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We launched a conversational AI agent, a sophisticated system that enables users to ask complex queries in a conversational, natural language, and the agent provides answers by querying databases and APIs.
A user might ask, “What are my time entries for last week?” and the system returns an accurate, contextual response.
The system feels fast and flexible to users, but because it’s an agentic AI that understands and responds to human language in real-time, testing it presents a whole different level of challenge.
Traditional QA methods, based on deterministic inputs and similarly deterministic outputs, simply don’t hold up.
This is where AI test automation and evals become essential. Conventional tools can’t handle the open-ended variability of human conversation, nor can they scale to test systems driven by large language models (LLMs).
To solve this, we engineered a new AI test automation framework that supports semantic evaluation, adapts to multi-turn dialogues, and scales for enterprise workloads.
It’s not just a script runner, it’s a generative AI agent that tests LLMs with human-like understanding and automation-grade consistency.
It represents the next evolution of AI testing tools, a purpose-built eval mechanism to validate modern AI systems at scale. It falls squarely in the emerging category of AI evals, which are tools and methodologies for systematically evaluating the behavior, performance, and reliability of AI systems.
From the outside, the launch was a success.
The conversational AI agent responded intelligently to complex, natural-language queries. It was fast, human, and helpful. But building a great user experience and accurate system behavior is only one side of the equation.
The other side is quality assurance, proving that the system behaves correctly, consistently, and safely.
In traditional software testing, that’s a well-understood process:
Simple. Deterministic. Repeatable.
But our AI agent didn’t return hard-coded strings. It generated language. It interpreted intent. It adapted to the context.
Suddenly, a straightforward “input-output” test wasn’t enough.
We couldn’t just ask, “Did the output match?” We had to ask:
This is precisely the kind of complexity traditional QA was never built to handle.
Our agent, powered by LLMs, handles open-ended queries with dynamic, context-aware responses, not fixed rule sets.
This introduces significant testing complexity: outputs are variable, conversations are non-linear, and behavior can evolve with minor changes to the prompt.
Even the best LLMs hallucinate, producing answers that sound convincing but are factually wrong. While these cases are rare (occurring in approximately 2-3% of interactions), they are significant. Just 2-3 inadequate responses in every 100 test runs means we need to test thousands of times to catch edge cases and ensure reliability.
Manual testing? Practically impossible.
With constantly evolving conversation paths, unpredictable user inputs, and responses that change in wording but not in meaning, manually testing every scenario would have been slow, inconsistent, and nearly unscalable. It isn’t just inefficient; it can’t keep up.
This is a classic problem that evals are designed to solve, systematically measuring LLM reliability and output accuracy under real-world variability.
We tried using deterministic test scripts, but they failed in two key ways:
Traditional API testing works because responses are fixed. You ask for data, and you get a predictable, structured result every time. But with a conversational AI system, things aren’t that rigid.
Take this simple case:
A static test might expect the response to be, “The profit was $356.74.”
But the chatbot could just as accurately say, “We achieved a profit of $356.74.”
Same meaning, different wording. Both are valid responses, but a conventional test that checks for exact text matches would mark the second one as a failure. That’s where deterministic logic breaks down; it simply can’t evaluate semantic meaning. It doesn’t understand that two different sentences can express the same truth.
This is why evals must incorporate semantic comparison, not just syntactic assertions.
The second issue is even trickier. Our chatbot is intelligent; it sometimes requests clarification before responding, especially if the original query is vague or lacks context. That’s a great feature from a UX perspective. However, it completely disrupts traditional test scripts.
Imagine this: Same input. Two valid runs. Two different traces.
Run A — Straight answer.
User: “What was revenue in Q4?”
Bot: “Revenue for Q4 was $1.2M.”
Run B — Clarify first, then answer.
User: “What was revenue in Q4?”
Bot: “Do you mean fiscal Q4 (Jan–Mar) or calendar Q4 (Oct–Dec)?”
User (QA Agent): “Calendar Q4.”
Bot: “Revenue for calendar Q4 was $1.2M.”
Both paths are correct, depending on subtle differences in context or model behavior.
This nondeterminism is why traditional QA scripts fail and AI-native evals are necessary. Evals account for multiple valid flows and contextual behavior, rather than rigid one-to-one mappings.
We engineered a QA Agent, a generative AI-powered testing agent that serves as an eval system by simulating a human QA analyst while operating with automation-grade speed and volume.
Each test case comprises:
The testing process follows this intelligent, eval-like workflow:
This isn’t just test automation, it’s an end-to-end eval loop that reflects how humans evaluate language-driven AI systems.
The QA Agent brings human-level reasoning into evals. It:
It’s a next-generation eval tool, merging deterministic QA structure with LLM-native flexibility.
After deployment, we observed:
Like any LLM-based system, our QA Agent isn’t immune to hallucinations. In fact, it faces the same core limitation as the chatbot it’s testing: it can occasionally make mistakes.
Let’s break that down. Understanding its errors, there are two ways the QA Agent can make mistakes:
Sometimes, the chatbot gives a valid answer, but the QA Agent, due to its own hallucination, flags it as a failure. Thankfully, this is rare. That’s because the QA Agent’s job is actually simpler: it only has to decide whether an answer passes or fails, not generate the full response itself. That makes it less prone to complex errors.
This one’s more serious. It occurs when both systems hallucinate simultaneously; the chatbot provides an incorrect answer, and the QA Agent mistakenly approves it.
But here’s the thing: the odds of that happening are incredibly low. With a baseline hallucination rate of 2-3%, the chance of both systems failing in sync is approximately 0.04%-0.09%, which is roughly 4-9 bad results in every 10,000 tests.
Despite minor risks, the QA agent is faster, more consistent, and less prone to errors than human testers for repetitive semantic evaluations.
In fact, its predictable error profile makes large-scale evals more trustworthy than fragmented manual testing.
As AI systems become increasingly complex, AI-native eval frameworks are becoming non-negotiable.
Static assertions are no longer viable for evaluating natural language systems.
Future-ready QA means building evals that:
Our generative QA agent represents an early but decisive step toward agentic evals, intelligent systems designed to validate other intelligent systems.
You’re not alone in facing challenges with deploying production-ready AI solutions.
We’ve helped companies take AI-based solutions live with robust analysis of the failure boundaries, a plan to minimize them, and to create solutions that operate within defined error thresholds.
This article is an example of how we’ve built frameworks to evaluate and validate solutions that have gone live to thousands of users. We’ve done it with startups, scaleups, and enterprise teams.
If you are ready to take your AI solutions (Agents, Chatbots, LLM-based solutions) to the next level, let’s talk.
We’ll walk you through what works, what doesn’t, and how to move forward. Confidently.
AI test automation, especially in the form of evals, uses machine learning and generative AI to evaluate software systems with non-deterministic behavior. Unlike traditional automation, which relies on fixed outputs, AI evals can assess semantic correctness, adapt to conversational flows, and understand human language.
Because LLMs produce variable outputs, static scripts don’t work. Evals enable meaningful validation of outputs by interpreting language and evaluating intent and correctness, not just structure.
Yes. Eval’s frameworks are built to identify hallucinations by comparing chatbot responses to expected outcomes on a semantic level, surfacing plausible but incorrect answers.
Absolutely. When powered by evals and LLM-aware logic, AI test frameworks can run thousands of high-fidelity test cases per day, achieving both coverage and consistency.
Start by identifying where traditional QA fails, often in LLM-based or NLP-driven systems. Then, adopt or build an eval system tailored to your product, incorporating semantic validation and multi-turn flow handling.
