AI agents are no longer a futuristic concept. From voice assistants to customer service chatbots, these agents now play an active role in how we live, work, and interact. But what exactly are AI agents, how do they operate, and what makes one type different from another?
In this article, we'll break down what AI agents are, the different types of AI agents with examples, and how they relate to concepts like types of agent authority and the 3 types of agency relationships in decision-making systems.
An AI agent is a system capable of perceiving its environment through sensors and acting upon that environment using actuators to achieve specific goals. In simple terms, it’s a program or system that can understand a problem and take actions accordingly, sometimes even improving with time.
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:
Let’s look at all of them one by one.
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.
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.
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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.
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.
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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.
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.
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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.
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.
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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.
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.
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You can read more about AI agents in our detailed blog.
In both legal frameworks and intelligent system design, understanding types of agent authority is essential for defining how decisions are made and who is accountable for them.
This is the clearest and most direct form of authority.
It occurs when a principal explicitly tells the agent what they are authorized to do—either in writing or verbally.
Implied authority is not explicitly stated but is assumed as necessary to carry out express authority. It arises from the nature of the agent's duties or customary business practices.
Apparent authority exists when a third party reasonably believes the agent has authority, based on the principal’s behavior or representations—even if that authority was never formally granted.
Understanding how agents interact with principals and third parties is crucial in both legal practice and AI agent system design.
This is the core relationship where the agent acts on behalf of the principal. The principal is liable for the agent’s actions within the defined scope of authority.
This describes the direct interaction between the agent and an external party. The agent’s statements and actions can bind the principal if they’re within their authority.
This is the end-result connection formed as a consequence of the agent’s actions. Even if the principal doesn’t directly engage with the third party, they are still bound by the agreements made by their authorized agent.
In law and business, agency refers to the relationship between a principal and their agent. This concept parallels how AI agents operate under certain types of decision authority.
Understanding AI agents through this lens:
Similar to human agents, AI agents can align with different relationship types:
These legal analogies help us frame AI authority and its implications in automated systems.
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As AI agents become more autonomous and embedded in high-stakes environments like sales, customer service, finance, and legal support, understanding the types of agent authority and agency relationships becomes non-negotiable.
Whether you’re designing a human-agent contract or building a generative AI copilot, clear boundaries must be drawn regarding:
This clarity protects your business, builds trust, and ensures your AI agents—or human ones—operate effectively and ethically.
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