As you may have noticed, artificial intelligence has been accelerating rapidly in recent years. More and more people are taking advantage of the democratization of AI and using intelligent virtual assistants on a daily basis, to facilitate the most complex tasks and save time on repetitive tasks. In 2025, one trend in particular stands out: AI agents. These intelligent agents, capable of learning, acting autonomously and interacting with their environment, could well revolutionize our daily lives and the way we work in the years to come.
Let’s find out more about what they are, how AI agents work and what their benefits are.
Explanation of what an AI agent is

An artificial intelligence agent is an AI-powered computer program capable of responding to human-defined objectives. To do this, it may be able to collect data, use it and interact with its environment. It determines defined actions that will enable it to achieve the stated objective in the most efficient way. It can operate autonomously or semi-autonomously, and communicates in natural language. These characteristics make AI agents an essential element in accelerating the automation of repetitive tasks within companies.
Examples of AI agents
Never had to deal with an intelligent agent before? Today, they can be found in a wide variety of fields. Here are just a few examples:
DeepSeek R1
This is a latest-generation AI agent model, developed by China, which competes directly with ChatGPT software. This conversational agent offers excellent performance at a more affordable price. It can help you translate text, search for information or write content.
OpenAI Operator
This is a new autonomous AI agent developed by OpenAI. It is able to respond to the objectives requested in a totally autonomous way, which means that it can navigate the Internet all by itself, and even take control of your mouse and keyboard. It can, for example, help you book a hotel or go shopping completely autonomously, without human supervision.
Crypto AI agents
AI agents can be used in many fields, including decentralized finance (DeFi). They analyze cryptocurrency trends to maximize profits, provide investment portfolio management and help optimize your trades in real time. Perhaps you’ve already heard of Algotrader AI or AAVE AI?
What are the advantages of using AI agents?

By using AI agents as part of your business, it is possible to benefit from a number of key advantages, including the following:
Cost reduction
AI agents can help companies achieve economies of scale. They can enable them to cut costs by reducing human error and costly, time-consuming manual processes, and by ensuring efficient, performance-enhancing processes. They can handle complex tasks and adapt to changing environments. The result? Lower operating costs and greater profitability.
Productivity boost
In many companies, AI agents can be used to take over time-consuming tasks, freeing up valuable time for teams, who can then concentrate on higher value-added tasks. AI agents are able to perform the tasks assigned to them without any human intervention. In this way, they can contribute to the company’s development and success.
Improving the customer experience
For customers, the use of AI agents can also be beneficial. These enable personalized experiences to be offered, facilitating exchanges between the company and its customers. They can be used to set up qualitative customer service that’s available around the clock, to personalize recommended products or services, helping to improve customer engagement .
Facilitating decision-making
AI agents are able to collect and process huge amounts of data in real time, a real asset for managers and decision-makers. They help business leaders to see more clearly and benefit from advanced forecasts, making it easier to take strategic decisions.
Boosts competitiveness
By using AI agents, you can accelerate your digital transformation and stand out from your competitors by improving the automation of your business processes.
Improving compliance
An AI agent can help improve AI and corporate compliance by monitoring every step of the process and offering full traceability.
How an AI agent works

AI agents work by simplifying and automating complex tasks. To meet specific objectives, most AI agents follow a workflow structured in a few steps:
1. Perception
For the agent, it all starts with assessing its environment, which it does by collecting and processing data from a variety of sources, be they sensors, cameras, databases or direct interactions with a user.
2. Defining goals and objectives
After gathering the initial information, the agent is able to synthesize the available data to establish clear objectives. He can identify the tasks to be carried out to achieve his objective.
3. Information acquisition
The agent then searches for information to help it provide the most appropriate response. To do this, it can take advantage of internal data extraction, perform Internet searches to retrieve external data, or interact with other AI agents. It can also draw on knowledge bases to develop its analyses. In the context of a customer service chat, for example, the AI agent could scan conversation histories to transmit a response tailored to the customer and his or her experience.
4. Reasoning, planning and decision-making
Based on this information, the agent can apply advanced algorithms and models to establish an optimized strategy for progressing efficiently towards its goal. To do this, it breaks down the central objective into different achievable tasks, plans their order of execution and determines the conditions that will enable it to carry them out in the most direct way.
5. Action and task execution
Now that tasks have been planned, they can be implemented. Throughout their execution, the intelligent agent can monitor progress and control its actions if necessary. It checks the impact of each task before moving on to the next step, and can sometimes provide external feedback to confirm that the action has been carried out correctly.
6. Learning and adaptation
Beyond these stages, and once the objective has been reached, the AI agent continues to learn by memorizing each past experience. It can store its actions and their results, analyze its performance and possible areas for improvement, and thus refine its strategies for future requests. It can also take user feedback into account.
The different types of AI agents

Different types of AI agents have been developed to meet the full range of business needs. Here are a few examples:
Goal-based agents
These agents have advanced reasoning capabilities. They compare different approaches and select the most efficient one to achieve the desired result. In particular, they can be used to plan tasks that have to comply with complex parameters.
Agents with simple reflexes
Agents with simple reflexes only act on immediate data and according to predefined rules. They provide an immediate response, but do not provide in-depth reasoning. They can be used in home automation, for example, to close electric shutters according to the time of day.
Model-based reflex agents
These agents are an improved version of simple reflex agents. They have an internal representation of their environment, enabling them to better understand the situations they face and adopt more intelligent behaviors.
Learning agents
This type of agent is constantly evolving, as it learns from each new experience and shared data. Its results are enhanced by previous experience, with the aim of responding to a specific request. These agents can be invaluable tools for personalizing customer recommendations.
Utility-based agents
These agents make their decisions by evaluating different scenarios and choosing the one that maximizes benefits. They use a utility function to quantify the benefits of each choice. They can, for example, help Internet users in their search for affordable housing that accepts pets.
Line agents
These agents can handle complex tasks by breaking the problem down into sub-problems. A complex task will be divided into different sub-tasks handled by subordinate agents. The higher-level agent coordinates the overall operation and collects the results obtained.
Multi-agent systems
Multi-agent systems, or MAS, are particularly effective for solving complex problems. In these systems, a set of AI agents work together to accomplish a mission. These agents operate in a coordinated fashion and can even communicate with each other to optimize their decisions.
AI agent or chatbot? What’s the difference?
Conventional chatbots work by answering queries according to predefined rules. They often have only a limited understanding of the context of the conversation. They can’t learn and adapt in real time, they offer linear interaction and depend on a static database. In contrast, an AI agent is much more sophisticated. It integrates reasoning, learning and autonomous decision-making capabilities. It can also benefit from multi-agent orchestration and has a long-term memory, which is not the case with a chatbot.
A chatbot is best used for basic customer support or automated FAQs. An AI agent, on the other hand, can be used in a variety of circumstances, for project management, advanced analysis or strategic decision-making.
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