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Agent QA is Birdie's AutoQA solution, it automates the quality scorecard process that QA teams have traditionally done manually.

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In a manual QA program, evaluators listen to a sample of calls (typically 2-5%), fill out a scorecard or rubric, and provide feedback. The problem: you're making decisions based on a fraction of reality.

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Agent QA evaluates 100% of interactions automatically:

• Every call, chat, and email is scored against your custom rubric
• Scoring is consistent, no evaluator bias or fatigue
• Results are available immediately, not weeks later
• Coaching opportunities surface automatically based on scorecard trends

Think of it as your QA scorecard, powered by AI, applied to every conversation.

Agent QA uses customizable scorecards (sometimes called rubrics) that reflect your quality standards. You define the criteria, Birdie's AI scores against them.

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Common scorecard categories include:

• Greeting and sign-off: Did the agent follow your brand script?
• Empathy and tone: How did the interaction feel to the customer?
• Accuracy: Was the information provided correct?
• Resolution: Was the issue actually solved, or just closed?
• Compliance: Were required disclosures and protocols followed?
• Upsell/cross-sell execution: For revenue-generating support teams

You can weight categories differently based on what matters most. Birdie's AI then applies your scorecard to every interaction, not a sample.

Most AutoQA tools stop at scoring. They tell you which agents performed well and which need coaching. That's useful, but incomplete.

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Agent QA connects quality scores to customer outcomes:

• Which agent behaviors correlate with higher CSAT?
• Which script deviations predict churn?
• Which compliance gaps create regulatory exposure?
• Which coaching investments actually move retention metrics?

Because Birdie combines AutoQA (Agent QA) with voice of customer analysis (VoC OS), you see both sides: how customers feel and how your team responds. Most automated quality management platforms only show you one half.

Agent QA automates the repetitive parts of quality management, scoring interactions, flagging issues, identifying patterns. But it doesn't eliminate the need for human judgment.

Here's how teams typically restructure:

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Before Auto QA:

• QA analysts spend 80% of time listening and scoring
• 2-5% of interactions reviewed
• Coaching based on small, potentially unrepresentative samples

After Auto QA:

• AI scores 100% of interactions against your rubric
• QA analysts focus on calibration, edge cases, and coaching
• Coaching based on statistically significant patterns

The shift is from "random sampling and manual scoring" to "full coverage with human oversight where it matters."

Strategic Purpose: Capture acronym searches, establish authority

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Answer: These terms are used interchangeably in the industry, but here's the distinction:

AutoQA (or Auto QA): The automation of quality scoring. AI evaluates interactions against a scorecard instead of a human doing it manually.

AQA (Automated Quality Assurance): Same as AutoQA, just the acronym version.

AQM (Automated Quality Management): A broader term that includes AutoQA plus the surrounding

workflows—coaching assignments, calibration, performance tracking, compliance monitoring.

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Birdie's Agent QA falls into the AQM category. It doesn't just score interactions, it connects scores to coaching, surfaces patterns across teams and BPOs, and links quality data to business outcomes.

Calibration ensures your scorecard is applied consistently—whether by AI or humans. Agent QA supports calibration in two ways:

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AI calibration: Birdie's models learn your terminology, edge cases, and scoring philosophy over time. You can review AI scores, flag disagreements, and refine accuracy.

Human-AI comparison: QA leads can score the same interaction manually and compare against the AI score. Discrepancies highlight where the rubric needs clarification or where the AI needs

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adjustment.

The goal isn't to eliminate human judgment, it's to make human judgment scalable by ensuring the AI scores the way your best evaluators would.

Yes. Agent QA applies the same scorecard approach to AI-powered support channels, generating customer intelligence from bot interactions too:

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• Chatbot accuracy: Did the bot provide correct information?
• Escalation appropriateness: Did it transfer to a human at the right moment?
• Tone consistency: Does the bot sound like your brand?
• Resolution quality: Was the issue actually solved?

This matters because AI-human handoffs are where experience often breaks down. Agent QA gives you customer intelligence showing exactly when bots transfer, why, and whether those handoffs create friction or resolution.

As AI handles more customer interactions, quality management for AI agents becomes as important as QA for human agents.

Customer intelligence isn't valuable unless it drives results. Agent QA links quality scores to outcome metrics:

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• Which agents have the highest CSAT correlation?
• Which behaviors predict first-contact resolution?
• Which compliance gaps create regulatory risk?
• Which coaching investments improve retention?

The goal isn't a leaderboard of agent scores, it's understanding which performance factors actually move your business metrics and doubling down on what works. That's customer intelligence in action.

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