AI agents is not a new term. We all use AI agents in one way or another in our daily lives. These AI agents do so many things like drafting an email, proof-reading your content, generating ideas for school projects, designing your logos and more. 

But what exactly are the AI agents? How do they work? And what are different types of AI agents? We’ll take a look at them in this blog. 

What are AI agents?

AI agents are computer systems that use sensors to perceive their environment and take actions using actuators.

Let’s simplify that definition. AI agents are systems that understand the problem statement, and use their knowledge and understanding to take actions. 

AI agents can be of various types. They can perform tasks with and without human supervision. They can perform actions like answering calls, writing text, uploading data, creating visuals and more. 

A type of AI agent

Types of AI Agents

In this blog, we will focus on different types of AI agents. There are four basic types of AI agents in order of increasing generality: 

  • Simple Reflex Agent
  • Model-based reflex agent
  • Goal-based agents
  • Utility-based agent
  • Learning agent

Let’s look at all of them one by one. 

Simple Reflex Agent

A Simple Reflex Agent is a type of AI agent that only uses current data and it ignores any past data. It uses a set of condition-action rules coded into the system to make its decision or take any action.

For example, imagine a vending machine as a simple reflex agent. 

  • You input money (condition) and 
  • select a snack (action), and 
  • the machine dispenses your choice based solely on that immediate input, without considering past or future transactions.

Simple reflex agents are straightforward and are suitable for simple situations where a condition leads to an action, just like our vending machine example. If we were to look at simple reflex agents and their interaction with their environment, sensors, it would look something like the image below. 

Simplex Agent interaction with environment and sensors.


Pros and Cons of using Simple reflex agents: 


  • Easy to design 
  • Easy to implement for specific tasks.
  • Responses quickly to any stimuli without complex processing. 


  • Limited Flexibility: Unable to handle unexpected or unprogrammed situations.
  • Lack of Context: Does not consider past interactions, leading to potentially suboptimal decisions.

Model-based Reflex Agent

Model-based reflex agents use the current state of the world & the internal model of that world, to decide on the best action. It partially observes the external environment by maintaining an internal environment. 

Model based reflex agent

Let’s understand it using an example of a thermostat which regulates the house temperature. It compares the inner house temperature (environment) with the temperature set by the user (internal environment) to identify whether it should turn heating/cooling on or off (action). 

Model-based reflex agents are useful in environments where complete information isn’t available, and some form of history or state needs to be considered. They're effective in applications like autocorrect where it adjusts based on the user's typing habits.

Pros and Cons of using model based reflex agents


  • You can adjust actions based on changes in the environment.
  • It uses an internal model to make informed decisions, even with incomplete information.


  • Complex to design and implement than simple reflex agents.
  • The internal model may need regular updates.

Goal-based Agents

Goal-based agents act to achieve specific goals, using the model of the world to consider the future consequences of their actions. They choose actions that lead them closer to their predefined goals. 

Goal based agents

Imagine a goal-based agent as a GPS navigation system. Given a destination (goal), it evaluates various routes (actions) using its world model (maps and traffic conditions) to recommend the fastest or shortest path, adjusting as conditions change.

Goal-based agents are ideal for complex planning and decision-making tasks where achieving a specific outcome is the priority. They're used in strategic game playing, automated planning in logistics, and resource allocation in project management, where considering future steps towards a goal is essential.

Pros and Cons of using goal-based agents


  • It is capable of adapting to achieve goals under changing conditions.
  • It considers future consequences of actions, leading to more strategic decision-making.


  • It requires more processing power for planning and evaluating potential actions.
  • It is focused on goal achievement, which may not always align with the best overall outcome.

Utility-based Agent

Utility-based agents aim not just to achieve goals but to maximize a measure of satisfaction or happiness, known as utility. They evaluate the potential utility of different states and choose actions that maximize this utility.

Utility based AI agent

Think of a utility-based agent as a savvy investor. Given various investment options (states), the investor evaluates each based on potential returns and risks (utility), aiming to maximize overall portfolio satisfaction rather than just achieving a set financial goal.

Utility-based agents are useful in scenarios requiring optimization among various competing criteria or preferences. They excel in financial analysis, complex resource management, and personalized recommendation systems where the best outcome depends on maximizing certain metrics.

Pros and Cons of using utility based agents


  • It focuses on maximizing satisfaction, leading to potentially better overall outcomes.
  • It considers a broader range of factors, leading to more nuanced decision-making.


  • Determining and quantifying utility can be challenging.
  • Evaluating and comparing utilities for different actions can be resource-intensive.

Learning Agent

Learning agents improve their performance and adapt to new circumstances over time. They can modify their behavior based on past experiences and feedback, learning from the environment to make better decisions.

Learning AI agent

Consider a learning agent as a student mastering a subject. With each lesson, homework, and test (experiences and feedback), the student (agent) learns and adjusts study habits (behavior) to improve grades (performance) over time.

Learning agents are pivotal in dynamic environments where conditions constantly change, or in tasks where human expertise and intuition are difficult to codify. They're employed in adaptive systems such as personalized learning platforms, market trend analysis tools, and evolving security systems that adapt to new threats.

Pros and Cons of using learning agents


  • It continuously improves and adapts to new information.
  • It learns from experiences, reducing the need for extensive programming for all possible scenarios.


  • It may perform sub optimally during the initial learning phase.
  • The learning processes can lead to unexpected behaviors, requiring safeguards and monitoring.

You can read more about AI agents in our detailed blog.

AI with Alltius

Are you looking to use AI agents?

Alltius is pioneering the use of generative AI agents in customer success and sales to improve buyer journeys across channels. Alltius is made by AI experts from CMU, Google, Amazon and more. With, sales and customer success teams can sell 3X more and reduce average resolution time to 10 seconds within weeks. Access a free trial or book a demo.  

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