What an AI testing agent does differently
Derives coverage from your product. From a specification, a requirements document, or by exploring the running application, rather than from what someone remembered to write.
Expresses steps as intentions. "Open the settings page" rather than a path through the DOM, which is why cosmetic redesigns usually do not break it.
Returns a failure the fixer can use. What was attempted, what happened, where they diverged, in a form another agent can act on.
Failure mode one: satisfying the check
Asked to make a test pass, an agent may take the shortest path, and adjusting the check is sometimes shorter than fixing the product. This is not malice, it is the objective being underspecified.
The guard is cheap. Name a specific observable in every check, and break something deliberately to confirm the check can go red. A check that cannot fail is not protecting anything.
Failure mode two: confident coverage nobody read
An agent will happily produce two hundred cases. Volume looks like progress and coverage nobody reviewed is coverage nobody can rely on, because you do not know what it asserts.
Read the plan rather than the code. Cut the administrative routes, add the product rules that are not written anywhere, and treat it like a pull request.
What it still cannot do
Know your business rules
What should happen when two discounts apply is a decision, not a derivation.
Invent the abuse case
It will check authorization. It will not think of the workflow someone could game.
Fix a broken environment
Flakiness from infrastructure stays flakiness.
Reviewing a plan efficiently
The main ongoing cost of working this way is reading coverage you did not write, and doing it badly is what produces the two failure modes above. There is a fast method.
Read only the assertions. Skip the steps entirely on the first pass, because steps are mechanical and assertions are where the judgement lives. Any assertion that would be true of a broken product is the thing to fix, and they are easy to spot once you are looking only at them.
Then scan the list for what is missing rather than what is there. Generated plans are reliably complete on the endpoints that exist and reliably silent on the rules that live in somebody's head. Adding three of those is worth more than correcting thirty steps.
Getting started
Terminal
npm install -g @testsprite/testsprite-cli
testsprite setup
The same setup is available in the TestSprite dashboard if you would rather not install anything locally. The rest of the CLI surface is in the CLI repository.
The trigger matters more than the mechanism. Pointing it at your deployment event means every change is checked without anyone deciding to; the GitHub App does that from the dashboard, and a GitHub Actions step does it from inside your workflow.
What TestSprite does as an agent
It derives coverage from your sources and the running application, expresses steps as intentions so cosmetic change does not break them, runs against the deployed app, and returns a failure as what was attempted, what happened and where those diverged.
Setup installs the verification skill into the coding agent you already use, so all of that happens inside the loop where the code is being written rather than as a separate step someone remembers.
The value is the loop closing. A change gets checked against the running product, a failure comes back in a form the agent can act on, and the fix gets confirmed by something other than the reasoning that produced it. What stays with you is reading the plan and deciding what correct means, which is an hour a week rather than a role.
How is this different from a test generator?
A generator produces code you then run and maintain. An agent also runs it, reads the result and can iterate, which is what closes the loop.
Can it work without a specification?
Yes, by exploring the running application, though a specification or requirements document produces a better first plan.
How much review does it need?
Read the plan before the first run and after any large regeneration. Between those, review new cases like you would review code.
What stops it running up costs?
Runs consume credits, so set expectations before a long session. An agent with a verification tool will use it, which is the point and worth budgeting for.
Does it replace a QA engineer?
It replaces the repetitive half. Defining correctness and adversarial thinking remain human work and are the more valuable half anyway.
It decides what to check. Check what it decided.
An AI testing agent derives coverage, expresses steps as intentions and returns usable failures. Guard against checks that cannot fail and coverage nobody read, and keep business rules and abuse cases in human hands.