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Call quality monitoring is how teams review calls against a QA scorecard. See what to score, how call center quality assurance works, and how AI reviews every call.
Call quality monitoring, often called call center quality assurance (QA), is the process of reviewing phone calls against a set standard, usually a QA scorecard, to check accuracy, compliance, professionalism and whether the caller's issue was resolved. In traditional call centers, this typically involved live supervisors listening to random call samples.
With AI-driven voice systems, call quality monitoring becomes continuous, automated, and data-rich to provide immediate visibility into every interaction without needing to manually review each call.
Each item is usually scored as pass or fail or on a points scale, and weighted by importance.
In manual QA, supervisors listen to a small sample of calls, often a few per agent per month, and fill in the scorecard by hand. AI call monitoring transcribes and scores every call against the same criteria, flags the ones that need a human review, and shows trends across all calls instead of a sample.
Agent QA is call quality monitoring applied to whoever handled the call. For an AI voice agent, that means scoring the AI on the same scorecard as your human agents: did it resolve the issue, state only correct facts, complete required steps and transfer when it should have.
What changes is how you check each item and what a failure leads to:
| Scorecard area | Human agent | AI voice agent |
|---|---|---|
| Accuracy | Reviewer listens for wrong answers | Each agent turn checked against instructions and knowledge base |
| Resolution | Reviewer judges the call | Outcome in your system, plus the tool call behind it |
| Responsiveness | Hold time and dead air | Latency and overlapping speech from call data |
| After a failed score | Coaching for that person | A prompt, flow or knowledge base fix, tested before release |
In Retell, AI QA scores a sample of calls you choose against conditions you write, such as "call resolved", and thresholds for metrics such as latency, agent hallucination and tool call inaccuracy. Reviewers can calibrate any score they disagree with. Our QA scorecard template for voice agents lists eight weighted criteria.
Someone has to act on the scores. Teams increasingly give that job to an in-house CX automation engineer, who fixes the agent and tests each change before it goes live.
For businesses automating calls with AI, maintaining consistent service quality is critical. Monitoring ensures that:
Conversations meet brand standards for tone, accuracy, and professionalism.
AI agents improve over time based on real-world performance data.
Compliance and risk management protocols are followed.
Customer experience remains a competitive differentiator, even at scale.
B2B teams in industries like finance, healthcare, and SaaS often rely on call monitoring to validate service delivery, maintain audit trails, and drive continuous optimization.
Automatic Transcription and Analysis
Calls are transcribed and evaluated automatically against KPIs like intent resolution, politeness, escalation rates, and script adherence.
Sentiment and Behavioral Scoring
AI models assess customer sentiment and agent responsiveness during calls.
Quality Dashboards and Alerts
Insights are surfaced in real-time dashboards, with alerts for anomalies like high drop-off rates, missed escalations, or negative sentiment spikes.
Continuous Improvement Loops
Insights from monitoring feed back into model training, call flow adjustments, and agent script optimizations.
A support team uses Retell AI quality assurance to score AI-handled calls on resolution, sentiment and whether it should have been escalated. Supervisors review only the calls the scoring flags, instead of a random sample.
Call quality monitoring ensures that as you automate more conversations, quality remains high and continues to get better over time, giving businesses a competitive edge through superior voice experiences.
Reviewing calls against a QA scorecard to check accuracy, compliance and customer experience, then using the results to coach agents or improve AI agents.
Agent QA is reviewing an agent's calls against a QA scorecard. AI voice agents get the same scorecard as human agents, plus checks for hallucinations, tool use and latency, and a failed score leads to a tested fix instead of coaching.
Common ones are resolution, accuracy, required steps, transfers and caller sentiment. AI voice agents add hallucination rate, tool call accuracy and response latency.
Manual programs often review a few calls per agent each month. With AI scoring, teams can review every call and send only flagged calls to a person.
Yes, when you follow call recording consent rules. Most businesses tell callers that calls may be monitored or recorded at the start of the call.