Quality Assurance (QA) for Support

What Is QA for Customer Support?

Quality assurance (QA) in customer support is the practice of reviewing and evaluating customer interactions to ensure they meet standards for accuracy, completeness, tone, and resolution. Traditionally, QA teams sampled a small percentage of human agent conversations, scored them against a rubric, and used the results for coaching and improvement.

With AI agents now handling 60-90% of customer interactions, QA must evolve to cover both human and AI performance.

QA for AI Agents: What Changes

AI QA differs from traditional human QA in several ways:

• Scale: AI handles thousands of conversations daily. QA can't rely on manual sampling — it needs automated quality monitoring through observability tools
• Consistency: AI agents are more consistent than humans, so QA focuses less on variation and more on systematic issues (wrong information, missed edge cases, poor retrieval)
• Root cause: When AI makes errors, the root cause is typically in the knowledge base, guardrails, or retrieval configuration — not in individual agent behavior
• Feedback loop: Fixing an AI quality issue fixes it for all future interactions. Fixing a human quality issue fixes it for one agent

Industry context: Organizations seeing the strongest results from AI treat AI agents as accountable workforce members — resetting QA metrics toward behavior-focused approaches and redesigning coaching and quality review processes for both humans and machines.

Key QA Metrics for AI-Powered Support

• Resolution rate: Was the issue actually resolved?
• Response accuracy: Was the information provided correct and complete?
• Grounding quality: Were responses based on verified source material?
• Hallucination rate: How often did the AI generate unsupported claims?
• CSAT: Were customers satisfied with the AI interaction?
• Escalation appropriateness: Were escalations warranted, or did the AI escalate unnecessarily?

The Maven Advantage: Built-In Quality Intelligence

Maven AGI's Inbox and Data & Insights provide automated QA capabilities. The Inbox detects knowledge gaps, conflicts, and outdated content that cause quality issues at the source. The "Thinks Out Loud" feature provides reasoning transparency for every interaction, making quality review efficient. Maven's audit trails log every decision, enabling systematic QA at scale.

Maven proof point: Check maintains 85% accuracy across complex financial queries with Maven AGI — a testament to the platform's quality architecture handling high-stakes interactions where accuracy is non-negotiable.

Frequently Asked Questions

Should QA teams review AI conversations?

Yes, but differently than human conversations. Rather than scoring individual interactions, QA teams should analyze patterns: what topics generate the most errors, which knowledge base articles cause confusion, and where the AI's confidence is miscalibrated. This systematic approach improves all interactions, not just the sampled ones.

How often should AI quality be reviewed?

Automated quality monitoring should run continuously. Human QA review of AI interactions should happen weekly at minimum, with deeper analysis monthly. Critical quality metrics should be tracked in real-time dashboards with alerts for sudden changes.

Can AI do QA on itself?

AI can assist with QA through automated response scoring, hallucination detection, and accuracy checks. However, human oversight remains essential — particularly for assessing tone, empathy, and judgment in complex situations that automated systems may not evaluate well.

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