Do you believe everything you see, especially on the Internet? You would probably answer “no” to that question. But does that “no” include the responses you receive from chatbot-style AI agents that you interact with on a daily basis? If, in your view, they provide accurate information, you should know that there are many quality practices applied to AI to make that happen.

We’ve already discussed here on the Inmetrics blog how quality in the software industry is linked to the perception of trust. We consider applications to be of high quality when they perform as expected; when we can be confident that the calculations are correct and, consequently, that the information we see on the screen is accurate.

It’s no different when interacting with artificial intelligence agents. We expect them to function like a chatbot, answering our questions while ensuring they provide us with accurate information. For this to happen, the agents must access and process a very large volume of information in a very short amount of time. To ensure that the output—or response—is accurate, it is necessary to apply rigorous quality control measures to the agents. In this article, we’ll explore the importance of this work and how it’s done.

AI, post-truth, and trust

We are living in the age of artificial intelligence, and with it come significant challenges related to the reliability of information. In a landscape where content can appear to be true without necessarily being so, discussions arise about the ethical limits of technology, often addressing the topic of post-truth: a context in which plausible information is highlighted as if it were proven facts or objective truth.

Lies are easier and cheaper to produce; however, it is the truth that generates real value for the business. This is precisely why quality practices applied to AI are so crucial. They enhance rigor in the use and processing of data, ensuring that applications deliver reliable and relevant information to the user.

In our daily lives, both personal and professional, we consult with and assign tasks to artificial intelligence agents, treating them as legitimate co-pilots. If we take this metaphor to its logical conclusion, a question arises: if you didn’t trust your co-pilot, would you perform a risky maneuver in the aircraft based on the information provided by them?

However, developing security for AI applications inevitably runs up against one of the key capabilities of agents, especially those based on agent-based artificial intelligence: autonomy. Just like co-pilots, we want agents to function properly with as little supervision from us as possible. But how can we trust them if we don’t have full visibility into how they perform their tasks?

This is exactly where quality practices applied to AI come into play. Their purpose is to reduce the risk of LLMs processing data incorrectly. In practice, this minimizes the chances of you receiving inaccurate information, thereby reducing the impact of a potential wrong decision.

Quality practices applied to AI: continuous monitoring through testing

Like any other software applications, we ensure the quality of AI agents through testing. The tests typically performed on other types of software— unit, integration, manual, and even user experience tests —are also carried out on AI agents. However, there are other, more specific tests that are fundamental to increasing the reliability of this type of system. To explain them, it’s worth recalling neural networks, the architectural model for artificial intelligence applications.

Artificial neural networks are structured in layers, with the output values of one layer serving as the input values for the next. The layers calculate a weighted sum by taking into account the received values, the weights assigned to each of them, and a bias value.

Testing for bias is one of the key quality practices applied to AI. The first step is to assess the depth of the bias. The most superficial forms—statistical or computational bias—can be identified and corrected through automated testing. Deeper forms of bias, however, such as human or even systemic bias —which often involves company culture—cannot always be corrected by the software industry.

In addition to bias, the inputs and outputs of each layer are tested using metamorphic tests. These are frequently used in LLMs to address the “Oracle Problem”—a situation where there is no definitive set of answers from applications —a reality commonly encountered with LLMs, which are probabilistic systems by nature.

Finally, AI agents “hallucinate.” To mitigate this effect, quality-as-a-service is applied; in other words, the application is continuously tested to verify whether these “hallucinations” are disrupting the agent’s workflows and at what point in the architecture this disruption is occurring.

And forecasts indicate that demand for quality-as-a-service solutions for agents will only continue to grow. The FutureScape 2026 report from the International Data Corporation (IDC) indicates that 80% of enterprise applications will be continuously monitored in production. Given that 75% of devices sold in 2026 will have embedded AI—with the corporate segment driving most of this growth—an increasing number of LLMs will need to be continuously monitored.

Here at Inmetrics, we were founded with the goal of improving the quality and performance of applications. Today, our Digital Experience unit is dedicated to safeguarding both your company’s revenue and reputation through quality engineering. We conduct large-scale, comprehensive testing to identify issues before they become bugs.

In our Consulting, Data, and AI division, one of our key priorities is to implement artificial intelligence with a focus on process optimization, thereby supporting the entire journey—from conception to execution—of technological solutions that drive real business impact.

If you want to implement AI agents in your company’s applications but are concerned about the consequences of errors they might cause, please contact us and speak with one of our experts! We have advanced expertise in verifying the quality of LLMs at every level, minimizing the risks of data misprocessing as much as possible.