In many real-world applications, multiple agents interact and work together — or compete. These are called Multi-Agent Systems (MAS).
Examples include traffic systems, online trading platforms, and video game AI. Agents in MAS can be cooperative, sharing information to achieve a common goal, or competitive, trying to outperform each other.
MAS research focuses on communication, coordination, negotiation, and collective intelligence, allowing complex systems to operate efficiently in decentralized environments.
The future of intelligent agents involves creating systems that can adapt, collaborate, and self-learn in unpredictable environments. With advancements in deep learning, reinforcement learning, and neuro-symbolic reasoning, agents are becoming increasingly context-aware and autonomous.
Emerging technologies like AI-driven robotics, digital twins, and cognitive environments are blurring the line between digital and physical worlds. The goal is to design self-sustaining ecosystems where agents evolve, interact, and optimize themselves — a true reflection of artificial intelligence at its peak.
A rational agent acts to maximize its performance measure based on the knowledge it has. It doesn’t mean it always makes the “right” decision, but rather the best possible decision with the information available.
For example, a delivery drone may choose the shortest path to deliver a package — this is a rational action even if unexpected wind slightly delays it. Rationality is not perfection; it’s about goal-oriented intelligence and adaptive reasoning under uncertainty.
This PEAS model helps in designing, analyzing, and improving AI systems by clearly defining what success looks like and how the agent interacts with its surroundings.
The relationship between an agent and its environment is the foundation of AI. This interaction can be described as a continuous cycle:
Perception: The agent observes the environment.
Decision: The agent reasons and chooses an action.
Action: The agent executes its chosen behavior.
Feedback: The environment responds, providing new information.
For example, a cleaning robot perceives dust, moves to that location, cleans it, and then updates its internal map. This feedback loop allows the agent to adapt and function intelligently in dynamic conditions.
There are several categories of AI agents, each with different levels of intelligence and autonomy:
Simple Reflex Agents – act only on the current situation (if-condition rules).
Model-Based Reflex Agents – use internal models to track environmental changes.
Goal-Based Agents – make decisions to achieve specific goals.
Utility-Based Agents – aim to maximize satisfaction or performance.
Learning Agents – continuously improve by learning from experience.
For example, a thermostat is a simple reflex agent, while a self-driving car is a learning agent that improves over time. These categories represent the evolution of intelligence in AI systems.
Agent Function – maps perceptions to actions using algorithms and logic.
The agent architecture defines how these components interact. For instance, a robot vacuum uses infrared sensors to perceive obstacles, a processor to plan paths, and motors to move — forming a loop of sense → think → act. This loop is the heartbeat of every AI system, ensuring continuous interaction with the environment.
The environment in AI is the world in which an agent operates. It provides inputs (perceptions) to the agent and receives outputs (actions) in response.
Environments can be:
Static or Dynamic – whether they change while the agent acts.
Discrete or Continuous – whether possible states are countable or infinite.
Observable or Partially Observable – whether the agent can fully see the environment’s state.
For example, a chessboard is a discrete, deterministic, and fully observable environment, while driving a car is dynamic and partially observable. Understanding the environment type is crucial for designing the right AI architecture.
An intelligent agent is an autonomous entity that perceives its environment through sensors and acts upon it through actuators to achieve specific goals. These agents form the foundation of Artificial Intelligence — capable of reasoning, learning, and adapting to their surroundings.
An AI agent can be a software program, robot, or system module that performs tasks like navigating a maze, recommending music, or trading stocks. The core idea is that agents operate independently, make decisions based on inputs, and improve their performance over time.
They are everywhere — from virtual assistants like Siri to self-driving cars, making them an essential concept in modern AI design.
An AI system is composed of an agent and its environment. The agents act in their environment. The environment may contain other agents.
What are Agent and Environment?
An agent is anything that can perceive its environment through sensors and acts upon that environment through effectors.
A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel to the sensors, and other organs such as hands, legs, mouth, for effectors.
A robotic agent replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors.
A software agent has encoded bit strings as its programs and actions.
Agent Terminology
Performance Measure of Agent − It is the criteria, which determines how successful an agent is.
Behavior of Agent − It is the action that agent performs after any given sequence of percepts.
Percept − It is agents perceptual inputs at a given instance.
Percept Sequence − It is the history of all that an agent has perceived till date.
Agent Function − It is a map from the precept sequence to an action.
Rationality
Rationality is nothing but status of being reasonable, sensible, and having good sense of judgment.
Rationality is concerned with expected actions and results depending upon what the agent has perceived. Performing actions with the aim of obtaining useful information is an important part of rationality.
What is Ideal Rational Agent?
An ideal rational agent is the one, which is capable of doing expected actions to maximize its performance measure, on the basis of −
Its percept sequence
Its built-in knowledge base
Rationality of an agent depends on the following −
The performance measures, which determine the degree of success.
Agents Percept Sequence till now.
The agents prior knowledge about the environment.
The actions that the agent can carry out.
A rational agent always performs right action, where the right action means the action that causes the agent to be most successful in the given percept sequence. The problem the agent solves is characterized by Performance Measure, Environment, Actuators, and Sensors (PEAS).
The Structure of Intelligent Agents
Agents structure can be viewed as −
Agent = Architecture + Agent Program
Architecture = the machinery that an agent executes on.
Agent Program = an implementation of an agent function.
Simple Reflex Agents
They choose actions only based on the current percept.
They are rational only if a correct decision is made only on the basis of current precept.
Their environment is completely observable.
Condition-Action Rule − It is a rule that maps a state (condition) to an action.
Model Based Reflex Agents
They use a model of the world to choose their actions. They maintain an internal state.
Model − knowledge about how the things happen in the world.
Internal State − It is a representation of unobserved aspects of current state depending on percept history.
Updating the state requires the information about −
How the world evolves.
How the agents actions affect the world.
Goal Based Agents
They choose their actions in order to achieve goals. Goal-based approach is more flexible than reflex agent since the knowledge supporting a decision is explicitly modeled, thereby allowing for modifications.
Goal − It is the description of desirable situations.
Utility Based Agents
They choose actions based on a preference (utility) for each state.
Goals are inadequate when −
There are conflicting goals, out of which only few can be achieved.
Goals have some uncertainty of being achieved and you need to weigh likelihood of success against the importance of a goal.
The Nature of Environments
Some programs operate in the entirely artificial environment confined to keyboard input, database, computer file systems and character output on a screen.
In contrast, some software agents (software robots or softbots) exist in rich, unlimited softbots domains. The simulator has a very detailed, complex environment. The software agent needs to choose from a long array of actions in real time. A softbot designed to scan the online preferences of the customer and show interesting items to the customer works in the real as well as an artificial environment.
The most famous artificial environment is the Turing Test environment, in which one real and other artificial agents are tested on equal ground. This is a very challenging environment as it is highly difficult for a software agent to perform as well as a human.
Turing Test
The success of an intelligent behavior of a system can be measured with Turing Test.
Two persons and a machine to be evaluated participate in the test. Out of the two persons, one plays the role of the tester. Each of them sits in different rooms. The tester is unaware of who is machine and who is a human. He interrogates the questions by typing and sending them to both intelligences, to which he receives typed responses.
This test aims at fooling the tester. If the tester fails to determine machines response from the human response, then the machine is said to be intelligent.
Properties of Environment
The environment has multifold properties −
Discrete / Continuous − If there are a limited number of distinct, clearly defined, states of the environment, the environment is discrete (For example, chess); otherwise it is continuous (For example, driving).
Observable / Partially Observable − If it is possible to determine the complete state of the environment at each time point from the percepts it is observable; otherwise it is only partially observable.
Static / Dynamic − If the environment does not change while an agent is acting, then it is static; otherwise it is dynamic.
Single agent / Multiple agents − The environment may contain other agents which may be of the same or different kind as that of the agent.
Accessible / Inaccessible − If the agents sensory apparatus can have access to the complete state of the environment, then the environment is accessible to that agent.
Deterministic / Non-deterministic − If the next state of the environment is completely determined by the current state and the actions of the agent, then the environment is deterministic; otherwise it is non-deterministic.
Episodic / Non-episodic − In an episodic environment, each episode consists of the agent perceiving and then acting. The quality of its action depends just on the episode itself. Subsequent episodes do not depend on the actions in the previous episodes. Episodic environments are much simpler because the agent does not need to think ahead.
