QA in customer service

Quality assurance (QA) in customer service is the systematic process of evaluating customer interactions — conversations, tickets, calls, chats — to ensure they meet defined standards for accuracy, tone, compliance, and resolution quality. QA programs exist to identify what's working, surface agent coaching opportunities, and maintain consistency across an organization's entire support operation.

Effective QA is the feedback mechanism that keeps customer service improving over time. Without it, issues with agent performance, policy adherence, or customer experience persist undetected. With it, organizations can quantify the quality of their support, intervene where standards aren't being met, and track whether improvements are actually taking hold. As AI takes on a larger share of customer interactions, QA programs are expanding to cover AI-generated responses alongside human ones — making the discipline more important, and more technically demanding, than ever before.

How QA works in customer service

Traditional QA involves reviewers listening to call recordings or reading chat transcripts and scoring them against a rubric. Common evaluation criteria include:

• Accuracy: Did the agent or AI provide correct information? Were policies applied appropriately?
• Tone and empathy: Did the communication match the brand voice and respond appropriately to the customer's emotional state, as assessed by sentiment analysis?
• Resolution quality: Was the issue actually resolved? This connects directly to first contact resolution (FCR) and customer satisfaction score (CSAT) metrics.
• Compliance: Did the interaction meet regulatory and policy requirements? This is especially important in industries with strict AI compliance obligations.
• Procedure adherence: Were escalation protocols, verification steps, and documentation requirements followed?

Modern QA operations use AI to move from sampling a small percentage of interactions to reviewing 100% of them — conversational analytics tools can automatically score interactions, surface outliers, and flag potential compliance issues without requiring a human reviewer to read every ticket. Decagon's agentic AI for CX buyer guide covers how AI-powered QA fits into a mature support operation.

Why QA matters for support teams

QA creates accountability. Without consistent evaluation, it's impossible to distinguish between an agent who genuinely needs coaching and one who is performing well but unlucky with difficult customers. QA data gives managers the evidence they need to make targeted interventions rather than blanket policy changes.

QA also drives continuous improvement at the system level. If QA scores for a particular issue type are consistently low across agents, that's a signal that the issue may be underdocumented in the knowledge base, or that escalation criteria need clarification. This organizational learning function is as valuable as the individual coaching function.

QA for AI-generated interactions

Applying QA to AI-generated responses introduces new requirements. AI systems can produce responses at volumes no human reviewer can keep up with, which makes automated scoring essential. It also means QA must assess for AI-specific failure modes: AI hallucinations, responses that are technically accurate but tonally inappropriate, and cases where the AI should have escalated to a human but didn't. Agent assist tools generate their own QA data by recording what was suggested versus what the agent ultimately sent — a comparison that reveals both agent judgment and AI suggestion quality. According to Gartner's guidance on AI quality management, organizations need dedicated QA frameworks for AI that go beyond traditional human-performance rubrics.

QA and customer experience

QA is ultimately in service of customers, even though they never see it. Every interaction that gets reviewed, scored, and used to improve agent or AI performance is a data point that makes the next thousand interactions better. Organizations that invest in robust QA — and that extend it to cover AI-generated interactions — are the ones most likely to deliver the consistent, high-quality customer experience that drives loyalty and retention.