1 Ticket QA AI Agent, Live in Production.

Ticket QA is almost always a sampling exercise, and the sample is rarely random — reviewers pick tickets that are convenient, or ones already flagged, which means the findings describe the exceptions rather than the queue. The rubric also tends to collapse three separate questions into one score. Whether the issue was actually resolved, how the exchange read to the customer, and whether the record would stand up if someone asked about it later are independent: a ticket can be resolved correctly, read coldly, and leave a record too thin to defend. A single quality percentage tells a team leader none of that, so coaching becomes generic.

Resolution Quality Review reviews a closed ticket against a quality rubric on those three axes separately: whether it was actually resolved, how it read to the customer, and what the record will not support if anyone asks later. The third is the one most often missing from a QA process and the most expensive to discover late — a complaint or a dispute months afterwards depends entirely on what was written down at the time. One agent covers this process, and it produces findings rather than scores against individuals; the coaching conversation stays with the team leader who knows the context.