Understanding Agent Environment in AI

The role of the agent is always very important in artificial intelligence, machine learning, and deep learning. Learn more about agents here.


Brief Introduction of Agents in AI


Before starting the article, it is important to understand what an agent in AI is. The agent is basically an entity that helps the AI, machine learning, or deep reinforcement learning to make a decision or trigger the AI to make a decision. In terms of software, it is defined as the entity which can take decisions and can make different decisions on the basis of changes in the environment, or after getting input from the external environment. the role of an agent is very important in these fields as all the parameters of an AI model depend primarily on the performance of the agent. In simpler words, the quick agent perceives external change and acts against it the better the results obtained from the model. Hence the role of the agent is always very important in artificial intelligence, machine learning, and deep learning. The most common example of AI agents is Alexa and Siri. As both of them act against the request of the user, gather information from the internet, provide solutions, and act accordingly. These agents are so intelligent that they can gather the complete information of the user and even suggest their user-related ads, and recommendations as well. (Intelligent Agents in Artificial Intelligence | Engineering Education (EngEd) Program | Section, 2019)


A Closed Loop System in which a system gets some Inputs from the Environment and provides some Actions back to the Environment
Figure 1. A Closed Loop System in which a system gets some Inputs from the Environment and provides some Actions back to the Environment | Image by Editor


Intelligent Agent


Intelligent is a software-based program that can understand the situations, perceive the environment, and on the basis of past history and regular training refine its search. The most important part for the intelligent agent is basically the user input, i.e., which kind of things interest the user. The most common example of intelligent agents is the agents that are being used on Facebook, YouTube, and Google. They always show the ads which attract the user. For example, if you wanted to buy perfume online and you have searched two to three different stores on the internet for perfume, later on in your social media newsfeed you have observed different ads related to perfumes. These are due to the intelligent Ai based agents working behind all the search history and user input. The several types of intelligence are human agents, software agents, and robotic agents. (What Is an Intelligent Agent? - Definition from WhatIs.Com, n.d.) 


Types of Agents


There are several types of Agents (Electronic, n.d.) in AI and machine learning. A brief discussion on each of them will be made in this section as follows.

  • Simple Reflex Agents are the most common type of agent. They work on the current perception and ignore all the past history and past paths. A common example is a room cleaner agent which works only when there is dirt in the room regardless of the past history. 
  • Model-Based Reflect Agents are the ones that can observe the environment only partially. They also track the situation. The word “model” here refers to what is happening in the world, and it has an internal state, which basically represents the current state based on the history of the model.
  • Goal-Based agents are the agents doesn’t only require the current state, past state, and what to do only. They also need some sort of information regarding the target or the goal as well. They are more capable because they draw paths on the basis of other machine learning techniques in order to reach goals better and in an effective way. They choose actions based on the situation of the environment.
  • Utility-Based Agents are agents which are similar to those goal-based agents, but they have only added responsibility to cater to the utility as well. They provide the efficiency, and status of the work at each step of the work to the model. They provide efficiency in real numbers with the help of a feedback system in order to check the validity and efficiency of the system. 
  • Learning Agents are the most intelligent type of agents in this regard. They learn from the user inputs, previous history, environment, and surroundings. They have started like conventional agents with basic knowledge of the environment. it has different components like the learning element, which improves the capabilities of the agent with the failures and achievements. The other component is criticism, with the help of which the learning element gets the feedback if the work is in the right direction or not. The third one is the performance element; this component is responsible to measure the performance of the system and keep the system in full flow. The final component is the problem generator with the help of which the system will gain new and latest experiences and refine the work with more and more load work. (Types of AI Agents - Javatpoint, n.d.)


Application of AI Agents


The question arises where all these agents applied or work. These agents are mostly software-based. They worked in games, searches, and automatic form filling. In gaming, different companies trying to make their games more competitive use these AI-based agents. These agents are mostly learning agents sort of agents who have learned from the moves of the user and can go from the beginner level to pro-level by playing a number of games and past experiences. There are some other games that have used very low-level agents which are usually simple reflex agents, as they didn’t improve with the passage of time and a certain moment comes when the user gets bored with the game and leaves playing the game. In the same way, these agents also work in search engines as well. As observed google only shows us relevant pages depending on the user’s search history, previous inputs, and past experience. These agents are very strong are used in such searches and can capture data very quickly. (Intelligent Agents: Characteristics and Applications | AI, n.d.) 




  • Intelligent Agents: Characteristics and Applications | AI. (n.d.). Retrieved February 26, 2022, from https://www.engineeringenotes.com/artificial-intelligence-2/intelligent-agents/intelligent-agents-characteristics-and-applications-ai/35618
  • Intelligent Agents in Artificial Intelligence | Engineering Education (EngEd) Program | Section. (n.d.). Retrieved February 26, 2022, from https://www.section.io/engineering-education/intelligent-agents-in-ai/
  • Types of AI Agents - Javatpoint. (n.d.). Retrieved February 26, 2022, from https://www.javatpoint.com/types-of-ai-agents
  • What is an intelligent agent? - Definition from WhatIs.com. (n.d.). Retrieved February 26, 2022, from https://www.techtarget.com/searchenterpriseai/definition/agent-intelligent-agent

Neeraj Agarwal is a founder of Algoscale, a data consulting company covering data engineering, applied AI, data science, and product engineering. He has over 9 years of experience in the field and has helped a wide range of organizations from start-ups to Fortune 100 companies ingest and store enormous amounts of raw data in order to translate it into actionable insights for better decision-making and faster business value.