AI
AI agents in customer service
At the end of 2022, when chatbots began to gain popularity, we viewed artificial intelligence as the future. However, its adoption was so sudden and widespread that it sometimes feels as though it has always been around.
But AI has a history, and it’s not even that short. Now integrated into a wide range of applications, it is the result of decades of research across various fields—from medicine to education, and all the way to computer science.
This wealth of scientific knowledge, combined with data collected through the daily use of artificial intelligence systems, has made it possible to envision—and test— AI applications to assist us in any activities that “normal” systems perform. As in other periods of history, a technological breakthrough is defining an era: we are living in the GenAI, or AI Generation.
To help you navigate this vast field of knowledge, we have compiled on this page the content produced by Inmetrics on this topic. From here, you can explore the history of artificial intelligence, understand how the concept of neural networks underpins the architecture of AI applications, and, building on that, learn how AI agents are built using LLMs.
Next, you’ll learn how we’ve already reached a stage in GenAI where we interact with more capable and autonomous agents, equipped with agent-based artificial intelligence, and how we regulate autonomy in AI agents using quality practices applied to AI to build much more accurate and reliable agents!
If you’re an early adopter, you may have interacted with an AI chatbot as early as 2022. Behind that first interaction lay nearly 80 years of scientific research on artificial intelligence, a time when computers were just beginning to emerge.
It may sound obvious, but conceptually speaking, the desire to build artificial intelligence stemmed from the study of… natural intelligence. It was by reflecting on the human brain, its capacity to learn, and, subsequently, its structure that we accumulated the knowledge needed to test, in machines, how the human mind works.
Want to learn about the history of artificial intelligence long before it became the chatbot you interact with? Just click here!
Artificial intelligence is yet another example of technological innovation that has drawn inspiration from life. From biology to the health sciences, such as medicine and psychology, it has been necessary to study the brain in various contexts to understand neural networks.
Of all the capabilities of AI agents, one of the most significant is the ability to learn. The understanding of how neurons organize themselves into networks to form memory—and, consequently, learning—was, as early as the 1940s, one of the key findings of researchers. This understanding was fundamental to the creation of artificial neural networks, the computational counterpart to biological ones.
To learn more about the conceptual foundations of AI, click here!
The first machines and programs capable of learning—and consequently classified as AI —date back to the 1950s. There is a huge gap between them and the ones we interact with today; in terms of usability, this gap can be summed up by the concept of artificial intelligence agents.
Although we are quite used to interacting with chatbots, not all AI applications are agents. Agents have specific characteristics that go beyond their architecture.
Learn how we classify artificial intelligence agents—the robots we talk to every day—just click here!
Whether at the conscious, subconscious, or unconscious level, the human mind functions through language. The formation of neural networks explains our connections from a morphological perspective. However, we store memories and learn only through language.
It is important to keep this in mind because the language barrier—more specifically, the barrier of natural language processing—is what needed to be overcome in order for AI agents to become so accessible. And this barrier was overcome through LLMs.
As we’ve already mentioned, there are AI applications that aren’t AI agents. However, for any chatbot-type agent to exist, an LLM must be configured.
Learn about large language models and how they form the backbone of AI applications by clicking here!
Artificial intelligence agents possess “human-like” characteristics: “social” skills, innocence, responsiveness, and intent, among others. Some of these characteristics enhance the agents’ ability to solve problems with less human intervention. With greater autonomy—or, to use another term, agency.
Agent-based artificial intelligence applications are advanced versions of AI agents. Rather than simply performing one-off tasks, they are capable of understanding the user’s objective and planning a solution by organizing tasks into a sequential workflow—all while learning as they go.
Today's most popular AI models already use agent-based artificial intelligence. To learn more about their unique features, click here!
AI applications are evaluated based on their “social” skills, their ability to process data, and their capacity to provide as many accurate answers as possible, with minimal human intervention and without “hallucinating.” To meet all these criteria, a wide range of quality practices must be applied to AI.
Custom AI agents are becoming increasingly common in businesses, driving growth across various industries. However, are they performing tasks in accordance with the company’s own quality standards? How can we ensure that the agent requires minimal human intervention while still performing tasks correctly?
Quality practices applied to AI involve tests that are traditionally performed on other types of applications —such as unit tests —as well as more specific tests for systems based on artificial neural networks. To learn more about these quality practices applied to AI, click here!
As we’ve already mentioned, for AI agents to become widely adopted, the language barrier had to be broken down. However, understanding the content of a prompt is just one of the tasks chatbots must perform. They must be able to find the requested information, respond without “hallucinating,” and without the user having to guide the search.
The truth is that while quality practices enhance rigor and precision, it is the degree of autonomy granted to artificial intelligence agents that makes the user experience feel quite natural. As a result, quality agents are constantly monitored to maintain this delicate balance between autonomy and rigor.
To learn how the modules are configured to determine the level of autonomy in artificial intelligence agents, click here!
