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Table of contents
What is an Agent in AI?
Structure of an AI Agent
Types of Agents in AI
Simple Reflex Agent
Model-Based Reflex Agent
Goal-Based Agent
Utility-Based Agents
Learning Agents
Multi-agent systems
Hierarchical Agents
The Functions of an Artificial Intelligence Agent
Uses of Agents
Medical Evaluation
Automatic Vehicles
Office Works Automation
PEAS Representation
Taxonomy of Agents
Frequently Asked Questions
What are agents in artificial intelligence?
What are the different types of agents in AI?
What are examples of intelligence agents?
What is the difference between agent and intelligent agent?
Last Updated: Apr 3, 2024

Types of Agents in Artificial Intelligence

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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM


In artificial intelligence, an agent refers to a computer program or system that is designed to perceive its environment, make decisions, and execute actions to accomplish predefined objectives or tasks.

Agents in Artificial Intelligence

An AI agent can have mental properties like knowledge, belief, intention, etc. A rational agent might be anything that makes decisions, as an individual, firm, machine, or software. Are you getting confused about understanding agents? So, don't worry, Ninjas, we will help you to understand artificial intelligence agents. In this, blog, we will discuss types of Agents in Artificial Intelligence. So, before moving on to the main topic, let us understand what an agent is.

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What is an Agent in AI?

An agent in Artificial Intelligence is a system that performs some particular operations. It helps to perceive its environment. It also helps to make decisions and to perform actions in order to achieve a predefined goal.

The agent runs on its own, which means that a human operator does not directly manage it. An intelligent agent is an autonomous entity that uses sensors and actuators to act on the environment in order to accomplish objectives.

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Structure of an AI Agent

The structure of an AI agent typically consists of several components:

  • Perception: The agent receives input from its environment or sensors. This includes data about the current state of the world or problem.
  • Knowledge Base: The agent maintains a knowledge base or memory to store information and past experiences, which it can reference when making decisions.
  • Reasoning/Inference: The agent uses its knowledge and reasoning mechanisms to draw conclusions, make predictions, or derive solutions based on the available information.
  • Decision-Making: The agent's decision-making component selects actions or plans based on the results of reasoning. It may involve algorithms, heuristics, or optimization techniques.
  • Actuators: The agent takes actions in the environment using actuators or effectors. These actions can impact the environment or help the agent achieve its goals.
  • Goal/Task Specification: The agent is guided by goals, objectives, or tasks that define what it aims to achieve. These goals may come from external sources or be internally defined.
  • Learning Component: Some agents can learn and adapt from experience. They may employ machine learning algorithms or other techniques to improve their performance.
  • Communication: In multi-agent systems, agents may communicate with each other to exchange information or coordinate their actions.

The specific structure and components of an AI agent can vary widely depending on the application, problem, and the type of agent (e.g., expert systems, robotic agents, software agents). Agents can range from simple rule-based systems to highly complex machine learning models.

Types of Agents in AI

Agents are often grouped into five classes supported their degree of perceived intelligence and capability. of these agents can improve their performance and generate better action over time. These are given below:

  • Simple Reflex Agent
  • Model-Based Reflex Agent
  • Goal-Based Agents
  • Utility-Based Agent
  • Learning Agent
  • Multi-agent systems
  • Hierarchical agents

Simple Reflex Agent

These agents take decisions supported the present percepts and ignore the remainder of the percept history. These agents only achieve a fully observable environment. The Simple reflex agent doesn’t consider any a part of percepts history during their decision and action process. This agent works on Condition-action rule, which suggests it maps the present state to action. like an area Cleaner agent, it works as long as there’s dirt within the room.

Problems for the straightforward reflex agent design approach:

  • They have very limited intelligence
  • They do not know non-perceptual parts of the present state
  • Mostly too big to get and to store
  • Not adaptive to changes within the environment
simple reflex agent

Model-Based Reflex Agent

This agent can add a partially observable environment, and track things. A model-based agent has two important factors:

  • Model: It’s knowledge about “how things happen within the world,” so it’s called a Model-based agent.
  • Internal State: It’s a representation of the present state based on percept history.

There are some important points that we need to remember about model-based reflex agents:

  • Unlike simple reflex agents that rely solely on the current percept, model-based reflex agents consider a broader context.
  • It can adapt its behavior based on changes in the environment or new information.
  • The ability to reason, plan, and consider a wider context generally leads to improved performance compared to simple reflex agents.
model based reflex agent

Goal-Based Agent

The knowledge of the present state environment isn’t always sufficient to make a decision for an agent as to what to try. The agent must know its goal, which describes desirable situations. Goal-based agents are very important as they are used to expand the capabilities of the model-based agent by having the “goal” information. There are some point about goal-based agent:

  • They choose an action, in order that they will achieve the goal. 
  • These agents may need to consider an extended sequence of possible actions before deciding whether the goal is achieved or not. 
  • Considerations of various scenarios are called searching and planning, which makes an agent proactive.
  • Once a plan of action is selected, the goal-based agent executes the actions in the environment. 
goal based agent

Utility-Based Agents

These agents are almost like goal-based agents but provide an additional component of utility measurement which makes them different by providing a measure of success at a given state. There are some points about utility-based agents:

  • These agents act based not only on goals but also on the simplest thanks to achieving the goal. 
  • These agents are beneficial when there are multiple possible alternatives, and an agent has got to prefer to perform the simplest action. 
  • The utility function maps each state to a true number to see how efficiently each action achieves the goals.
  • These agents aim to make rational decisions that maximize their expected utility.
utility based agents

Learning Agents

A learning agent in AI is the sort of agent that may learn from its past experiences or its learning capabilities. It starts to act with basic knowledge and then is ready to act and adapt automatically through learning. A learning agent has main four conceptual components that are: 

  • Learning element: It’s liable for making improvements by learning from the environment.
  • Critic: The learning element takes feedback from the critic, which describes how well the agent is doing for a hard and fast performance standard.
  • Performance Element: It’s liable for selecting external action
  • Problem Generator: This component is liable for suggesting actions that will cause new and informative experiences.

Hence, learning agents can learn, and analyze performance and appearance for brand-spanking new ways to enhance performance.

learning agents

Multi-agent systems

Multi-agent systems (MAS) are computational systems composed of multiple interacting autonomous agents. Each agent has its own goals, capabilities, and knowledge, and can perceive the environment, make decisions, and take actions independently or collaboratively with other agents.

Hierarchical Agents

Hierarchical agents refer to agents in a multi-agent system organized in a hierarchical structure. In this structure, agents are arranged in levels or layers, with higher-level agents overseeing and coordinating the activities of lower-level agents. This hierarchical organization allows for efficient delegation of tasks, division of labor, and coordination of complex behaviors within the system.

The Functions of an Artificial Intelligence Agent

The functions of an agent in artificial intelligence are as follows:

  • To resolve complex issues using intelligent machines.
  • To decide what to do in a specific situation.
  • To make conclusions and take decisions.
  • The perception of dynamic environmental circumstances.
  • Using logic to interpret perceptions.
  • To make an effort to change environmental conditions.

Uses of Agents

Artificial Intelligence agents are used in various real-life applications.

Medical Evaluation

  • The surroundings are regarded as the patient. 
  • The sensor that collects information on the patient's complaints is a computer keyboard. 
  • The intelligent agent uses this data to determine the best plan of action. 
  • Actuators used in healthcare include tests and therapies.

Automatic Vehicles

  • Various sensors are used in automatic vehicles to gather data from the surroundings. 
  • These consist of radar, GPS, and cameras. 
  • The environment in these agents could consist of people, other cars, roads, or road signs. Actions are started using a variety of devices. For instance, the car's brakes are used to stop it. Self-driving vehicles operate better with the assistance of intelligent agents.

Office Works Automation

  • The monotonous workplace tasks can be resolved using AI agents.
  • Customer service and sales are two functional areas that have been automated. 
  • To cut operating expenses, some businesses have automated a few administrative processes. 
  • Office efficiency has also been increased using intelligent agents.

PEAS Representation

 It may be a sort of model on which an AI agent works upon. once we define an AI agent or rational agent, then we will group its properties under PEAS representation model. It’s made from four words:

  • P: Performance measure
  • E: Environment
  • A: Actuators
  • S: Sensors

Here performance measure is that the objective for the success of an agent’s behaviour.

Taxonomy of Agents

There is no consensus on the way to classify agents. this is often because there’s no agreed-upon taxonomy of agents. With this in mind, allow us to begin to classify the various sorts of agents, using some suggestions from the sector of agent theory. Charles Petrie, Stan Franklin, Art Glaesser and other agent theorists suggest that we offer an operational definition. So we’ll attempt to describe the agent’s essential components and specify what the agent seeks to accomplish.

By using the definition which we discussed above as a guide, we specify an autonomous agent by describing its:

Environment (this must be a dynamic description, that is, an outline of a state of affairs that changes over time as real-life situations do). Sensing capabilities (this depends on the sensor equipment; it determines the type of knowledge the agent is capable of receiving as input). Actions (this would be a change within the environment caused by the agent, requiring the agent to update its model of the planet, which successively may cause the agent to vary its immediate intention). Desires (these are the general policies or goals of the agent). Action Selection Architecture (the agent decides what to due next by consulting both its internal state, the state of the planet, and its current goal; then it uses decision-making procedures to pick an action).

Intelligent agents are applied as automated online assistants, where they function to perceive the requirements of consumers to perform individualised customer service. Such an agent may contain a dialogue system, an avatar, also an expert system to supply specific expertise to the user.] they will even be wont to optimise the coordination of human groups online.

  • To acquaint the peruser with the thought of an operator and specialist based frameworks.
  • To help the peruser with perceiving the space qualities that show the fittingness of an operator based arrangement.
  • To present the elemental application regions wherein operator innovation has been effectively sent so far.
  • To acknowledge the principle obstructions that dwell the tactic of the operator framework designer, lastly.
  • To offer a manual for the remainder of this book.

Frequently Asked Questions

What are agents in artificial intelligence?

In AI, agents are entities or systems that perceive their environment and take actions to achieve goals or solve problems.

What are the different types of agents in AI?

In artificial intelligence, there are various types of agents. Each agent is designed to perform specific tasks and exhibit particular characteristics. The types of agents are

  • Simple reflex agents
  • Model-based reflex agents
  • Goal-based agents
  • Utility-based agents
  • Learning agents

What are examples of intelligence agents?

Examples of intelligence agents include virtual assistants like Siri and Alexa, autonomous robots, recommendation systems, and self-driving cars. These agents exhibit varying degrees of autonomy and intelligence in their operations.

What is the difference between agent and intelligent agent?

An agent refers to any entity that perceives its environment and acts upon it, while an intelligent agent possesses additional capabilities such as reasoning, learning, and decision-making, enabling it to exhibit autonomous behavior and adapt to changing environments.


In this article, we have discussed the Agents in Artificial Intelligence. Agents in artificial intelligence play a pivotal role in simulating autonomous behavior and enabling intelligent interaction with environments. From simple rule-based systems to complex autonomous agents, the field continues to evolve, promising advancements in areas such as robotics, virtual assistants, and autonomous vehicles. 

We hope this blog has helped you. We recommend you visit our articles on different topics of Artificial Intelligence, such as

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