A dog barks once in the background of an otherwise perfect call. The agent scores zero.
A phone transfer drops the first two seconds of a handoff. The receiving agent is dinged for an incomplete greeting.
A customer’s real name happens to sound like a profanity. An AI-powered QA system flags the agent for inappropriate language.
An agent wraps up a routine bill payment efficiently and accurately. They’re marked down for failing to “build rapport.”
These aren’t hypothetical scenarios. They’re among the most common complaints shared in communities like r/callcentres, and if you work in support operations you’ll recognize them immediately.
In every case, the Quality Assurance (QA) verdict was “bad agent.” And in every case, it was wrong.
If you’re a support leader who suspects your quality scores aren’t quite telling the true story, this article is for you.
We’ll cover the most common categories of QA misattribution, why checklist-based scoring keeps blaming the wrong party, what a fairer QA program looks like in practice, and how to approach AI QA responsibly so it works for your team rather than against it.
Why QA Scores Get the Attribution Wrong?
QA programs exist to answer a relatively simple question: did this interaction meet the standard we’ve set?
The problem is that most QA rubrics were built to evaluate agents.
So, when something goes wrong in an interaction — a dropped greeting, background noise, a customer who won’t give their name — the rubric interprets it as agent failure, because that’s the only variable it was designed to assess.
But interactions have multiple parties: the agent, the customer, the technology, and the organization that designed the system. A rubric that only grades one of them is structurally guaranteed to misattribute some of the time.
This isn’t a niche failure mode. It’s baked in. And it’s worth naming the categories clearly before you try to fix them.
The Four Most Common Categories of QA Misattribution
System failures scored as agent errors.
Transfer systems drop the first few seconds of audio. CRM tools lag and agents are left waiting on-screen. Phone lines degrade at the infrastructure level. When these things happen mid-call, QA flags the gap and the agent takes the hit.
Environmental factors the organization also owns.
This one is more nuanced. A dog barks. A neighbor runs a drill. A child cries in the background. Some of this is on the agent: if you’re in a customer-facing role, you should be in an environment where you can do that role well.
But if an organization provides equipment that picks up excessive background noise, or approves remote work without clear environment standards, it’s worth examining that at the organizational level before it becomes part of an individual agent’s score.
It’s a two-way street, and being clear about which side of that street a particular issue sits on is what makes a QA program fair.
Customer behavior the rubric can’t see.
A customer gives a name that triggers a profanity filter. A caller is so agitated they won’t let the agent speak. A customer hangs up before resolution.
Automated QA in particular struggles to distinguish agent-caused outcomes from customer-caused ones. When in doubt, the rubric flags the agent.
Rubric design that misunderstands the interaction type.
“Build rapport” on a routine bill payment. “Confirm the customer’s feelings” on a password reset. Some QA frameworks apply the same emotional labor expectations to every ticket type, regardless of what the customer actually needed.
An agent who handles a transactional inquiry efficiently and accurately but skips rapport-building on it isn’t performing below standard — the rubric is.
Why a Frozen Checklist Always Blames the Agent
A QA checklist is a snapshot. It captures what happened in an interaction. It doesn’t capture why it happened, who caused it, or whether the outcome was actually bad for the customer.
That’s fine when the rubric measures things the agent clearly controls, like following verification steps, communicating next actions, or staying professional under pressure. Where it breaks down is every time it scores an outcome rather than a behavior.
“Background noise present” is an outcome. “Agent failed to manage their call environment” is a behavior.
These are different things, and if your rubric doesn’t distinguish between them, it will keep blaming the agent for the noise — even when the noise came from equipment your organization chose, in an environment your organization approved.
The other structural problem with many QA programs is context. Customer service interactions are inherently variable: the customers are different, the issues are different, the constraints are different.
A rubric that scores every interaction the same will consistently penalize agents who were navigating real complexity in good faith.
When QA Becomes Primarily a Performance Driver
The misattribution problem is partly technical. But there’s an organizational dynamic that often makes it worse.
In many support environments, QA functions primarily as a performance management tool:
Low score → coaching conversation → performance plan
That’s not unreasonable in isolation. But when QA gets bundled with other metrics like handle time and after-work time into a single composite performance score, it creates a lot of pressure on agents who are already managing difficult customer interactions every day.
I’ve seen this play out firsthand. When people are monitored for deviations they may not fully understand or can’t always control, the experience becomes stressful in a way that can work against the team over time.
Disengagement creeps in, which leads to more inconsistency, which leads to more coaching, which — without the right approach — can accelerate attrition rather than reduce it.
There’s a second dynamic worth naming here. When agents don’t have a clear picture of what “good” actually looks like — like when they’re expected to intuit a standard rather than being shown real examples of it — feedback on QA scores doesn’t land the way it’s intended.
Good QA programs don’t only measure quality; they define and demonstrate it, so agents have something concrete to work toward.
This is also where QA and Customer Satisfaction (CSAT) scores share the same underlying problem: both are routinely used as blunt performance instruments rather than diagnostic tools. Getting either one right requires the discipline of being able to separate the signal from the noise and to separate what the agent controlled from what they didn’t.
What Fair Customer Service QA Actually Looks Like
The core principle is straightforward: score what the agent can control, flag what they can’t.
That doesn’t mean ignoring factors outside the agent’s control.
A pattern of background noise issues across a whole team is a useful signal about something the organization needs to fix. But those items shouldn’t count against individual agent QA scores until you’ve ruled out organizational causes.
In my experience at AIHR, one of the clearest signs that a QA program is working (or isn’t) shows up when a customer moves between teams or channels.
If they get a noticeably different response to the same question depending on who they spoke to, that’s the gap QA should be catching.
We work cross-functionally with leaders across every customer-facing team to align on voice, tone, and core process, so the experience feels connected and consistent regardless of which team a customer lands with.
At the same time, we actively want our team members to have their own style and sound like real people having real conversations. Good QA practices creates the foundation for both: consistency in the fundamentals, with room for individuals to bring their own approach to how they deliver it.
In practice, a fairer QA program requires:
- Calibration sessions. If your reviewers score the same call differently, your rubric isn’t a standard — it’s a collection of personal opinions. Regular calibration brings reviewers into alignment and makes scores mean something consistent.
- Clear definitions of what “good” looks like. Agents should be shown real examples of excellent, acceptable, and below-standard interactions. Abstract criteria invite inconsistent application and leave agents without a target.
- Separating quality signals from performance management. QA data can inform coaching. It shouldn’t automatically trigger it. One low QA score is a data point, not a verdict.
- A way for agents to contest scores. If an agent was penalized for a system drop or a customer-caused outcome, they should be able to flag it. Without that feedback loop, misattributed scores become embedded in performance records and follow people unfairly.
How AI QA Plays a Role in Supporting (or Hurting) Results
The promise of AI-powered QA is compelling: 100% interaction coverage, instant scoring, no evaluator fatigue. The important caveat is that if your rubric logic is flawed, AI won’t correct it — it will apply that same logic across every interaction it reviews.
As Justin Robbins of Metric Sherpa points out, “if automation simply multiplies the application of flawed QA logic, contact centers risk making systemic errors at scale.”
A system scoring background noise without context will apply the same judgment across every interaction — and at 100% coverage, that means a lot of agents accumulating scores for outcomes that weren’t theirs to control.
AI QA also struggles with context in ways that are easy to miss. A customer’s name that triggers a profanity filter. Sarcasm that sentiment analysis reads as hostility. An agent who deviated from the script because it was genuinely the right call.
As Swifteq’s guide to AI support automation notes, if your knowledge base has gone stale after a product update, AI QA can flag accuracy issues across a batch of tickets in a way that looks like a performance problem — when it’s actually a content problem.
The misattribution happens at scale, and without human review in the loop, it becomes embedded in coaching and performance records before anyone catches it.
If you’re deploying automated QA, the essential discipline is building real feedback loops: regularly auditing whether the system is flagging the right things, checking for patterns in what’s being penalized, and giving agents visibility into their own scores so they can understand what’s being measured.
Creating consistency before the interaction ever happens is the other side of this equation. Tools like Swifteq’s Agent Co-writer give your team shared AI-powered guidance on tone, format, and content as they write — so there’s less variance for QA to manage after the fact, and agents have a clearer model of what “good” actually looks like in practice.
Build a QA Program That Tells the Whole Story
The agents in customer support communities posting about zero scores and unfair QA reviews are describing a real and common experience. Their frustration isn’t with quality standards.
It’s with rubrics that don’t distinguish between what the agent controlled and what they didn’t. Getting an unfair grade over a situation you can’t control is bound to be upsetting.
The most useful shift a support leader can make is treating QA as a diagnostic tool rather than just a performance measurement. That means building a program that asks: is this agent performing well against the things they can actually control?
And separately: are there patterns in our scores that point to systemic or organizational issues that need fixing on our end?
Start with calibration. Define what “good” looks like and make sure your team can see it. Build in a way for agents to understand and engage with their scores. And if you’re deploying AI QA, build in regular audits — automated systems are consistent, but they’ll consistently repeat the same gaps if those gaps aren’t being reviewed.
Done well, QA becomes exactly what it was always meant to be: a tool that helps your team understand what excellence looks like and get there more consistently.
If you’re curious about how Zendesk apps like Swifteq’s might help you improve your customer service and your team’s capabilities, you can schedule a demo and we’ll show you how our apps best fit your workflows. You can also check out all of our Zendesk apps here — many of which are free or include 14-day free trials (no credit card required).
Scoring What the Agent Controlled, at Full Coverage
The principle is the easy part: score what the agent can control, flag what they can’t. Doing it on every ticket rather than a sample is where most QA programs run out of hours.
Calibration sessions, clear definitions, and a contest process all work. They also all depend on a reviewer having time to read the interaction closely enough to tell a system failure from an agent failure. At a few percent coverage, that time exists. At scale, it doesn’t, which is how misattributed scores end up embedded in performance records before anyone catches them.
This is the gap ResolveLoop was built for. It scores the agent on what they controlled, and separately names the cause when the cause sat elsewhere: the product, the policy, or a broken process. Every closed ticket gets a score and a written reason for the verdict, checked against your own help center and internal policies rather than a frozen checklist.
So the dropped transfer, the noise from equipment the organization chose, and the knowledge base that went stale after a release stop reading as agent performance problems and start reading as the organizational patterns they actually are.
That’s the same shift this article argues for. The difference is that it applies to every interaction, not the handful a reviewer had time to open.
If your QA scores are telling you something you don’t quite believe, you can book a 30 minute walkthrough and see it run against your own tickets.
Written by Neal Travis
Curious learner and builder of customer experiences in scale-ups. Neal is the Head of Customer Experience at the Academy to Innovate HR (AIHR) and Host of Growth Support.
