An agent, in the sense most vendors now use, is an AI system that works toward a goal by planning a sequence of actions and calling tools to carry them out. It can open an application, read a page, write a file, run a job and look at the result, then decide what to do next.
Applied to testing, that sounds like the whole job. Point an agent at an application, tell it to make sure everything works, and wait. Teams that have tried this tend to find two things. Agents are genuinely good at a lot of the work around testing. And the parts they are bad at are exactly the parts that make testing worth doing.
A test is a statement of belief
It helps to be precise about what a test is for. A passing test is a claim: this behavior, under these conditions, produced this expected result. The value of the claim depends on it being the same claim every time, checked the same way, against an expected result that someone agreed was correct.
Anything that changes the claim, or the way it is checked, changes what the organization believes about the quality of its software. That is the line agents should not cross on their own.
Where agents earn their place
Agents add the most value on work that is high in volume, easy to review and easy to undo.
- Drafting tests. Turning a process document, a recording or a requirement into readable test steps for a person to review
- Exploring. Crawling an application to find screens, fields and paths that no test touches yet
- Mapping change. Reading release content and proposing which modules and steps a feature touches
- Triaging failures. Grouping failed checks by likely cause and attaching the logs and screenshots that support it
- Proposing heals. Suggesting how a test should find a control that moved, with before and after evidence
- Summarizing. Writing the first draft of a run report for a person to edit and sign
In every case the output is a proposal. A person, or a transparent rule, decides what happens next. If the proposal is wrong, the cost is a few minutes of review.
Where agents should not act alone
Other tasks look similar but carry a different kind of risk. Here a wrong answer does not cost minutes. It silently changes what a passing result means.
- Interpreting steps at run time. If a model decides mid-run what "submit the order" means, two runs of the same test may do different things
- Changing expected results. Editing what a test checks so that it passes again hides the defect it was written to catch
- Approving its own work. An agent that proposes a heal and approves it has removed the only independent check
- Making release decisions. A go or no-go is an accountable call that needs a named person
- Working outside scope. Acting on environments, data or projects it was not given permission to touch
The split in one table
| Task | Agent's role | Who decides |
|---|---|---|
| Draft a new test | Writes steps from a source | Reviewer approves |
| Map a release feature | Proposes affected steps | Rules score, person confirms |
| Run a test | None at run time | Compiled plan, same every time |
| Locator heal | Proposes with evidence | Person who is not the author |
| Expected result change | May flag, never edits | Test owner with stronger review |
| Failure triage | Groups and explains | Engineer confirms cause |
| Release go or no-go | Drafts the summary | Named signer |
Three questions for any agent task
When a new use for an agent comes up, three questions sort it quickly.
- Can a person review the output in less time than it took to produce?
- Can the action be undone cleanly if the review finds a problem?
- Does the action change what a passing test claims, or how the claim is checked?
If the first two answers are yes and the third is no, the task is a good fit. If the third answer is yes, the agent can prepare the work but a person makes the change.
Keep the run deterministic
The single most important boundary is at run time. AI can do a great deal of useful work before a run and after it. During the run, the test should execute a fixed, approved plan. This keeps results comparable across runs and across releases, keeps cost predictable, and means a failure points to the application rather than to a model's interpretation on that day.
It also makes AI optional in a useful way. If the AI services are unavailable or switched off for a project, the suite still runs. Authoring slows down. Testing does not stop.
Give agents a clear interface
Agents need a defined way in. That means an interface where every action is named, checked against the agent's role and scope, and written to an audit trail. An agent with broad credentials and a browser is hard to govern. An agent that can call "propose heal", "run selection" and "read results" in one project, and nothing else, is easy to govern.
How Qventis One helps
Qventis One uses AI where it produces reviewable proposals, such as drafting tests in Studio and proposing heals with evidence, while release impact is scored by transparent rules. Agents can read step logs, screenshots and what each check expected and saw to help triage failures. Tests compile to a deterministic plan and run with zero AI tokens at run time. Agents connect through MCP, the CLI or the HTTP API under per-project policy, an agent can never approve its own change, and every action is audited. Book a demo to see where agents help and where people decide.