Les agents d'intelligence artificielle ont explosé ces dernières années. La complexité de leur technologie et de leurs capacités fait qu'il existe aujourd'hui de nombreux types d'agents d'intelligence artificielle.
Un agent d'intelligence artificielle est un logiciel qui exécute des tâches. Contrairement à un chatbot standard, il peut prendre des mesures au nom d'un utilisateur.
There's a wide range of AI agents, from smart thermometers and self-driving cars, to agents with chat interfaces. All of these use cases fall into one of the seven main categories of AI agents. In this article, I'll share the 7 main types of AI agent and some real-world examples of AI agents.
1. Agents réflexes simples
A simple reflex agent is an AI system that makes decisions based only on the current input from its environment.
It uses a set of condition-action rules to map observed inputs to specific responses. When it detects a certain state in the environment, it executes the corresponding rule.
It has no memory or internal model of the world — so it can only operate effectively in fully observable environments where every decision can be made based on the current input alone.
Examples of Simple Reflex Agents
- A thermostat that turns on the heat if it’s too cold
- A robot that turns when it hits a wall (hello, Roomba with a cat on top)
- A basic chatbot that replies “Hello!” when a user says “Hi”
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2. Agents réflexes basés sur un modèle
A model-based reflex agent is an AI agent that makes decisions based on both the current input and an internal model of the world.
Unlike simple reflex agents, this type keeps track of the environment’s state over time. It uses a model — essentially, stored information about how the world works — to fill in gaps when the environment isn’t fully observable.
When it receives a new input, it updates its internal state, consults its condition-action rules, and chooses the best response based on both the current percept and what it knows from previous interactions.
Examples of Model-Based Reflex Agents
- A robot vacuum that remembers the layout of a room and avoids areas it has already cleaned
- An LLM agent that continues a conversation while keeping track of past user inputs
- A game AI that reacts not only to what it sees but also to what it knows from earlier in the match
3. Agents d'apprentissage
A learning agent is an AI agent that improves its performance over time by learning from its experiences.
It has four main components: a learning element, a performance element, a critic, and a problem generator.
The performance element chooses actions, while the learning element adjusts its behavior based on feedback. The critic evaluates the outcome of actions using a predefined standard, and the problem generator suggests new actions to try for better learning.
This structure allows the agent to adapt to changes, refine strategies, and operate effectively even in unfamiliar environments.
Examples of Learning Agents
- A crypto AI agent that adjusts trading strategies based on market performance
- A recommendation engine that gets better at suggesting products based on user behavior
- A healthcare chatbot that learns from patient interactions to improve triage accuracy
4. Agents basés sur l'utilité
A utility-based agent is an AI agent that chooses actions based on which outcome is expected to provide the highest overall value or “utility.”
Rather than just aiming to achieve a goal, this agent evaluates different possible outcomes and selects the one that maximizes a predefined utility function.
This allows it to handle situations where there are multiple ways to reach a goal, or where trade-offs must be made. It requires the ability to compare options, predict consequences, and rank outcomes based on preferences or priorities.
Examples of Utility-Based Agents
- A chatbot for sales that prioritizes leads based on likelihood to convert
- A stock trading bot that balances risk and return to maximize long-term gains
- A business chatbot that schedules meetings to minimize conflicts and maximize convenience
5. Agents hiérarchiques
A hierarchical agent is an AI agent that organizes its decision-making process into multiple layers or levels, with higher levels handling abstract goals and lower levels managing specific actions.
This agent breaks complex tasks into smaller sub-tasks, with each level of the hierarchy responsible for a different scope of decision-making.
High-level layers may plan long-term strategies, while lower layers handle immediate sensor data and real-time responses. Communication flows between layers, allowing the agent to coordinate broad objectives with detailed execution.
This structure makes it easier to manage complexity and scale behavior across different time frames or priorities.
Examples of Hierarchical Agents
- In manufacturing, a high-level agent plans the assembly process while lower levels control robotic arms and timing
- In a smart factory, different layers manage production schedules, machine coordination, and physical operations
6. Goal-Based Agents
A goal-based agent is an AI agent that makes decisions by evaluating which actions will help it achieve a specific goal.
The agent is given one or more goals — desired outcomes it wants to reach. It uses search or planning algorithms to explore possible sequences of actions, then selects the ones that are most likely to lead to the goal.
Unlike reflex agents, it doesn't just react — it reasons about future consequences before acting. This makes it more flexible and capable in dynamic or unfamiliar environments, but also more computationally demanding.
Examples of Goal-Based Agents
- A navigation system that calculates the best route to a destination
- A puzzle-solving AI that searches for moves that will lead to a completed puzzle
- A robotic arm that plans a sequence of motions to successfully assemble a product
7. Multi-Agent Systems (MAS)
Last but not least: the multi-agent system.
A multi-agent system (MAS) is a system composed of multiple interacting AI agents that work together (or sometimes compete) to accomplish individual or shared objectives.
Each agent in the system operates independently, with its own capabilities, goals, and perception of the environment.
These agents communicate and coordinate — either directly through messages or indirectly by observing changes in the environment. The system as a whole can solve problems that are too complex or distributed for a single agent to handle.
Multi-agent systems can be cooperative, competitive, or a mix of both, depending on the design and goals.
Examples of Multi-Agent Systems
- Autonomous vehicles coordinating at an intersection to avoid collisions
- A set of finance bots manages invoicing, fraud detection, and reporting through AI workflow automation
- A supply chain system where different agents manage inventory, shipping, and demand forecasting
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Questions fréquemment posées
Is ChatGPT an AI agent?
Yes, ChatGPT can be considered an AI agent — it receives input, processes it, and generates responses, often using a goal or utility-driven approach depending on how it’s deployed.
What are the 7 types of AI agent?
The 7 types are: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, learning agents, hierarchical agents, and multi-agent systems.
Que sont les agents intelligents et comment fonctionnent-ils dans les environnements numériques ?
Les agents intelligents sont des entités conçues pour agir dans divers environnements numériques. Ils recueillent des connaissances sur leur environnement, évaluent la situation actuelle et exécutent des actions pour atteindre des objectifs prédéfinis. Leurs performances sont influencées par les actions externes qu'ils entreprennent dans des environnements observables.
Qu'est-ce qui constitue la base de connaissances des agents intelligents ?
Les connaissances des agents intelligents comprennent des informations sur l'environnement, des règles prédéfinies et une compréhension fondamentale de la situation actuelle. Ces connaissances constituent la base de leurs processus décisionnels.
Qu'est-ce que l'élément de performance dans le contexte des agents intelligents ?
L'élément de performance des agents intelligents fait référence à leur capacité à atteindre des objectifs et à prendre des décisions qui optimisent leurs actions dans un environnement donné. Il s'agit d'un élément crucial qui détermine l'efficience et l'efficacité de l'agent.
Les agents peuvent-ils opérer dans des structures hiérarchiques ?
Oui, les agents hiérarchiques sont un type d'agent intelligent qui opère à des niveaux structurés. Les agents de haut niveau supervisent la prise de décision générale, tandis que les agents de niveau inférieur s'occupent de tâches spécifiques dans un cadre plus large. Cette structure hiérarchique permet un fonctionnement efficace dans des environnements complexes.
Les agents intelligents fonctionnent-ils avec une intelligence limitée ?
Oui, de nombreux agents intelligents fonctionnent avec une intelligence limitée, ce qui signifie qu'ils ont un champ de connaissances et de capacités défini. Cette limitation les aide à se concentrer sur des tâches et des environnements spécifiques où leur expertise est la plus pertinente.
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Comment l'intelligence artificielle joue-t-elle un rôle dans la fonctionnalité des agents ?
L'intelligence artificielle permet aux agents intelligents d'apprendre, de raisonner et de s'adapter. Les agents utilisent l'IA pour améliorer leur base de connaissances, ce qui leur permet de prendre des décisions plus sophistiquées dans divers environnements.