Agent scorecard

A quality assurance score is the rating a customer service conversation receives after evaluation against a defined rubric, typically covering accuracy of information, process and compliance adherence, and communication quality such as tone, empathy and clarity. A simple rubric might weight ten criteria at 10 points each, with a call losing points for a missed disclosure or an inaccurate answer to land at 82/100. The score is only as reliable as the rubric’s clarity and the sample size behind it: manual QA programs conventionally review roughly 5 to 10% of conversations, so most interactions never get scored. OnClarity’s Agent QA scores 100% of conversations within five minutes, cutting QA operations cost by roughly 70%, and keeps scoring criteria visible so teams can calibrate evaluators against each other.

Quality assurance is the structured review of conversations against a scorecard, applied to human agents and AI agents alike. Traditional QA samples a handful of conversations per agent each month; automated QA scores every conversation, which changes the exercise from spot-checking into actual measurement.

QA coverage is the percentage of customer service conversations reviewed and scored against a quality rubric. It measures how much of an operation’s actual customer interaction volume actually gets evaluated. Manual QA programs typically sample only a small slice of contacts. Industry data puts the standard at 2 to 5 calls per agent per month, often a low single-digit percentage of total volume. Everything outside that sample is a blind spot, where compliance breaches, coaching opportunities, and recurring issues go undetected. OnClarity’s AI Quality Agent scores 100% of conversations within about five minutes of call end, roughly 20 times typical manual coverage. That shift turns QA from a sampling problem into a question of rubric consistency and audit-trail completeness.

QA calibration is a recurring exercise in which multiple evaluators independently score the same customer interaction against the same agent scorecard, then compare results and reconcile any gaps. Sessions typically run on a regular cadence to catch rubric ambiguity before it spreads and to keep evaluators aligned as scorecards evolve. Success gets measured by inter-rater reliability: how closely scores converge across reviewers. Calibration is necessary because manual QA relies on small, sampled reviews, where individual judgment drifts over time and across reviewers. Applying one rubric automatically across every conversation, as OnClarity’s AI Agent QA does, removes evaluator-to-evaluator drift by design.