Most AutoQA programs don’t fail because the technology doesn’t work. They fail because of decisions made in the weeks before anyone touches a single configuration setting.
In the first of our AutoQA Summer Sessions, evaluagent’s Product Enablement Manager Chris Mounce walked through the six most common AutoQA mistakes he sees — across programs of every maturity level. Here’s what they are, and how to avoid them.
1. Chasing 100% coverage before your framework’s ready
100% coverage is one of the most compelling things about AutoQA. You absolutely can score every interaction. But coverage is a capability, not a strategy.
If you switch AutoQA on across the board before you’ve validated your scoring criteria, you’ll be getting 100% noise, rather than insight.
And because AutoQA publishes at scale instantly, any gremlins in your framework get amplified across every single interaction. The reputational damage to your program can be swift and hard to undo.
The fix: Start with a sample. Run your scoring, validate the results, then widen the net. As Chris put it: “The teams that go the slowest at the start go fastest overall.”. The tortoise always wins.
2. Not defining your desired outcomes
You know you want AutoQA. But what’s it actually for?
Switching AutoQA on without a clear objective is like getting answers to questions you haven’t asked yet. Are you measuring for compliance assurance? Coaching and development? CX insight? Each one of those is a different scorecard, with different audiences and different measures of success. Without that clarity, you’ll end up with reports — but no results.
That said, your objectives don’t need to be perfect on day one. If you’re not sure what good looks like yet, start by identifying what you don’t want to see. AutoQA will surface it quickly enough.
The fix: Before you configure anything, write down what decision each scorecard is feeding. What changes if those scores move? Then calibrate your evaluators against those standards so you can trust the validation process as you go.
3. Expecting it to work out of the box, instantly
AutoQA is an evaluator that follows your instructions — literally. Where a human evaluator intuitively knows what “showing empathy” looks like in a real conversation, AI takes your instructions at face value. The precision of your prompt writing matters.
This is where programs can run into trouble. When early results aren’t perfect, teams label it as “AI failure” and the program dies from mistrust or lack of accuracy before it’s had a chance to prove itself. In reality, those early misses aren’t failures — they’re design feedback.
Chris’s rough guide: expect around eight weeks of configuration, testing and validation before you’re genuinely confident in your results.
That being said… evaluagent’s recent introduction of Line Item Builder — which drafts prompts from a plain-language description of what you want to measure — has sped that up significantly.
The fix: Run AutoQA as a pilot alongside your existing QA process. Keep reporting to stakeholders in the way they’re used to while you refine the new program in parallel. When it’s ready, the transition is clean.
4. Leaving agents out of the process
Agents often experience QA as surveillance rather than support — and that creates resistance before AutoQA has even been introduced.
There’s a compelling piece of research Chris referenced here: people accept outcomes they disagree with, if the process feels fair. Bring agents into the pilot early. Explain how the framework was built and why. Let them see their own data. That transparency changes the dynamic entirely — coaching conversations become something agents can own rather than something that’s done to them.
The fix: Include agents from the start. Don’t just recruit your top performers for the pilot — you want a breadth of experience, including those with the most development potential. And make it clear: this isn’t Big Brother. The measurement is one part of it. What you do with that measurement is what matters.
5. Scoring for compliance, not coaching
A scorecard full of yes/no, pass/fail questions makes sense for compliance checks. Did they confirm ID? Did they read the required statement? Binary is appropriate there.
But when you apply the same binary logic to soft skills — empathy, rapport, conversation flow — you lose all the nuance. You can’t differentiate between a score that’s exceptional and one that just barely scraped past. And without that gradient, you can’t coach from it. Worse, agents can figure out how to game a binary system: say the right words, hit the criteria, get the pass. The customer experience can still suffer.
The shift from black-and-white scoring to a sliding scale is, as Viki put it, like going from black-and-white TV to color.
The fix: Use binary scoring for compliance criteria. Use a scaled approach for coaching behaviors and soft skills. Match the measurement to the purpose.
6. Ignoring your AI agent interactions
AI agents — chatbots, virtual assistants, whatever they’re called in your organization — are handling more and more customer interactions every day. And most QA frameworks have zero criteria for them.
The risk is significant. A human agent makes a mistake on one call. A bot makes the same mistake in every interaction it handles that day. At scale, that’s a compliance exposure, a reputational hazard, and the kind of story that ends up on Trustpilot — or in the news. Chris didn’t have to look hard to find examples: bots that swore at customers, gave wrong information, were talked into selling a car for a dollar.
There’s also a methodological point worth noting: measuring AI agents isn’t the same as measuring human agents. For humans, QA insight feeds coaching. For bots, it feeds configuration changes. Same methodology, different outcome.
The fix: Build your AI agent evaluation criteria now. The gap only gets harder to close the longer you leave it.
The common thread
Every one of these mistakes comes down to the same underlying tension: the excitement of AutoQA’s potential, versus the discipline of doing it properly. The programs that succeed are the ones that move with intention, validate as they go, and bring the right people along for the journey.
None of these problems are unsolvable. But it does require patience at the start, which is harder than it sounds when the technology is right there and ready to go.
This article is based on Session 1 of the AutoQA Summer Sessions. Watch the recording →
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