Blueprint for QA Across Bot and Agent Support Models

The Blueprint for a Future-Ready QA Program

If your support experience includes bots, humans, and hybrid flows, your QA strategy needs to account for all of it, at scale. That means evaluating more interactions, surfacing the right ones for review, and shifting your team’s time toward higher-impact work.

Here’s a blueprint for how future-ready teams are making it happen.

1. Universal Experience Scoring

Start by measuring what matters—consistently.

Rather than grading a small sample of human-agent tickets, leading teams use large language models (LLMs) to evaluate 100% of conversations. This includes interactions handled entirely by bots, those escalated to agents, and everything in between.

Each conversation is scored using consistent experience signals, like sentiment, completeness, and clarity. The result is a universal experience score that applies across roles and channels, helping teams get a more complete view of how interactions are going—regardless of who handled them.

This score doesn’t replace your QA rubrics, CSAT, or other KPIs. It works alongside them, adding a scalable layer of insight that helps you triage faster, compare performance more fairly, and prioritize where to dive deeper.

2. Automated Monitoring of AI Interactions

Bots shouldn't be exempt from QA. With automated monitoring in place, your QA system can evaluate every bot-handled interaction—flagging low-quality resolutions, vague replies, or missed escalation opportunities. This lets your team track resolution quality, not just rates.

Instead of waiting for customer complaints or CSAT drops, you get ahead of problems with AI classifiers designed to flag friction in real time.

You can also compare bot performance with human performance using the same scoring logic. That helps your team understand where bots succeed, where they fall short, and where coaching or retraining may be needed.

For a deeper dive into how teams apply targeted QA to bot-only conversations, check out our guide on Improving Chatbot Performance with Targeted QA.

3. Targeted QA Assignment

Not every ticket needs human review. The key is knowing which ones do. Instead of relying on CSAT scores or random sampling, AI signals like low experience scores or severity flags can automatically surface the highest-impact conversations. This helps QA analysts spend their time on tickets that actually need attention.

When AI surfaces the outliers, QA analysts can dig in to understand what happened and why. That’s how you shift from reactive grading to proactive insight generation.

4. Evolving Analyst and Team Lead Roles

As more workflows become automated, roles shift. QA analysts spend less time on routine grading and more time identifying patterns, surfacing insights, and investigating high-risk issues. Coaching doesn’t disappear—but it moves closer to team leads, who are equipped with performance dashboards, experience scores, and flagged interactions.

This creates a feedback loop that’s faster, more focused, and less reliant on manually written scorecards.

5. Internal Feedback Loops and Save Programs

With the right foundation in place, you can activate internal programs that close the gap between insight and action.

As we’ll discuss below, one health tech brand created a “Close the Loop” initiative. When AI scores flagged potentially poor experiences, those tickets were automatically escalated for human follow-up. The team could intervene early, respond with context, and turn around interactions before the customer left negative feedback.

Programs like this turn QA into a proactive tool, not just a report card. They show how AI signals, internal workflows, and fast action can work together to recover trust and improve outcomes in real time.

Real-World Example: How a Leading Health Tech Team Built a Smarter QA Program

When a leading health tech brand realized their support experience varied wildly between bots and human agents, they knew traditional QA wasn’t enough. Their team wanted more than resolution rates and survey scores. They needed a clearer picture of how every customer interaction actually felt, no matter who handled it.

Here’s how they brought that system to life with MaestroQA.

1. Laying the Foundation: Scoring Every Interaction

The team began by creating a unified “Member Experience Score” to evaluate all support conversations—whether handled by a bot, a human agent, or both.

Using large language models (LLMs) in MaestroQA, they scored 100% of interactions on key experience signals like sentiment, completeness, and tone. This gave them a consistent view of how each conversation went, regardless of channel or handler.

Why it mattered:
This foundational score didn’t replace CSAT or QA rubrics. Instead, it added a consistent layer of intelligence that helped the team compare interactions, prioritize reviews, and detect breakdowns that other metrics missed.

2. Surfacing the Right Conversations with AI

With the scoring system in place, the team expanded their use of AI to automatically detect issues within conversations. They created LLM-powered classifiers that flagged signals like:

They also launched automated monitoring of all bot-handled conversations. By applying the same standards across both bot and human interactions, they could clearly see where automation was falling short—and where improvements were needed.

Why it mattered:
These AI-driven flags replaced guesswork and random sampling with precision. Instead of sifting through hundreds of conversations, QA analysts were pointed directly to the interactions most likely to need review.

3. Driving Action: From Risk Signals to Real Outcomes

Rather than just reviewing conversations after the fact, the team built proactive workflows around MaestroQA’s insights.

Team leads used experience scores and classifier signals to guide coaching conversations—reinforcing what worked and addressing gaps in agent performance. Analysts shifted from manual grading to spotting trends and surfacing systemic issues.

They also launched an internal program called Close the Loop, where high-risk interactions flagged by MaestroQA were routed for immediate follow-up. In many cases, they were able to turn a potentially negative customer experience into a positive one before it escalated.

Why it mattered:
This wasn’t just about monitoring, it was about making QA operational. The team turned real-time feedback into real-world action, improving both customer satisfaction and internal efficiency.

How MaestroQA Supports Compliance Excellence

A step-by-step look at how this leading health tech team restructured QA using MaestroQA.

1

Created a Consistent Member Experience Score: Used LLMs to evaluate 100% of support conversations—bots, agents, and hybrid tickets—based on clarity, sentiment, and completeness.

2

Built Custom LLM Classifiers: Collaborated with MaestroQA to build classifiers that flagged key issues, like unanswered questions, confusion, or emotional tone.

3

Automated Bot QA Monitoring: Applied the same evaluation standards to bot-only interactions, enabling consistent performance tracking across channels.

4

Prioritized the Right Conversations: Used AI signals to surface high-risk tickets, directing analysts toward meaningful reviews instead of random sampling.

5

Launched “Close the Loop” Workflow: High-risk tickets triggered internal follow-ups before CSAT was impacted—turning potential escalations into recovery opportunities.

A Model to Learn From

This health tech brand didn’t just update their QA process—they reengineered it to match the complexity of modern support. By scoring every conversation, surfacing risk in real time, and launching internal workflows to act on what mattered most, they built a system that connects insights directly to action. The results were clear:

More Targeted Coaching

Team leads guided agent performance using flagged tickets and experience signals, making coaching more relevant and efficient.

Real-Time Risk Detection at Scale

LLM scoring and classifiers replaced guesswork with AI-driven triage, improving prioritization and surfacing unseen issues in real time.

Unified View of Quality Across Bots and Humans

By scoring all conversations using the same framework, the team gained a holistic picture of quality—without needing separate QA programs for bots vs agents.

MaestroQA Features That Made It Work

Performance Dashboards

Performance Dashboards allowed the team to visualize member experience scores and trends across bot and agent interactions, helping the team track quality and prioritize areas for improvement.

LLM Classifiers

Custom Classifiers flagged signals like unresolved questions, negative sentiment, or vague responses—making it easier to surface risky conversations for deeper analysis.

AI Analytics

With 100% of conversations scored, the team used AI-driven insights to spot patterns across escalations, bot interactions, and agent behaviors. This helped them go from observation to action faster.

Integrations

The team leveraged integrations to keep their data unified and accessible across systems, helping them sync conversations and context for faster follow-through.

Coaching

Insights didn’t stop at analysis. Team leads used MaestroQA’s coaching features to share feedback with agents, guide conversations, and reinforce key behaviors tied to customer experience.

Their approach offers a clear model for teams looking to evolve beyond traditional QA—one designed to catch what matters, when it matters most.

Building Your Own Future-First QA Program

You’ve seen what’s possible. Now it’s time to apply the same principles to your own team. Designing a modern QA program starts with rethinking the foundation.

Instead of relying on outdated methods or piecemeal improvements, the most effective teams are building systems designed for the realities of today’s support landscape. That includes hybrid conversations, AI involvement, and constantly shifting customer expectations.

Here’s how to get started

Define What Quality Looks Like

• Don’t just audit agent behavior. Clarify what a great customer experience looks like across all channels. Focus on resolution accuracy, helpfulness, tone, clarity, and emotional cues.
• Once you’ve defined these core attributes, you can build LLM-based classifiers to detect them in both bot and human conversations.

Let AI Do the Heavy Lifting, Then Prioritize What Needs You

• AI can analyze every conversation, but your team doesn’t need to. Use AI scores and classifiers as a baseline to surface high-risk or unusual interactions.
• This approach helps you focus reviews where they’ll have the most impact, instead of relying on random sampling or CSAT alone.

Normalize Scoring Across Support Types

• Apply a consistent experience score to all conversations, regardless of whether they were handled by a bot or a human.
• This score won’t replace your existing metrics. It adds a new layer of insight that allows you to evaluate quality at scale and compare performance across your entire support ecosystem.

Shift Roles to Unlock New Value

• QA analysts can focus more on identifying trends and less on grading tickets one by one. Team leads can take ownership of coaching conversations, guided by experience scores and AI signals.
• This change helps each role drive more value without adding complexity or additional headcount.

Use QA Insights to Power Cross-Team Initiatives

• With scoring and workflows in place, your QA program becomes a foundation for broader strategy.
• You can launch follow-up workflows that recover poor experiences in real time. You can use AI signals to improve chatbot responses or update internal processes.

The stronger your QA foundation, the more insights you unlock for support, product, and operations.

Looking to go deeper into targeted QA strategies? Explore our guide to Building a Path from QA to Performance Excellence.