• Articles
  • Tutorials
  • Interview Questions

Types of Agents in AI (Artificial Intelligence)

Types of Agents in AI (Artificial Intelligence)
Tutorial Playlist

Agents use algorithms and frequently use machine learning techniques to improve their performance over time, making them useful tools. This article explores what agents in AI actually are and their different types, along with examples and use cases. 

Enhance your artificial intelligence knowledge with this exclusive training video featuring real-world expertise.

What are Agents in AI?

Agents are entities or computer programs that use artificial intelligence (AI) to observe their surroundings, make choices, and carry out actions to accomplish certain objectives. A core idea in artificial intelligence is that agents are used in a variety of AI systems, such as autonomous robots, chatbots, and game-playing AI. An agent is a system that carries out specific tasks and helps with the perception of its surroundings. The agent is self-running, meaning that a human operator is not directly in charge of it. An autonomous thing known as an intelligent agent uses sensors and actuators to operate in the environment in order to achieve goals.

Don’t let data remain untapped potential. Enroll in our Artificial Intelligence course and equip yourself with the skills to extract meaningful patterns and knowledge from data.

Different Types of Agents in AI

Artificial intelligence agents are classified into five major categories. These are:

  1. Simple Reflex Agent
  2. Model-Based Reflex Agent
  3. Goal-Based Agent 
  4. Utility-Based Agent
  5. Learning Agent

1. Simple Reflex Agent

  • The simplest kinds of agents are simple, reactive ones. All of their decisions are purely based on the sensory information they are receiving.
  • The Simple Reflex Agent makes decisions and takes actions without taking any account of perceptual history.
  • Simple Reflex Agent works under the condition-action rule, which allows it to translate the current state into an action. For example, a room cleaner only functions if there is dirt in the room.
  • Issues with the straightforward reflex agent design strategy:
    •  They are not very intelligent.
    •  They are unaware of the non-perceptual elements of the current situation.
    •  Most of the time, they are too enormous to produce and store.
    •  Not able to adjust to environmental changes
  • Although they are unable to learn from past mistakes, they can still enhance their performance by refining their pre-set rules and behaviors to react more effectively to certain circumstances.
simple reflex agent

2. Model-Based Reflex Agent

  • Model-based agents have the ability to keep a model inside their surroundings. 
  • They can use this to design various actions and their potential results before making judgments. These agents can improve their models over time, which will improve their ability to adapt and make decisions.
  • Two crucial components make up a model-based agent:
    • Model: A model-based agent is one that has knowledge of “how things happen in the world, which is why it is given that name.
    • Internal State: This portrays that the present condition is based on perceptual history.
  • These agents possess a model, “which is knowledge of the world,” and behave in accordance with the model.

Get 100% Hike!

Master Most in Demand Skills Now !

model-based reflex agent

3. Goal-Based Agent

  • Goal-based agents make decisions in accordance with predetermined goals.
  • By developing more effective methods for achieving these objectives, they can enhance their performance. They can develop and modify their strategies over time with the use of machine learning techniques like reinforcement learning.
  • Knowing the environment’s current state is not always enough for an agent to make decisions.
  • The agent must be aware of its goal that outlines suitable circumstances.
  • By possessing the “goal” knowledge, goal-based agents enhance the model-based agent’s capabilities.
  • They choose a plan of action to achieve their goal.
  • Before confirming whether the objective is achieved or not, these agents must consider numerous potential actions. This collection of scenario assessments is known as ‘searching and preparing,’ which enhances an agent’s proactivity.
goal-based agent

To uncover the core components behind PEAS in AI, read our blog on PEAS in AI

4. Utility-Based Agent

  • Utility-based agents consider not only what they want but also think about the good and bad things that can happen with other choices.
  • They give outcomes utility values, which enable them to choose options that will maximize expected utility.
  • Utility-based agents are like goal-based agents, but they have an extra feature. They use a special tool to show how well they’re doing at a particular point in their journey. This tool helps them stand out and make smarter decisions.
  • To assess how well each action achieves its goals, the utility function translates every state into a numerical value.
  • When an agent has to choose between different good options to do something in the right way, utility-based agents come into the picture.
utility-based agent

5. Learning Agent

  • The greatest possibility for development over time is with learning agents. 
  • Through learning algorithms, they can modify and improve their decision-making procedures. Learning agents continuously gather data and modify their techniques to get better results as they gain experience, whether through supervised learning, unsupervised learning, or reinforcement learning.
  • A learning agent primarily consists of these four conceptual parts:
    •  Learning component: It is responsible for producing advancements by taking signals from the environment
    •  Critic: The learning component uses critical feedback to determine how well the agent is performing in relation to previously established criteria.
    •  Element of Performance: It is in charge of choosing an external action.
    •  Problem Generator: This element is in charge of making recommendations for actions that will result in new and useful experiences.
learning agent

Ace your artificial intelligence interview with our comprehensive collection of the Top 70+ Artificial Intelligence Interview Questions and Answers 

Examples of Agents

Agents are used in wide applications, and some examples of agents are given below: 

  • Virtual Personal Assistants: Virtual assistants that respond to voice commands and questions, such as Siri, Alexa, and Google Assistant, carry out duties including making reminders and playing music.
  • Autonomous Robots: Agent-like autonomous robots, like self-driving automobiles and vacuum cleaners, sense their surroundings and decide how to carry out tasks.
  • Chatbots: Chatbots are artificial intelligence (AI) programs that can communicate with people via text or voice. They work in information retrieval, e-commerce, and customer service.
  • Characters in the game: AI agents control non-player characters (NPCs) in video games. Within the game environment, they act on behaviors, make choices, and communicate with other players.
  • Recommendation Systems: Recommendation agents look at user preferences and behavior to make recommendations for goods, movies, music, or other content that suits likely tastes. Examples include purchase recommendations from Amazon and Netflix.
  • Search Engines: Web Crawlers and ranking algorithms for search engines work as agents to index web pages, retrieve search results, and give users relevant data.
  • Autonomous Drones: Drones with AI agents can perform jobs like surveillance, deliveries, and aerial photography by navigating their environment and avoiding objects.

Uses of Agents

AI agents are like smart helpers that can be used in many different situations. They’re useful because they can see, think, and do things to get stuff done. Here are some examples of where we can use these agents.

  • Healthcare: Patients can be monitored, specific therapy treatments can be given, and the best use of healthcare resources may be made because of agents.
  • Robotics: Some of the major tasks in the industrial, transportation, and other industries can be automated and controlled by agents.
  • Finance: In the financial sector, agents can be used for automated trading, fraud detection, and risk management.
  • Games: Intelligent opponents can be created using agents in games and simulations, giving players a more difficult and realistic experience.
  • Smart Houses and Buildings: To manage heating, lighting, and other HVAC systems and to maximize energy efficiency and comfort, smart homes and buildings can employ agents.
  • Transportation Systems: Systems for managing traffic flow, planning routes for autonomous cars, and enhancing logistics and supply chain management can all be done with the help of agents.
  • Natural Language Processing (NLP): NLP agents are helpful for sentiment analysis, language generation, and machine translation since they can understand, generate, and translate human language


In the world of AI, agents are like smart helpers who can see, think, and take action to get things done. They are used in many ways, from virtual assistants on our phones to robots in data warehouses. These agents make our lives easier, help us find information, and even drive cars. With their ability to learn and adapt, they are becoming increasingly valuable in solving complex problems and making our technology more helpful and efficient.

Become a part of Intellipaat’s community, where you can connect with fellow learners, share experiences, and find answers to your questions.

Course Schedule

Name Date Details
Data Scientist Course 01 Jun 2024(Sat-Sun) Weekend Batch
View Details
Data Scientist Course 08 Jun 2024(Sat-Sun) Weekend Batch
View Details
Data Scientist Course 15 Jun 2024(Sat-Sun) Weekend Batch
View Details

About the Author

Principal Data Scientist

Meet Akash, a Principal Data Scientist who worked as a Supply Chain professional with expertise in demand planning, inventory management, and network optimization. With a master’s degree from IIT Kanpur, his areas of interest include machine learning and operations research.