Every day, we experience the implementation of AI in an increasing number ofapplications, giving us the feeling that it is omnipresent. Yes, perhaps it is already everywhere; but to interact with it, in most cases we will have to resort toartificial intelligence agents.

The history of AI goes back a long way, with the first machines usingneural networksdating back to the 1950s. However, most of society only became aware of the existence of AI from 2022 onwards, with the popularization ofartificial intelligenceagents such as ChatGPT, DeepSeek, Gemini, Claude, and several others.

In practice, what are these agents? How do they work, what are their potential uses, and how can companies use them to solve problems more efficiently? Follow along with us in this article to find the answers!

Retropropagation: the milestone that made agents as we know them today possible

As the name suggests, agents areartificial intelligenceprograms that have agency, meaning they have the ability to act partially independently, making choices and exercising control over some actions. Therefore,to be classified as an agent, software must have the following characteristics:

• Autonomy → They can perform at least some tasks without human intervention.
• Social skills → They are able to interact with users in their own language.
• Reactivity → They perceive the "environment"—or context—and know how to respond to it, even if changes occur within it.
• Proactivity → More than just responding to the context, you take the initiative to find solutions that go beyond the information in the "environment."
• Knowledge stock → More than just storing memory, agents are capable of learning and understanding, that is, they can interact with data, interpret it, and "draw conclusions" from it.
• Intention → They act "rationally," aiming to achieve results based on user requests
  Naivety → They trust the user; agents are programmed not to distrust the information they receive

Above, we presented characteristics of AIapplications, but they easily fit the description of a human being. This is because, thanks to the architecture ofartificial neural networks,artificial intelligenceagents possess one of humanity's most innate traits: the ability to learn. In programs, this ability became more easily achievable after the proposal of the backpropagation algorithm, described in thearticle “Learning Representations by Backpropagation of Errors” by David Rumelhart, Geoffrey Hinton, and Ronald Williams.

In the post onartificial neural networks, we explained that they are composed of perceptrons, a linear classifier that maps input values to an output value—the artificial "neuron." Before the backpropagation algorithm,neural networkswere predominantly of thefeedforward type, meaning that information flows in one direction only. One-way data flows can “make decisions” taking into account weightings and biases. However, if the decision is not exactly what is expected, i.e., it is an “error,” infeedforwardneural networksit was not possible to “assign blame” to the previous perceptrons in order to then seek a new answer. This was only possible with the backpropagation algorithm.

What it did wasallow for some level of correctivefeedback. In other words, the algorithm enabled the treatment of a fundamental element for learning: error. If we developartificial intelligencebased on natural intelligence, let us remember how we learn and, consequently, how we form knowledge: we test, we make mistakes, we learn from our mistakes, we test again—and so on.

Error is fundamental to human learning. When a way was discovered to treat it within the architecture of applications that useartificial neural networks, it became possible to evolve these systems to the point of creating theartificial intelligenceagents we know today.

Artificial intelligence agents: how can they be useful to us?

In this text, we list seven characteristics of agents. Some of these are related to their ability to perceive and interpret context. In artificial intelligence agents as we know them, this is achieved throughLarge LanguageModels( LLMs).

LLMs are models trained on massive amounts of textual data, enabling them to make connections between distant words and thus capture long-range dependencies in the text. Because they can process sequences efficiently, they are capable of understanding and generating text. In other words, the model understands the context of the message you type to an AI agent—which is why it can respond to you.

LLMs overcome the first hurdle in user-AI application interaction: language. With this foundation, we can ask agents to perform tasks—and they are capable of a great deal.

In everyday use, we usually ask AI agents to look up information on the Internet. From there, we make various types of requests: they help us calculate data in spreadsheets, correct programming codes, performtestson systems and Internet pages, organizeautomationflows by integrating applications... In general, much of what an individual does on a computer,artificial intelligenceagents are also capable of doing.

As an example of a practical problem to be solved, the agents developed by Inmetrics work inapplication modernizationprocesses to make them suitable forthe CNPJ change. Given that Brazil will adopt thealphanumeric CNPJ in July 2026, applications will need to be able to processinvoicesand other legal entity data in the new CNPJ model. Ourartificial intelligenceagents automatically modify the code, update and validate masks, and adjust APIs and integrations.

If you’re facing other challenges at your company and think AI can help,get in touch to learn how we integrate it into all our solutions!

Artificial intelligence agents have the potential to completely transform the way your company operates, boosting operational efficiency. Contact usto get started on your transformation process as soon as possible!