In 2026, AI coding agents write 40% of new code and it keeps growing as new models provide better quality and larger contexts.

Agents ship more changes than any human review process was sized for, so bringing AI into testing was a natural step in the process. This is how agentic QA appeared.

Definition

Agentic QA is a new approach to software testing where AI agents plan, create, run, and maintain tests based on a goal you give them, instead of following scripts written in advance.

An agent is not a tool that waits for instructions at every step. It receives an objective, such as verify that checkout still works after this change, and then decides on its own what to test, runs the tests, looks at the results, and adjusts.

The agent can change how it reaches the goal, but it cannot change the goal itself. A human sets the goal and reviews the outcome. The agent handles the steps in between.

How it compares to what you have today

Most teams today run some mix of manual testing and scripted automation. Let’s compare agentic QA to both.

Manual testing is flexible but slow. A person reads the change, decides what to check, and works through it by hand. Quality depends on attention and time.

Scripted automation is fast but rigid. The scripts only cover what someone wrote, and they break when the application changes. They require a high maintenance cost.

Agentic QA takes the judgment part of manual testing and the speed of automation.

The agent reads requirements or code changes the way a tester would, then generates and executes tests the way a pipeline would. When the application changes, it updates the tests itself instead of leaving a broken script for a human to fix.

It does not replace either of the older approaches completely. Exploratory testing, risk decisions, and judgment about what quality means for your product stay with people. The agent takes over the repetitive middle: writing routine cases, running them constantly, and sorting through the failures.

The agentic QA loop

A reliable agentic QA system runs in five steps, and it repeats on every change rather than once per release.

1. Plan. The agent reads the input. That can be a user story, an API spec, a pull request, the code, or a running instance of the application. From that it decides what needs testing and in what order.

2. Generate. The agent writes the tests it planned. Depending on the system, these come out as natural language steps, structured test cases, or executable scripts. In specific cases, it can include a data seeding step. At this step, a QA engineer can evaluate, adapt or change them.

3. Execute. The agent opens a browser and runs the tests against a live instance of the application, clicking through pages the way a user would. It watches the results through signals like the page structure, screenshots, network calls, and console logs. When something fails, it does not stop at a red mark. It classifies the failure: a real defect, a test that no longer matches the application, or noise from the environment. This step is where most of the saved time comes from, because setting up a live instance and executing the tests is a large share of what QA engineers do today.

4. Report. After a complete execution, the agent generates a report with all the findings. It can include screenshots, video recordings, and suspected code areas for the failed tests. Here again, a QA engineer can decide to open a bug or to approve a feature.

5. Adapt. When the application changed rather than broke, the agent repairs the affected tests and records what it learned. The next cycle starts from a slightly better position than the last one.

One practical note.

The agent can only reason about what it can observe, so it needs a running version of the application to test against. If several agents or runs share one staging environment, they interfere with each other and the results become hard to trust. It is just like having a team of QA engineers running tests on the same computer.

Plan for where these tests will run before you plan how many agents will run them.

GetWebstack creates live instances of the application under test, so that each agent runs its tests in isolation.

Try it now

Key takeaways

• Agentic QA means AI agents that plan, generate, run, and maintain tests from a goal, with humans setting the objectives and reviewing the results.
• Agents need a live, isolated environment to test against, and that requirement is worth planning early.
• It adds to your current process rather than replacing it.

Sources

• Sonar, State of Code 2025: developers surveyed estimate that 42 percent of the code they commit is AI-assisted.