Chases the testers, collects the results and separates a real defect from a misunderstanding, so acceptance testing stops stalling.
An AI agent for UAT coordination runs the acceptance testing phase that most implementations schedule properly and then watch collapse. The pattern is consistent: testing is booked for two weeks, the customer nominates testers who have day jobs, and by the end of week one three of the eight have logged in once. Your team finds out at the review meeting. The agent handles the coordination — it knows who was nominated, what each person was asked to test, who has done it and who has not, and it follows up individually rather than sending a group reminder nobody reads. When results do come in it does the second useful thing: it separates a genuine defect from a tester who has misunderstood the workflow, which is a large share of what gets logged.
Chases individually and triages what comes back.
Tracks who was nominated and what each was asked to test
Follows up individually rather than by group reminder
Reports coverage as testing progresses, not at the review
Separates a real defect from a misunderstood workflow
Routes genuine defects to your engineers with the steps attached
Flags when coverage will not support a sign-off date
UAT is where implementation timelines go to die, and it fails in a specific way: nothing appears wrong until the sign-off date, at which point it turns out half the scope was never exercised and the defects that did get logged are a mix of real bugs and confusion. Both problems are coordination problems rather than engineering problems. Testers are not ignoring the work out of bad faith; they have jobs, the request came from their colleague rather than their manager, and a group email is easy to leave for tomorrow. Individual follow-up with a specific ask works far better and nobody has time to do it. Triaging what comes back matters just as much, because an engineer who spends a day reproducing a defect that turns out to be a tester clicking the wrong tab has lost a day and some goodwill. Sorting those before they reach engineering is straightforward and nobody does it.
Track the testers, chase individually, triage the results.
Each nominated tester has a specific scope, not a general invitation to try the system.
Personal follow-up with the specific thing they were asked to do, on a cadence, rather than a group reminder.
A misunderstanding gets answered directly; a real defect goes to engineering with reproduction steps.
A UAT phase that was two days from failing.
Scenario: an implementation team had signed off two projects on partial UAT coverage and inherited the defects afterwards. Testing opens with eight nominated testers across four workflows. By day four the agent's coverage report shows two workflows fully exercised, one partly, and one — the approvals flow — untouched, because both testers assigned to it are in the same team and both are mid-quarter-close. That is visible on day four rather than at the day-ten review. The implementation lead moves the approvals testing to a different team with the sponsor's help, and the workflow is covered by day eight. Meanwhile eleven issues are logged: the agent resolves four as misunderstandings by answering the tester directly, and routes seven to engineering with reproduction steps. Under the previous process all eleven would have reached an engineer, and roughly a third of that day would have gone to the four that were never defects.
Anybody who has signed off UAT they knew was thin.
Thin sign-off means the defects arrive after go-live.
Your engineers reproduce issues that were never defects.
Coverage you cannot see is coverage you cannot fix.
Chasing testers individually is work nobody has time for.
A slipped sign-off moves every downstream date.
Post-go-live defects land with you, not with delivery.
Where testing is tracked and what reaches engineering.
Holds testers, assigned scope, coverage and issue status.
Runs the individual follow-up with each nominated tester.
Reports coverage daily and flags a sign-off date at risk.
Receives genuine defects with reproduction steps attached.
Shows the live coverage picture in the shared project space.
Reports how much logged UAT feedback turns out to be misunderstanding.
The UAT situations that decide whether sign-off is real.
Questions about running acceptance testing that means something.
An AI agent for UAT coordination tracks who was nominated to test what, follows each tester up individually, reports coverage while it can still be fixed, and separates genuine defects from misunderstandings before they reach your engineers.
Because a group reminder asks nobody in particular to do something unspecified. A message naming the person and the workflow they were asked to exercise gets acted on, and it also reveals quickly who was never going to test at all.
It can resolve the clear cases — a documented behavior, a step performed in the wrong place — and route everything else. Getting the obvious third out of the engineering queue is worth doing even if the ambiguous cases still need a person.
With the project lead's agreement, yes. Testers respond better to a specific request about their own workflow than to pressure relayed through a colleague who has no authority over them.
It should say so while the date can still be defended. Coverage reported on day four can be fixed; the same fact at the sign-off meeting leaves a choice between slipping and signing off something untested.
No. Whether partial coverage is acceptable is a commercial and risk decision for your implementation lead and the customer's sponsor. The agent makes sure they are deciding with the real picture.
How much of your UAT feedback is confusion rather than defect. A high proportion is usually a training or documentation problem, and it is cheaper to fix there than in the engineering queue.
Chases the testers, collects the results and separates a real defect from a misunderstanding, so acceptance testing stops stalling.