Evals for AI Agents: Differences with QA Testing

According to a study by PWC , 73% of customers say that a positive experience is key to influencing brand loyalty, while 32% say they would walk away from a brand they love after just one bad experience. As more companies rely on generative AI chatbots to power customer care, it’s important to ensure a consistent, safe, and high-quality experience without human intervention. Yet, there’s a dangerous misconception floating around: that traditional QA testing is enough to guarantee chatbot performance.

It’s not.

In fact, that’s where evals for AI agents come in—and why customer care executives must understand the critical difference between QA testing and AI evals if they want to avoid outcomes that could damage their brands.

What is QA testing?

Quality Assurance (QA) testing is the process of proving that a software product functions correctly, reliably, and without bugs. Its primary goal is to check if the product meets customer requirements. There are different types of QA testing, such as finding and fixing bugs and redundancies, confirming logical flow, making sure User Interface (UI) and User Experience (UX) work smoothly, among others. Many QA testers are technical enough to understand code, but also savvy enough about the business model and UX to be able to detect malfunctions that don’t necessarily boil down to good or bad code.

That being said, QA is based on the idea of a deterministic system, which means that the same input always results in the same output. This is a key difference between code, as we’ve always known it, and generative AI.

Because that assumption breaks down with generative AI. Chatbots powered by large language models don’t follow scripted logic; they generate language dynamically based on probabilities, context, and training data. This means that even if a chatbot passes QA by responding correctly during testing, it might behave unpredictably or inappropriately once live. And this can lead to bad customer experiences, a bad reputation for your business, legal trouble or even data breaches. Just remember when a hacker tricked a car dealership’s chatbot into selling him a Chevrolet for just $1.

Evals for AI agents can help with that. Unlike QA, which focuses on functionality, evals assess qualitative aspects like tone, factual accuracy, safety, and alignment with brand values. They’re designed to catch behavior that QA simply isn’t built to test.

What are evals for AI agents?

Evals for AI agents refer to a set of practices specifically designed to test and monitor the behavior, reliability, and safety of generative AI systems—like customer service chatbots—once deployed in real-world scenarios. Rather than a pass/fail exam, evals are scenario-driven evaluations focused on how an AI agent performs in diverse and often unpredictable customer interactions.

It’s a qualitative evaluation: it tests whether the system can correctly interpret signals and respond appropriately to changing conditions—whether it makes the right choices or goes off the rails and fails to reach the intended outcome. This applies even in unpredictable situations, such as user prompts with malicious intent, like prompt injection attacks, or tricky language.

There are a few main ways to evaluate AI agents. Human evals rely on people giving feedback—like letting users rate responses or having experts review answers to improve how the AI responds over time. Code-based evals check whether the AI’s outputs, like generated code or API calls, actually work as expected. Then there are LLM-based evals, which use another AI model to review and score the chatbot’s responses. This method can mimic human feedback without needing a person to check every single output.

How QA testing falls short for AI chatbots

QA is great at answering questions like: Does the chatbot launch on the site? Are the UI buttons working? Is the backend integration successful? QA is designed for deterministic systems—ones that behave the same way every time under the same conditions.

But here’s the catch: generative AI doesn’t work like that.

A chatbot powered by a large language model (LLM) like GPT-4 or Claude can generate different responses even when asked the same question twice. This randomness is a wanted feature in chatbots. It enables the AI to be more conversational and human-like. But it also introduces risk.

Your QA test might verify that the chatbot works on a test server. But once live, the same bot might give an incorrect refund policy to a customer, hallucinate a discount code that doesn’t exist, provide unsafe or non-compliant medical advice or make up false information about your product.

None of these risks would have shown up in standard QA testing. That’s because QA isn’t designed to probe behavior . It checks function, not judgment. It confirms performance, not alignment with brand values, ethics, or compliance.

Why customer care should care

Customer care is about building trust. When an AI agent goes rogue, the consequences are immediate and public. Bad bot behavior is a headline.

Customer care is responsible for efficiency metrics and for the voice and tone of your company. Your chatbot is often your first line of interaction with a customer. If that bot behaves inappropriately or misleadingly, it reflects directly on your brand. That’s why understanding the limitations of QA and the necessity of AI evals is critical.

Evals for AI agents allow you to test AI behavior under real-world scenarios: How does the chatbot handle angry customers? Complex refund issues? Cultural sensitivity? It also identifies latent risks before they go live: Will the bot leak sensitive information or violate your policies when pressed?

Genezio’s evals for gen AI chatbots

So, how can customer care teams effectively evaluate their AI agents without turning into machine learning engineers?

Genezio provides evals for AI agents purpose-built for generative AI applications. Whether you’re deploying your first AI chatbot or managing an entire AI-powered support pipeline, Genezio helps you move beyond basic QA to understand how your agents behave in the wild.

With Genezio, you can: simulate real conversations with various personas, emotions, and edge cases; Run behavior tests across different model updates or prompt changes; Detect hallucinations , tricky answers, or compliance risks before customers do; make detailed reports for Customer Care and Risk teams.

Genezio allows you to do a one-off eval before deployment or continuous daily or weekly check-ups. This is important because LLMs change, prompts evolve, and new edge cases crop up as real users interact with the bot.

Run your evals for AI agents with Genezio

Generative AI chatbots are powerful tools, but they come with a unique set of risks that traditional QA testing isn’t equipped to handle. Customer care is in a pivotal position to shape your company’s customer experience. That starts with recognizing that evals for AI agents are essential.

QA checks whether your bot works. Evals check whether your bot behaves.

If you want to ensure your AI chatbot aligns with your brand values, delivers accurate information, and avoids costly PR mishaps, it’s time to go beyond QA.

Run your evals with Genezio reliably and for free now. You can sign up for a free report.