5 Types of AI Agents and How They Make Decisions (+ Use Cases)

Everyone’s talking about AI agents these days, and it’s no surprise. The global market size was estimated at $5.4 billion in 2024 and is projected to grow at a CAGR of 45.8% from 2025 to 2030. Everyone, from game developers to healthcare companies, is getting on board the agentic AI train. Why? Because these agents can automate all kinds of workflows. 

But not all agents are equal – at least in decision-making capabilities. Some rely more on context and memory. While others continuously learn and improve with time. So, which types of AI agents are best for your specific needs?

What Are AI Agents?

Based on definitions from AIMA, AI agents are systems that:

• Operate on a device
• Take data
• Make logical decisions
• Act autonomously to reach certain goals.

AI agents mimic human reasoning and make decisions based on their environment. Many can also learn and adapt over time.

We’ve shortlisted five key types, including examples of how they can be applied in a game setting.

5 Types of AI Agents by Decision Logic

The following five AI agents differ in what decisions they can make.

1. Simple Reflex Agents

These agents are extremely reactive and conditional. They operate without memory or context, making decisions solely on what they perceive in real time. Simple reflex agents only have the current situation to do their job. 

Key Components

• Sensors: Collecting basic input data from their environment.
• Condition-action rules: Predefined rules that determine reactions to inputs.
• Actuators: Execute the agent’s specific decisions.
• No internal state or memory: Not retaining past data, making it impossible to adapt.
• Limited scope: Can only consider current data. Only effective in basic, structured environments.

Use Case (Platformer Game)

A simple reflex agent might control an enemy character (e.g., patrolling enemy). It might attack the player if it comes close. 

So, the enemy demonstrates the same behavior because it has no memory of the player’s past actions.

2. Model-Based Reflex Agents

These agents have their own internal model of the world. They don’t simply follow commands based on predefined rules. This model tracks how the environment evolves, and it considers past events, allowing the agent to make better decisions. 

Key Components

• Internal model: Tracks evolving relationship between agent’s actions and environment.
• Condition-action rules: Uses if-then rules and incorporates data from internal model.
• State tracking: Monitors environment’s past and current states to inform future actions.
• Contextual decision-making: Uses past actions and immediate inputs to inform decisions.

Use Case (Stealth Game)

This agent controls enemy guards that react to the player’s movement while maintaining an internal model of their environment. 

These guards track the player’s most recent position. Even if the player escapes, the agent updates the internal model and continues searching.

3. Goal-Based Agents

These agents are built around achieving a specific goal. And they exhaust their options to reach it. This means that goal-based agents consider future consequences. They use search and planning algorithms to take the most efficient paths to get to their desired destination. 

Key Components 

• Goal-oriented: Specific objectives that drive decision-making.
• Planning capabilities: Devises plans and action sequences to achieve goals.
• State evaluation: Evaluates states based on potential to help reach goals.
• Flexibility: Adapt strategy if it’s not working.
• Problem-solving: Handles complex situations that lead to various outcomes.

Use Case (Strategy Game)

A goal-based agent could dynamically adjust objectives for in-game enemies. The AI evaluates the current state and chooses the most relevant actions. 

Let’s say a player’s army grows stronger. The agent might use this information to prioritize building alliances with other enemies.

4. Utility-Based Agents

While goal-based agents seek the optimal path to reach a goal, utility-based agents measure the quality of each outcome. This makes them extremely useful in moments of uncertainty. They provide multiple solutions to the same problem, evaluating the best decisions to make.

Key Components

• Utility function: Evaluating the most ideal pathways to take.
• Decision-making mechanism: Choosing the best actions to maximize utility. 

Use Case (Shooter Game)

A utility-based AI opponent dynamically adjusts difficulty according to the player’s skill level. It achieves this by evaluating factors such as success rate, accuracy, and speed. 

By tailoring challenges to each player’s experience, the AI ensures each player is adequately challenged.

5. Learning Agents

Highly adaptable, learning agents start with a foundation of basic skills and knowledge. With each experience, they receive feedback to learn and adjust their performance. Ideal for user-centric, personalized systems and applications.

Key Components

• Learning element: Learns based on user input, data analysis, and experience.
• Critic element: Compares agent’s decisions with expected results and gives feedback.
• Performance element: Uses external actions to inform decisions.
• Problem generator: Improves learning by introducing new challenges.

Use Case

A learning agent could be used in games:

1. The agent learns the game’s fundamentals, managing to execute basic tasks and moves. 
2. As this happens, the learning agent observes the game in finer detail. 
3. Based on this added context, the AI begins to make more sophisticated, informed moves. 
4. It receives feedback on whether its moves were successful or not, which it uses to enhance strategies. 
5. Through iterative play, the agent gradually becomes a better player.

Pros and Cons of Each AI Agent

Here are some advantages and limitations that come with each type of AI agent.

AI Agent Type	Pros	Cons
Simple Reflex Agent	– Easy to design and implement – Real-time responses to changes – No need for extensive training	– Can’t learn from past interactions, limiting adaptability – Can’t make decisions or adapt to complex situations – Struggles in partially observable environments
Model-Based Reflex Agent	– Efficient at decision-making – Adaptable to environmental changes	– Demands many resources and computational power – Agent is only as good as its model – Not the most adaptable agent
Goal-Based Agent	– Simple to implement – Easy to evaluate performance – Can be used in various applications	– Can struggle to adapt to changing environments – Might not make optimal decisions without complete information – Potential planning limitations
Utility-Based Agent	– Handles various decision-making problems – Learns and adjusts to decision-based strategies – Works well in uncertain environments	– Utility function design usually requires high technical expertise – May raise ethical questions
Learning Agent	– Highly adaptable as it learns from experience – Performs better over time, leading to better decision -making – Agent makes more accurate predictions	– Requires an accurate model of environment, which is subject to change – Can be computationally expensive – A huge dependency on quality data

What Types of AI Agents Are Best for Your Game?

Choosing the right AI agent completely depends on your specific needs and goals. For predictable interactions, leveraging simple reflex agents is probably the way to go. 

But if you want to see continuous improvement in your application, then it’s best to build and utilize learning agents. 

Usually, though, it makes sense to use multiple types of AI agents to balance efficiency, creativity, and adaptability in your game.