Intelligent agent

In artificial intelligence, an intelligent agent is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge. Leading AI textbooks define artificial intelligence as the "study and design of intelligent agents," emphasizing that goal-directed behavior is central to intelligence.
A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods, thereby exemplifying a novel form of digital agency.
Intelligent agents can range from simple to highly complex. A basic thermostat or control system is considered an intelligent agent, as is a human being, or any other system that meets the same criteria—such as a firm, a state, or a biome.
Intelligent agents operate based on an objective function, which encapsulates their goals. They are designed to create and execute plans that maximize the expected value of this function upon completion. For example, a reinforcement learning agent has a reward function, which allows programmers to shape its desired behavior. Similarly, an evolutionary algorithm's behavior is guided by a fitness function.
Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, and the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations.
Intelligent agents are often described schematically as abstract functional systems similar to computer programs. To distinguish theoretical models from real-world implementations, abstract descriptions of intelligent agents are called abstract intelligent agents. Intelligent agents are also closely related to software agents—autonomous computer programs that carry out tasks on behalf of users. They are also referred to using a term borrowed from economics: a "rational agent".
The concept of intelligent agents provides a foundational lens through which to define and understand artificial intelligence. For instance, the influential textbook Artificial Intelligence: A Modern Approach (Russell & Norvig) describes:
Other researchers and definitions build upon this foundation. Padgham & Winikoff emphasize that intelligent agents should react to changes in their environment in a timely way, proactively pursue goals, and be flexible and robust (able to handle unexpected situations). Some also suggest that ideal agents should be "rational" in the economic sense (making optimal choices) and capable of complex reasoning, like having beliefs, desires, and intentions (BDI model). Kaplan and Haenlein offer a similar definition, focusing on a system's ability to understand external data, learn from that data, and use what is learned to achieve goals through flexible adaptation.
Defining AI in terms of intelligent agents offers several key advantages:
An objective function (or goal function) specifies the goals of an intelligent agent. An agent is deemed more intelligent if it consistently selects actions that yield outcomes better aligned with its objective function. In effect, the objective function serves as a measure of success.
The objective function may be:
The objective function encapsulates all of the goals the agent is designed to achieve. For rational agents, it also incorporates the trade-offs between potentially conflicting goals. For instance, a self-driving car's objective function might balance factors such as safety, speed, and passenger comfort.
Different terms are used to describe this concept, depending on the context. These include:
Goals, and therefore the objective function, can be:
Some AI systems, such as nearest-neighbor, reason by analogy rather than being explicitly goal-driven. However, even these systems can have goals implicitly defined within their training data. Such systems can still be benchmarked by framing the non-goal system as one whose "goal" is to accomplish its narrow classification task.
Systems not traditionally considered agents, like knowledge-representation systems, are sometimes included in the paradigm by framing them as agents with a goal of, for example, answering questions accurately. Here, the concept of an "action" is extended to encompass the "act" of providing an answer. As a further extension, mimicry-driven systems can be framed as agents optimizing a "goal function" based on how closely the IA mimics the desired behavior. In generative adversarial networks (GANs) of the 2010s, an "encoder"/"generator" component attempts to mimic and improvise human text composition. The generator tries to maximize a function representing how well it can fool an antagonistic "predictor"/"discriminator" component.
While symbolic AI systems often use an explicit goal function, the paradigm also applies to neural networks and evolutionary computing. Reinforcement learning can generate intelligent agents that appear to act in ways intended to maximize a "reward function". Sometimes, instead of setting the reward function directly equal to the desired benchmark evaluation function, machine learning programmers use reward shaping to initially give the machine rewards for incremental progress. Yann LeCun stated in 2018, "Most of the learning algorithms that people have come up with essentially consist of minimizing some objective function." AlphaZero chess had a simple objective function: +1 point for each win, and -1 point for each loss. A self-driving car's objective function would be more complex. Evolutionary computing can evolve intelligent agents that appear to act in ways intended to maximize a "fitness function" influencing how many descendants each agent is allowed to leave.
The mathematical formalism of AIXI was proposed as a maximally intelligent agent in this paradigm. However, AIXI is uncomputable. In the real world, an IA is constrained by finite time and hardware resources, and scientists compete to produce algorithms that achieve progressively higher scores on benchmark tests with existing hardware.
An intelligent agent's behavior can be described mathematically by an agent function. This function determines what the agent does based on what it has seen.
A percept refers to the agent's sensory inputs at a single point in time. For example, a self-driving car's percepts might include camera images, lidar data, GPS coordinates, and speed readings at a specific instant. The agent uses these percepts, and potentially its history of percepts, to decide on its next action (e.g., accelerate, brake, turn).
The agent function, often denoted as f, maps the agent's entire history of percepts to an action.
Mathematically, this can be represented as:
Where:
It's crucial to distinguish between the agent function (an abstract mathematical concept) and the agent program (the concrete implementation of that function).
The agent function can incorporate a wide range of decision-making approaches, including:
Russell & Norvig (2003) group agents into five classes based on their degree of perceived intelligence and capability:
Simple reflex agents act only on the basis of the current percept, ignoring the rest of the percept history. The agent function is based on the condition-action rule: "if condition, then action".
This agent function only succeeds when the environment is fully observable. Some reflex agents can also contain information on their current state which allows them to disregard conditions whose actuators are already triggered.
Infinite loops are often unavoidable for simple reflex agents operating in partially observable environments. If the agent can randomize its actions, it may be possible to escape from infinite loops.
A model-based agent can handle partially observable environments. Its current state is stored inside the agent maintaining some kind of structure that describes the part of the world which cannot be seen. This knowledge about "how the world works" is called a model of the world, hence the name "model-based agent".
A model-based reflex agent should maintain some sort of internal model that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state. Percept history and impact of action on the environment can be determined by using the internal model. It then chooses an action in the same way as reflex agent.
An agent may also use models to describe and predict the behaviors of other agents in the environment.
Goal-based agents further expand on the capabilities of the model-based agents, by using "goal" information. Goal information describes situations that are desirable. This provides the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state. Search and planning are the subfields of artificial intelligence devoted to finding action sequences that achieve the agent's goals.
Goal-based agents only distinguish between goal states and non-goal states. It is also possible to define a measure of how desirable a particular state is. This measure can be obtained through the use of a utility function which maps a state to a measure of the utility of the state. A more general performance measure should allow a comparison of different world states according to how well they satisfied the agent's goals. The term utility can be used to describe how "happy" the agent is.
A rational utility-based agent chooses the action that maximizes the expected utility of the action outcomes - that is, what the agent expects to derive, on average, given the probabilities and utilities of each outcome. A utility-based agent has to model and keep track of its environment, tasks that have involved a great deal of research on perception, representation, reasoning, and learning.
Learning lets agents begin in unknown environments and gradually surpass the bounds of their initial knowledge. A key distinction in such agents is the separation between a "learning element," responsible for improving performance, and a "performance element," responsible for choosing external actions.
The learning element gathers feedback from a "critic" to assess the agent’s performance and decides how the performance element—also called the "actor"—can be adjusted to yield better outcomes. The performance element, once considered the entire agent, interprets percepts and takes actions.
The final component, the "problem generator," suggests new and informative experiences that encourage exploration and further improvement.
According to Weiss (2013), agents can be categorized into four classes:
In 2013, Alexander Wissner-Gross published a theory exploring the relationship between Freedom and Intelligence in intelligent agents.
Intelligent agents can be organized hierarchically into multiple "sub-agents." These sub-agents handle lower-level functions, and together with the main agent, they form a complete system capable of executing complex tasks and achieving challenging goals.
Typically, an agent is structured by dividing it into sensors and actuators. The perception system gathers input from the environment via the sensors and feeds this information to a central controller, which then issues commands to the actuators. Often, a multilayered hierarchy of controllers is necessary to balance the rapid responses required for low-level tasks with the more deliberative reasoning needed for high-level objectives.
"Intelligent agent" is also often used as a vague term, sometimes synonymous with "virtual personal assistant". Some 20th-century definitions characterize an agent as a program that aids a user or that acts on behalf of a user. These examples are known as software agents, and sometimes an "intelligent software agent" (that is, a software agent with intelligence) is referred to as an "intelligent agent".
According to Nikola Kasabov, IA systems should exhibit the following characteristics:
In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems) are a class of intelligent agents distinguished by their ability to operate autonomously in complex environments. They possess several key attributes, including complex goal structures, natural language interfaces, the capacity to act independently of user supervision, and the integration of software tools or planning systems. Their control flow is frequently driven by large language models (LLMs).
A common application of AI agents is the automation of tasks—for example, booking travel plans based on a user's prompted request. Prominent examples include Devin AI, AutoGPT, and SIMA. Further examples of agents released since 2025 include OpenAI Operator, ChatGPT Deep Research, and Manus. Frameworks for building AI agents include LangChain, as well as tools such as CAMEL, Microsoft AutoGen, and OpenAI Swarm.
Proposed protocols for standardizing inter-agent communication include the Agent Protocol (by LangChain), the Model Context Protocol (by Anthropic), AGNTCY, Gibberlink, and the Internet of Agents.
In February 2025, Hugging Face released Open Deep Research, an open source version of OpenAI Deep Research.
Galileo published on Hugging Face a leadership board for agents, which ranks their performance based on their underlying LLMs.
A non-peer reviewed research survey of 67 agents released by the end of 2024 found that the majority of agents are built by developers based in the United States, built by companies, purposed for coding or computer interaction, have code or documentation, and lack safety policies or evaluations.
Proponents argue that AI agents can increase personal and economic productivity, foster greater innovation, and liberate users from monotonous tasks. A Bloomberg opinion piece by Parmy Olson argued that agents are best suited for narrow, repetitive tasks with low risk. Conversely, researchers argue that agents could be applied to web accessibility for people who have disabilities, and researchers at Hugging Face propose that agents could be used for coordinating resources such as during disaster response.
Potential concerns include issues of liability, an increased risk of cybercrime, ethical challenges, as well as problems related to AI safety and AI alignment. Other issues involve data privacy. Additional challenges include weakened human oversight, algorithmic bias, and compounding software errors, as well as issues related to the explainability of agent decisions, security vulnerabilities, problems with underemployment, job displacement, and the potential for user manipulation, misinformation or malinformation. They may also complicate legal frameworks, foster hallucinations, hinder countermeasures against rogue agents, and suffer from the lack of standardized evaluation methods. They have also been criticized for being expensive and having a negative impact on internet traffic and potentially the environment.
Journalists have described AI agents as part of a push by Big Tech companies to "automate everything".
Yoshua Bengio warned at the 2025 World Economic Forum that "all of the catastrophic scenarios with AGI or superintelligence happen if we have agents".
In March 2025, Scale AI signed a contract with the United States Department of Defense to work with them, in collaboration with Anduril Industries and Microsoft, to develop and deploy AI agents for the purpose of assisting the military with "operational decision-making." Researchers have expressed concerns that agents and the large language models they are based on could be biased towards aggressive foreign policy decisions.
Research-focused agents have the risk of consensus bias and coverage bias due to collecting information available on the public Internet. NY Mag unfavorably compared the user workflow of agent-based web browsers to Amazon Alexa, which was "software talking to software, not humans talking to software pretending to be humans to use software."
Agents have been linked to the Dead Internet Theory due to their ability to both publish and engage with online content.
Agents may get stuck in infinite loops.
Hallerbach et al. explored the use of agent-based approaches for developing and validating automated driving systems. Their method involved a digital twin of the vehicle under test and microscopic traffic simulations using independent agents.
Waymo has developed a multi-agent simulation environment, **Carcraft**, to test algorithms for self-driving cars. This system simulates interactions between human drivers, pedestrians, and automated vehicles. Artificial agents replicate human behavior using real-world data.
The concept of agent-based modeling for self-driving cars was discussed as early as 2003.