams release software faster, improve quality, reduce maintenance work, and have more time for innovation.

Agentic quality assurance helps teams stay competitive as software systems continue to grow in complexity. Tools like Tricentis will always be an important part of how teams build and maintain reliable applications.

This post was written by Theophilus Onyejiaku. Theophilus has over 5 years of experience as a data scientist and a machine learning engineer. He has garnered expertise in the fields of Data Science, Machine Learning, Computer Vision, Deep learning, Object Detection, Model Development and Deployment. He has written well over 660+ articles in the aforementioned fields, python programming, data analytics, and so much more.