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5 types of AI agents and how to choose the right one
AI agents vary in design and purpose. Use this guide to explore different types of AI agents and determine which ones are best suited for various use cases.
While all AI agents carry out tasks autonomously, how they interpret user input and complete tasks can vary. Hence, there are various types of AI agents, each with unique inner workings and optimal use cases.
AI agents are autonomous systems capable of carrying out business workflows and actions, such as installing software applications, configuring user accounts or posting content to social media.
The ability to perform complex actions makes AI agents different from other types of AI systems, such as chatbots. A chatbot answers questions and shares information with users, while an AI agent can undertake complex actions, often involving multiple steps that the agent performs independent of human prompting.
There are five main types of AI agents: simple reflex, model-based reflex, goal-based, utility-based and learning agents. Understanding the differences between each type can help businesses choose the right type of agent for a given need.
5 types of AI agents
AI agents are categorized by how they work. For example, simple reflex agents use predefined rules to guide actions, whereas other agent types use AI models to function.
Moreover, different types of agents focus on different ways to solve problems. For instance, goal-based agents work backwards from a goal to identify the steps needed to reach it. In contrast, utility agents consider multiple goals to make workflows as efficient as possible.
The following five types of agents each have unique characteristics and capabilities for organizations to consider.
1. Simple reflex agents
Simple reflex agents are the most basic type of AI agent in widespread use today. They work by using predefined rules to determine how to respond to a user request.
For example, consider an agent whose purpose is to help drivers determine the fastest way to move from a point of origin to a destination. If the agent has access to preplanned routes between the two locations, it can provide instructions. However, it can't modify routes or add locations not represented in its data.
Simple reflex agents can only respond to requests that align with their predefined rules. However, an advantage of simple reflex agents is that they are relatively easy to implement because they don't require complex technology. In addition, agent behavior is predictable and consistent because developers can define how agents should respond to a given condition.
2. Model-based reflex agents
As the term implies, a model-based reflex agent uses an AI model to guide actions. The model could be a large language model, which is helpful in cases where agents must interpret a wide range of input. Model-based reflex agents might also use smaller models to support a more limited range of actions.
Model-based reflex agents work by using a model to predict how an action will change the state of an environment. For example, consider an agent designed to navigate a self-driving car through a city. The agent would approach the task step-by-step, anticipating how each left or right turn changes the car's location. By repeating this process, the agent guides the car toward its ultimate destination.
Model-based reflex agents excel in situations where the agent doesn't know all variables ahead of time. This might be useful if, for instance, a self-driving car must navigate a city where traffic conditions change rapidly. Working through the trip step-by-step and reacting in real time to problems like a blocked intersection are helpful.
That said, because model-based reflex agents focus on step-by-step problem-solving, they might not work as efficiently as agents that focus on achieving a desired outcome as quickly as possible.
3. Goal-based agents
Like model-based reflex agents, goal-based agents also use an AI model to guide actions. The key difference is that, instead of trying to arrive at a particular outcome by determining which steps will lead there, goal-based agents work backwards. They first identify the goal they want to achieve, then determine how to achieve it.
For instance, instead of guiding a car step-by-step toward a destination, a goal-based agent would identify the entire route necessary to arrive at the destination and then follow that route. The route could end up being the same as the one charted by a model-based reflex agent, but the process of determining it is different.
Goal-based agents are best for situations where conditions are predictable and consistent. For instance, while a goal-based agent might not be ideal for guiding a car through a city with unpredictable traffic conditions, it could work better for charting a long-distance travel route, where localized problems -- like a traffic jam that delays traffic by a few minutes -- don't have a significant effect on the overall outcome.
However, a goal-based agent might fail when facing conditions it did not expect to encounter because they were not represented in its model.
4. Utility-based agents
A utility-based agent distinguishes itself from other agent types by focusing on maximizing the overall efficiency of its actions.
Utility-based agents weigh the benefits and drawbacks of each possible approach to completing a task and determine which method will maximize the pros and minimize the cons.
For instance, a utility-based agent that navigates a self-driving car would consider more than just how to get from point A to point B as quickly as possible. It might also factor in gas efficiency, tolls and access to rest stops or service stations, since these factors matter to many passengers.
Utility-based agents work well when optimizing for multiple competing goals is desirable. However, they're less efficient in scenarios where one clear priority exists because they waste resources assessing irrelevant criteria.
5. Learning agents
Learning agents use the output of previous actions to guide future actions. This distinguishes them from agents that rely only on AI models to determine how they approach a problem.
The main advantage of learning agents is that they can accommodate unexpected variables. For instance, imagine an agent guiding a self-driving car. When the car attempts to make a turn identified as part of the route, its sensors detect that the road is closed. In this case, the agent would learn from this outcome that the data represented in its model is inaccurate and would change its behavior accordingly by turning and rerouting the car.
Hierarchical vs. flat agents
In a hierarchical agent system, a manager agent delegates tasks to sub-agents. For example, a manager agent navigating a self-driving car might identify the calculations it needs to chart a route -- such as determining where to turn and evaluating how to optimize gas -- and then instruct other agents to perform each calculation.
This is distinct from a flat model, in which a single agent performs a task independently. Multiple agents can exist in a flat system, but they operate alongside each other rather than having a manager agent delete tasks.
Hierarchical agents are not an agent type because the term doesn't refer to internal agent design or logic. Instead, agent hierarchies organize agent activity and coordination. A hierarchical agent system can use any type of AI agent, and multiple agent types can function within one hierarchical system.
Choosing the best AI agent type
No AI agent type is universally better than another. Different agents excel at various tasks and come with their own challenges.
Table 1 highlights the main benefits and drawbacks of the five common types of AI agents:
| Agent type | Pros | Cons |
| Simple reflex | Easy to implement and has predictable behavior. | Functionality is limited to predefined condition-action logic. |
| Model-based reflex | Can adapt to changing or unknown conditions. |
Might not provide the most efficient answers to problems. |
| Goal-based | Adept at identifying the most efficient way to complete or solve a task. | Might struggle when faced with unexpected conditions not represented in the agent's model. |
| Utility-based | Excels at finding the optimal way to complete a task that involves multiple considerations or goals. | Can be unnecessary for use cases where only one goal matters. |
| Learning | Can account for and correct inaccuracies or gaps in an AI model's guidance. | Requires historical data to make optimal decisions, causing them to be less effective when historical information is limited. |
Table 1. AI agent types, pros and cons.
To decide which agent type to use for a given task, first assess available agent types, starting with the simplest and moving on until you've identified an agent that meets your requirements. This approach lets you select the simplest type of agent capable of meeting your needs.
Once you've determined the scope of a given task, ask yourself these five questions:
1. Can an agent complete the task with pre-formulated responses or actions that align with predefined conditions?
If so, a simple reflex agent is usually the best type of agent to choose.
2. Does the task have predictable conditions and a clearly defined goal, where the only relevant criterion is to meet that goal?
In this case, a goal-based agent likely works best.
3. Is there a clear goal, but unpredictable or unknown conditions?
A model-based reflex agent works best in this scenario.
4. Is it necessary to optimize multiple factors or goals while completing the task?
If yes, choose a utility-based agent.
5. Does the task require unpredictable or changing variables?
In this case, a learning agent might work best.
Some degree of overlap exists between AI agent types. For instance, a learning agent could also be a utility-based agent if it is designed to learn from historic outputs while also optimizing for multiple criteria. Thus, in some cases, it might be possible to combine the features of multiple agent types when implementing agentic AI workflows.
A strategic approach to AI agent implementation
While choosing the best type of AI agent is valuable, don't focus so much on the various agent types that you miss other key aspects of AI agent implementation.
No matter which specific category of agent -- or combination -- you deploy, your implementation plan should include the following considerations:
- Framework. If you're building an agent from scratch rather than purchasing an off-the-shelf tool, which agentic AI framework will you use to develop it?
- Agent deployment. How will you expose the resources, such as databases or software utilities, that agents need to support an intended use case?
- Agent security. Which safeguards will you erect to minimize agentic AI security risks? How will you ensure that agents can't access or modify resources that should not be available to them?
- Performance measurement. How will you measure the agent's performance and ensure it creates your intended business value?
These factors -- just as much as agent type -- determine the value of agentic AI projects.
Chris Tozzi is a freelance writer, research adviser and professor of IT and society. He has previously worked as a journalist and Linux systems administrator.
