What Are AI Agents? A Beginner’s Guide

What Are AI Agents? A Beginner’s Guide

AI agents are computer programs that are capable of performing tasks autonomously by making decisions based on their environment and input. It is not simply an automation where a program follows specific instructions. It adapts to changing inputs and feedback from users. This capability allows them to collect the data they need to execute the tasks at hand.

Simply put, it is a computer program that can learn and act on its own, that can sense and understand its environment and take steps autonomously to achieve its goal.

Characteristics of AI Agents


AI agents can operate and make decisions independently without the constant need of human intervention.

Continuously Learning

It improves over time through continuous learning which makes it capable to adapt to its changing environment.

Reactive and Proactive

An AI Agent responds to its environment through prompts and sensors and is proactive based on their internal model or goal – meaning it can predict the future and make decisions based on its historical data.

Components of an AI Agent

Perception Module

These are responsible for collecting data. Some physical examples include sensors, cameras, and microphones. Digitally, it could be as simple as typing a prompt, a database, web APIs, or a browsing history.

Reasoning Module

This is the brain of the agent where the data is stored and processed using complex algorithms. It allows the agent to remember patterns, information, frequently asked questions or rules from the past interactions which will be used to make decisions in the future. This module also examines the current situation and determines the best course of action as a response.

Photo by Growtika / Unsplash

Action Module

These are responsible for taking action and interacting with its environment. It could be in the form of a speaker, a screen display, a text message or a database update.  

To better understand this, let’s look at some examples.

  1. Self-driving cars. A self-driving car has sensors like cameras, LiDAR, radar, and ultrasonic sensors to perceive its location and surroundings and identify the objects, road signs, and pedestrians. It then interprets these data and applies appropriate reaction or response in a situation like applying break when the traffic light is red, or a pedestrian is crossing the road, or steering the wheel to turn left or right.
  2. Unthread AI. When a customer ticket is created, Unthread identifies the customer concern, adds a relevant tag to the ticket, generates a title, and automatically assigns the ticket to an agent. The interaction is text-based. Once the concern is resolved, it stores the relevant data from the said ticket to add in the assessment of the overall support performance.
  3. Virtual Assistants. VAs like Siri and Alexa are voice-activated to do certain tasks like setting reminders, sending messages, and turning on connected devices to name a few.
Amazon Alexa Echo Dot 3rd Gen
Photo by Lazar Gugleta / Unsplash

Common Types of AI Agents

Simple Reflex Agents

This kind of agent functions based on conditions set without the context/history. It follows the condition-action rule: “If condition, then action.” They also work in fully observable environments, meaning the perception module can sense the complete state of the system. The most common example is the spam filter. Once an email is received, the spam filter will scan the email using the pre-set conditions. When these conditions are met, the AI agent will filter out the email, and send it to the spam folder.

Model-Based Reflex Agent

This works on a partially observable environment and relies on the internal model on how the world operates. Unlike Simple Reflex agents, this type of AI agent relies on context or history. It still functions on condition-action rules, but it still needs historical data and then anticipates the consequences of the action. A robot vacuum cleaner is a good example of this.

Goal-Based Agent

This agent has predefined objectives. It gathers information and then decides on the most likely path to reaching its desired goal. One example of this is a community based traffic and navigation app. In order to avoid traffic, the application will check all the possible routes and decide on a better path.

Utility-Based Agent

Utility based agents are designed to achieve specific outcomes. But unlike goal-based agents, these do not have specific goals but instead identify the best solution based on the predefined criteria or utility function. An example of this is a home thermostat that senses the temperature and acts accordingly based on it.

Photo by Dan LeFebvre / Unsplash

Learning Agent

This type of agent gets better all the time by learning from past experiences. It has basic knowledge at the start, and as it encounters different situations, it gets smarter and can handle things on its own. It’s like how one gets better at playing a game. One good example is stock trading bots which can make good decision based on a combination of technical analysis, fundamental analysis and their proprietary algorithms

Hierarchical Agents

In this type, the higher level agents supervise the lower level agents. Examples include robotics, manufacturing, transportation, and autonomous vehicles.

What Are The Benefits of Using AI Agents?

Improved Productivity

AI agents can handle multiple, repetitive tasks with accuracy. That means the less time people will spend on doing these tasks (data entry, answering FAQs, scheduling appointments, and basic data analysis), the more time it is for them to handle complex tasks and inquiries.

Reduced Costs

Since the work is pre-programmed, the likelihood of having errors are minimal and the need for additional workforce will be reduced.

Informed Decision-Making

AI agents make decisions based on its pre-programmed and gathered data. They analyze all the information and could bring information to businesses based on its analysis.

Improved Customer Experience

AI agents can personalize interactions and provide support based on the historical data. This entails that the AI can discern customer preferences and tailors its service based on it.

How Does an AI Agent Work?

Determine Goals

The agent identifies what needs to be done and then creates a detailed plan.

Acquire Information

Using its perception module, it gathers data from its environment by voice, by text, browsing the internet, accessing databases, using APIs, and more other integrations. This will allow the agent to do more and produce more results.

Store and Process Data

The AI agents then process the information using complex algorithms to determine the best course of action. It would also store the data (based on the agent type) as part of its learning to improve performance.

Implement Tasks

After determining the goal, acquiring information, and storing them into its memory for better context and learning, the AI agent is now ready to interact with its environment based on its decision.


Data Security and Privacy Concerns

As mentioned above, data collection and storage is part of the components of an AI agent to ensure its continuous learning and improve its service. Ensuring the security and privacy of the collected data is crucial since the users’ privacy and some sensitive information are at stake. To guarantee security and privacy, strong encryption and access control should be implemented.

Ethical Challenges

Potential biases in AI algorithms and implications of autonomous decision-making require careful consideration. The training data could lead to unfair treatment of individuals based on race, gender, age, and other protected characteristics. For example, an AI agent could be used to filter out job candidates and may unintentionally favor men.

Technical Complexities

Technical complexities include handling and processing of data, selection of algorithms, its continuous learning and adaptation, and integration and interoperability. So, addressing these challenges would require specialized knowledge and resources to effectively manage each aspect.

Limited Compute Resource

Processing large amounts of data can strain computing power. Aside from having a remarkable processing power, its response time should be taken into consideration as well to maintain its reliability and outstanding performance. Model compression, edge computing, and incremental learning are some ways to address this challenge.

The Future of AI Agents

The reach of AI and AI agents is constantly expanding, touching everything from simple chatbots to autonomous machines. As these use-cases propagate, the impact on our daily lives will continue to grow. Keeping in mind the ethical risks and complications, we can leverage this new technology to greatly improve our everyday lives.


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