> For the complete documentation index, see [llms.txt](https://ask.birdie.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ask.birdie.ai/agent-quality-assurance/product-overview.md).

# Product Overview

Birdie's Agent Quality Assurance module transforms the traditional approach to Quality Assurance (QA) by shifting its focus from simple execution to strategic, scalable quality management. Our AI-enabled Customer Experience (CX) platform empowers Operations teams, drives measurable organizational impact, and cultivates a genuinely customer-centric culture.

## The Four-Step Quality Monitoring Framework

AgentQA is built on a comprehensive, four-step framework designed to elevate your quality monitoring process:

{% stepper %}
{% step %}

### Define

Establish objective, consistent criteria (rubrics) that align with key customer behaviors. Use examples of expected behavior and processes to train the AI for large-scale monitoring. Guidelines for manual review are also defined for criteria requiring human judgment.
{% endstep %}

{% step %}

### Understand

Connect quality performance directly to business outcomes. Monitor the impact of evaluated behaviors on metrics such as NPS, CSAT, and resolution rates. Supervisors gain access to consolidated overviews of team and individual agent performance, with filtering capabilities across critical dimensions (e.g., BPO, client tier, resolution, human vs. LLM).
{% endstep %}

{% step %}

### Prioritize

Leverage data to identify the highest-opportunity agents and behaviors. Focus efforts where the operational impact and urgency are greatest. Define and track strategic, individual, or group initiatives to ensure resources are targeted for maximum results.
{% endstep %}

{% step %}

### Develop

Close the performance loop with meaningful action. Implement coaching cycles and action plans based on monitoring data. AI insights enable supervisors to provide clear, actionable feedback, explaining issues and highlighting concrete steps for continuous agent improvement.
{% endstep %}
{% endstepper %}

## Explore the avaliable features

<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><h4><i class="fa-list-check">:list-check:</i></h4></td><td><strong>Criteria</strong></td><td>Define QA rules</td><td><a href="/pages/Umg5JxH3daWNSQnH8UhJ">/pages/Umg5JxH3daWNSQnH8UhJ</a></td></tr><tr><td><h4><i class="fa-phone-arrow-down-left">:phone-arrow-down-left:</i></h4></td><td><strong>Reason</strong></td><td>Contact reason</td><td><a href="/pages/5VHc1LdZYJpbK56C3jUY">/pages/5VHc1LdZYJpbK56C3jUY</a></td></tr><tr><td><h4><i class="fa-headset">:headset:</i></h4></td><td><strong>Agent</strong></td><td>Agent performance</td><td><a href="/pages/93pEJ5ryeMEWas8l07w8">/pages/93pEJ5ryeMEWas8l07w8</a></td></tr></tbody></table>


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
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```
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