For years, automated testing has followed a simple model: define the steps, run them, and check the result. Agentic testing works differently. Teams give an AI agent a goal and let it figure out how to reach it, adapting as it interacts with the application.
Agents can already explore applications, generate test cases, and adjust tests when software changes. That raises a question: what should QA engineers focus on when AI handles more of the execution? The answer is all about defining what good testing looks like, spotting the risks that matter, and deciding whether the results can be trusted.
In this article, we’ll look at what agentic testing actually changes, where QA engineers still add value AI can’t replace, and what this shift means for building a QA team.
What Is Agentic Testing?
As mentioned, traditional automated tests follow a predefined sequence. That makes them predictable but also brittle. When an application changes, the test can break even if the feature still works, and it will never explore paths nobody thought to write down.
Agentic testing flips the script. The QA engineer defines an outcome, and the agent decides how to get there, observing the current state of the application, choosing an action, checking the result, and continuing until it reaches the goal or runs out of options.
What AI Agents Can Do in Testing
The AI Engineer newsletter breaks the process down into three core tasks.
- Explore: An agent can navigate an application and find flows worth testing, including paths a predefined suite would miss.
- Generate: Based on what it finds, an agent can draft test cases for review and inclusion in the suite.
- Repair: When a test fails because the application changed, an agent can look for the element or flow that now matches the original intent.
Repair is where the limits show up fastest. A test turning green doesn’t mean the application works correctly. An agent might fix a test by adapting it to what is actually a regression. It can find a new route to a goal, but it can’t independently judge whether that goal, or the resulting behavior, is the right one.
Automating execution doesn’t remove the need for someone to decide what should be tested and whether the result holds up.
AI for QA Testing Is Changing the QA Engineer’s Role
As AI takes over more test creation and maintenance, QA work is shifting from writing tests to oversight, strategy, and judgment.
Reviewing AI-Generated Tests
AI can produce a solid first draft of a test suite, but generated tests aren’t necessarily correct. They can rest on wrong assumptions and skip important edge cases. QA engineers now spend more time reviewing that output for missing scenarios, redundant tests, false positives, and flaky assertions. The job shifts from writing every test to reviewing, correcting, and extending what AI produces.
Focusing on Risk and Edge Cases
AI is strong at covering the obvious paths: common inputs, expected outcomes, and standard errors. Spotting what could go wrong in less predictable situations is harder.
For a payment system, an agent might generate tests for valid cards, invalid cards, and expired tokens. An experienced engineer will also ask what happens if the connection drops mid-transaction or if two processes hit at the same time.
Those questions come from knowing the system and its risks, which is exactly where QA expertise gets more valuable as AI handles the routine work.
Defining What Good Testing Looks Like
The bigger change is that QA engineers increasingly shape the strategy itself. This is a shift from test author to quality strategist, where senior QA professionals define prompts, set coverage requirements, and build fallback procedures for when AI misreads the system.
AI can produce testing material at a scale that’s hard to match, but QA engineers are still the ones deciding whether that testing means anything.
The Limits of Agentic Testing
More autonomy makes testing faster and broader, but not automatically better. AI agents can generate and run large numbers of tests while still missing important behaviors or misusing testing techniques.
More Tests Do Not Always Mean Better Coverage
Agents often struggled to use testing techniques well, even when told exactly how to use them. They produced tests that were technically valid but low value. In one example, agents wrote several tests around a feature with four data streams but fed them identical inputs, missing bugs that only showed up when the streams were handled differently.
The pattern held across approaches. Asking agents to use TDD produced more tests, not better correctness, and property-based testing got used superficially rather than pointed at the areas where it would actually help. Test volume and test quality are not the same thing, and a large suite can leave real risks untested if the agent never understood where those risks lived.
AI-Generated Tests Still Need a Reliable Quality Check
Someone still has to determine whether the test itself is correct. An agent can build an assertion on a wrong assumption or make a test pass by adapting to a change that’s actually a regression.
Self-healing tests carry this risk directly, as an agent may find a new element after a UI change without proving that element does the same job. Nondeterminism compounds it too. If a model judges whether a result is correct, the same test can get a different verdict on different runs, unlike a fixed assertion with a deterministic pass or fail.
That’s why the quality check can’t be delegated to the system that generated the test.
Read More: The Engineering Skills Behind AI-Ready Infrastructure
The Skills That Matter in Agentic QA
As AI takes on more testing tasks, QA value is shifting toward technical depth, analytical thinking, and product knowledge, and away from execution. This tracks a wider workforce trend Anthropic has documented, where AI automates specific tasks within a job rather than the job itself.
| Traditional QA Focus | Emerging QA Focus |
| Writing test cases | Designing test strategy |
| Manual test execution | Reviewing AI-driven testing |
| Maintaining test suites | Defining quality standards |
| Regression testing | Risk-based testing |
| Increase test coverage | Evaluating meaningful coverage |
| Test automation | AI-assisted and agentic testing |
| Finding defects | Identifying blind spots |
| Following requirements | Challenging assumptions |
None of this means every QA engineer needs to become a software engineer or AI specialist. It means having enough technical breadth to understand AI tools, evaluate their output, and work across engineering and product teams.
Test strategy and risk-based testing matter more once AI can generate hundreds of tests, because deciding which ones count becomes the real skill.
Exploratory testing and product context still separate a QA engineer from a test generator. AI works from code and known behaviors, while QA engineers understand how people actually use the product, chasing unexpected behavior and failures that could hit revenue, trust, compliance, or security.
AI-assisted testing and output evaluation round out the skill set: generating tests with AI, reviewing the output, catching errors, and refining the guardrails behind the system. The core skill is knowing both how to use AI for QA testing and how to recognize when it’s wrong.
CI/CD and observability pull QA closer to the engineering pipeline, as AI-generated tests become part of the pipeline and tools like logs, traces, and metrics help engineers tell a real defect from noise.
Building QA Teams for an Agentic Testing Environment
As testing becomes more AI-driven, the strongest QA hires combine testing expertise, automation, engineering fundamentals, AI fluency, and product context. That mirrors what Techunting’s research on AI-native engineers has found more broadly: problem-solving, adaptability, and curiosity matter alongside technical experience.
Deep testing experience still matters, since AI needs human judgment around risk and quality standards. It’s also worth evaluating:
- how candidates learn and adapt
- how they approach an unfamiliar system
- how they assess AI-generated work
- how they decide what to test next.
What teams want is QA professionals who can pick up new technology without losing the judgment that makes testing valuable.
Techunting helps companies find technology professionals with this mix of QA experience, automation skill, engineering knowledge, and AI fluency, whether that means strengthening an existing team or adding specialized talent as workflows evolve.
Our network spans more than 145 technologies and includes over 200 Techunters, with a 97% retention rate. Most searches produce a shortlist of vetted candidates within 3 days, and full squads are ready in 2 to 4 weeks.
QA Is Not Disappearing. The Work Is Changing.
Agentic testing is changing how software quality gets delivered, but it doesn’t remove the need for QA engineers. Agentic testing changes who does the work, not who owns quality.
Looking to build a QA team ready for the next stage of software testing? Contact Techunting.
Frequently Asked Questions About Agentic Testing and QA Engineering
As agentic testing becomes part of modern QA workflows, teams are also asking how it works, where AI can help, and what remains the responsibility of QA engineers. Here are answers to some of the most common questions about agentic testing and AI for QA testing.
1. What is agentic testing?
Agentic testing uses AI agents to test software based on goals rather than fixed sequences of steps. The agent can explore an application, choose actions, evaluate what happens, and adjust its approach as it works toward the testing goal.
2. How does agentic testing differ from test automation?
Traditional test automation follows predefined instructions. Agentic testing gives the AI a goal and allows it to determine the steps needed to achieve it, making the process more adaptable to changes in the application.
3. How is AI used for QA testing?
AI for QA testing can help generate test cases, explore applications, identify potential scenarios, maintain tests, and analyze results. QA engineers still need to review these outputs and determine whether they provide meaningful coverage.
4. Can AI agents generate and maintain software tests?
Yes. AI agents can generate tests based on their exploration of an application and attempt to repair tests when changes cause them to fail. However, a repaired test is not automatically a correct test, so human review remains important.
5. Will agentic testing replace QA engineers?
Agentic testing is more likely to change the QA engineer’s responsibilities than eliminate the role. AI can handle more repetitive testing tasks, while QA engineers focus on strategy, risk, edge cases, and evaluating whether test results can be trusted.
6. What skills do QA engineers need for AI-driven testing?
QA engineers increasingly need skills in test strategy, risk-based testing, exploratory testing, AI-assisted testing, AI output evaluation, automation, CI/CD, observability, and product context. The ability to combine deep QA expertise with broader technical skills is increasingly valuable.
7. What are the limitations of agentic testing?
AI agents can generate low-value or redundant tests, miss important risks, make incorrect assumptions, and adapt tests in ways that hide real regressions. More tests do not necessarily mean better coverage, which is why QA engineers remain essential for evaluating testing quality.