Six Types of AI Agents to Automate Your Workflows in 2025

Generative AI might get all the praise (and some occasional rage), but innovative business leaders are already looking beyond traditional GenAI.

Imagine an autonomous AI system that doesn’t just generate content but makes decisions and takes actions independently to achieve specific goals.

This isn’t science fiction—it’s a reality that’s unfolding right now.

AI that can actually do things, not just talk about them.

AI agents are poised to transform how we work, innovate, and lead–boosting the efficiency and productivity of teams in all industries, and bring organizations immediate ROI from their AI initiatives.

In this article, we’ll get you up to speed on the next big step in the development of Enterprise AI, diving into:

• What AI agents are How they work
• How AI agents work Best examples
• Examples of AI agents–including some that are already household names
• When to use AI agents in your business.‍

What are AI agents?

AI agents are automated intelligent software programs designed to understand their environment, make decisions, and take action to achieve specific goals. These AI-powered systems can operate autonomously or semi-autonomously, adapt to changing conditions, and learn from experience to improve their performance over time.

AI agents are the building blocks of many modern AI applications, powering everything from chatbots and virtual assistants to autonomous vehicles and sophisticated decision-making systems in various industries.

6 Types of AI Agents

1. Simple Reflex Agents

Simple reflex agents are the most basic intelligent agents. They respond to what they see now without using memory or past experiences.

These agents operate on a straightforward "if-then" principle, where specific inputs trigger predefined actions. Despite their simplicity, simple reflex agents can be highly effective in controlled environments where the state of the world is fully observable and the rules are clear-cut.

How It Works

Here's how simple reflex agents work:

1. Understand the environment: The agent uses sensors to gather information about its current surroundings (any system or domain where the agent operates and interacts.)
2. Match current state to rules: The agent compares the perceived state to its predefined set of condition-action rules.
3. Select an action: The agent chooses the corresponding action based on the matched rule.
4. Execute the action: The agent performs the selected action through its actuators.
5. Repeat the process: The agent continuously cycles through these steps, responding to new inputs as they occur.

Best Example

A classic example of a simple reflex agent is a basic thermostat. It operates on a straightforward if-then principle:

• If the temperature is below the set point, turn the heating on.
• If the temperature is above the set point, turn the heating off.

This system doesn't need the memory of past states or predictions of future conditions. It simply reacts to the current temperature reading.

Pros and Cons of Simple Reflex Agents

Pros

• ✅ Fast response times thank to a simple decision-making process
• ✅ Easy to implement and understand
• ✅ Reliable in well-defined, fully-observable environments.

Cons

• ❌ Limited flexibility in complex or changing environments
• ❌ Inability to learn from past experiences or adapt to new situations
• ❌ Not suitable for tasks requiring memory or long-term planning.

While simple reflex agents excel in straightforward, predictable scenarios, their limitations become apparent in more complex or dynamic environments. For example, this AI agent is not the best solution when there is too much data from multiple sensors or cameras.

When to use simple reflex agents:

Simple reflex agents are best suited for environments where:

• The problem domain is well-defined and predictable
• Quick responses are more important than complex decision-making
• The environment is fully observable
• There's no need for learning or adaptation over time.

Examples include basic control systems, simply automated responses in customer service chatbots, and elementary safety systems in manufacturing.

2. Model-Based Reflex Agents

Model-based reflex agents are a bit more advanced than simple reflex agents. They have access to both the external and its own internal world, which gives them more data and flexibility when making decisions.

Here’s how it works:

1. The agent gathers information about its surroundings using sensors or other input devices.
2. The agent analyzes the collected information and determines the best action to achieve its goal.
3. The agent executes actions to reach its goal.
4. After acting, the agent assesses its progress toward the goal and adjusts its actions if necessary.
5. Upon achieving its goal, the agent ceases operation or moves to a new workflow.

Best Example

A good example of a model-based reflex agent is a smart home thermostat, like the Nest Learning Thermostat.

Earlier, we talked about a simple thermostat. Now, with a smart thermostat, it goes beyond sensing the current room’s temperature. The smart thermostat also considers the room’s current temperature and humidity levels in comparison to the following:

• Past temperature patterns
• The number of people in a home
• Current weather conditions based on locations.

This internal model allows the thermostat to make more sophisticated decisions. For instance, it might start heating or cooling before people typically arrive home or adjust its behavior based on seasonal changes.

As the name implies, the Nest thermostat also uses a learning AI agent because it’s supposed to remember and learn the users’ preferences. For example, if a user goes to bed earlier than usual, it can automatically make changes (or suggest the change to be accepted or rejected) based on their current behavior and past preferences.

Pros and Cons of Model-Based Reflex Agents

Pros

• ✅ Can handle partially observable environments
• ✅ Makes more informed decisions than simple reflex agents
• ✅ Adapts better to changing environments.

Cons

• ❌ More complex to design and implement than simple reflex agents
• ❌ Requires more computational resources
• ❌ Performance depends on the accuracy of the internal model.

Model-based reflex agents offer a good balance between simplicity and sophistication. They are best suited for applications where the environment is not fully observable or is subject to change.

When to use model-based reflex agents:

Model-based reflex agents are ideal for situations where:

1. The environment is partially observable
2. The agent needs to maintain some understanding of the world state
3. Quick responses are required, but with more context than simple reflex agents can provide
4. The environment changes over time but in predictable ways.

Examples of model-based reflex agents in business include inventory management systems that predict stock levels based on historical data and trends and automated trading systems that make decisions using market models and real-time information.

Customer relationship management (CRM) tools that personalize interactions based on customer history and behavior patterns also use this type of agent. These applications demonstrate how model-based reflex agents can enhance decision-making and operational efficiency in a diversity of industries.

3. Goal-Based Agents

Goal-based agents work with specific objectives, allowing them to decide how their actions will contribute to achieving their goals.

How It Works

Goal-based agents follow these steps:

1. Observe surroundings: The agent gathers information about its environment.
2. Update knowledge: The agent refreshes its understanding of the situation.
3. Set goals: The agent decides what it wants to achieve.
4. Think of options: The agent considers different actions it could take.
5. Guess results: For each option, the agent imagines what might happen.
6. Compare options: The agent checks how well each possible result matches its goals.
7. Pick the best choice: The agent selects the action most likely to help it reach its goals.
8. Take action: The agent does what it decided to do.
9. Start over: The process begins again with new information and goals.

Best Example

A prime example of a goal-based agent is a GPS navigation system. Its primary goal is to guide the user to their destination efficiently. Here's how it operates:

• Perceives the environment (current location, traffic conditions, road closures)
• Updates its internal map and understanding of the route
• Identifies the goal (reaching the destination)
• Generates possible routes
• Predicts outcomes (estimated arrival times, potential obstacles)
• Evaluates routes based on criteria like shortest time or distance
• Chooses and recommends the best route
• Executes by providing turn-by-turn directions
• Continuously updates and adjusts based on new information.

Pros and Cons of Goal-Based Agents

Pros

• ✅ Can handle complex, multi-step problems
• ✅ Adaptable to changing goals and environments
• ✅ Capable of long-term planning and strategy.

Cons

• ❌ More computationally intensive than simpler agent types
• ❌ Can be slower to make decisions due to complex reasoning
• ❌ Effectiveness depends on the accuracy of goal formulation and outcome predictions.

Goal-based agents excel in scenarios where achieving specific objectives is crucial, and the path to those objectives may not be straightforward. They're particularly valuable in dynamic environments requiring flexibility and strategic thinking (like considering traffic conditions).

When to use goal-based agents:

Goal-based agents are ideal for situations where:

1. The environment is complex and dynamic
2. Long-term planning and strategy are required
3. The agent needs to adapt to changing goals or conditions
4. Multiple steps or decisions are needed to achieve an objective.

Goal-based agents have practical applications across many industries. For example, advanced customer service chatbots can use these agents to navigate complex queries through multi-step interactions.

As we move from goal-based to utility-based agents, we'll see how decision-making can be further refined by considering goals and the relative value or utility of different outcomes.

4. Utility-Based Agents

Utility-based agents are an advanced form of goal-based agents that consider whether an action will achieve a goal and evaluate how desirable the outcome of that action is. These agents use a utility function to assign a value to each possible outcome, allowing them to make decisions that maximize overall utility or satisfaction.

How It Works

Utility-based agents operate as follows:

1. Perceive the environment and update the internal state
2. Generate possible actions
3. Predict outcomes for each action
4. Calculate the utility of each outcome using a utility function
5. Choose the action that maximizes the expected utility
6. Execute the chosen action
7. Update knowledge based on the results.

Best Example

A real-world example of a utility-based agent in finance is the AI-powered robo-advisor Wealthfront. This automated investment service uses machine learning algorithms to make investment decisions based on a client's risk tolerance, financial goals, and market conditions.

Here's how Wealthfront's AI agent operates:

• Perceives market data, economic indicators, and individual client information
• Generates possible investment strategies and portfolio allocations
• Predicts potential outcomes for each strategy
• Calculates utility based on factors like expected returns, risk levels, tax efficiency, and client preferences
• Chooses the investment strategy with the highest expected utility
• Executes trades and rebalances portfolios automatically
• Continuously learns and adapts its strategies based on market performance and client feedback.

Pros and Cons of Utility-Based Agents

Pros

• ✅ Can make sophisticated decisions in complex, uncertain environments
• ✅ Able to balance multiple competing objectives
• ✅ Can handle scenarios where the "best" outcome isn't always clear-cut.

Cons

• ❌ Designing appropriate utility functions can be challenging
• ❌ Computationally intensive, especially in complex environments
• ❌ May struggle in scenarios where utility is difficult to quantify.

Utility-based agents excel when decision-making involves weighing multiple factors, and the best course of action isn't always obvious. They're particularly valuable in finance, resource allocation, and complex optimization problems.

When to use utility-based agents:

Utility-based agents are ideal for situations where:

1. Multiple competing objectives need to be balanced.
2. The environment is complex, and outcomes are uncertain.
3. Decision quality can be quantified in terms of desirability or value.
4. There's a need to make trade-offs between different outcomes.

In addition to automated trading systems, utility-based agents find applications in energy grid management (balancing supply, demand, and costs) and recommendation systems (considering user preferences and item characteristics).

5. Learning Agents

Learning agents are AI systems that can improve their performance through experience. These agents can learn from their interactions with the environment, adapt to new situations, and enhance their decision-making capabilities.

How It Works

Learning agents typically operate through the following steps:

1. Perceive the environment and collect data
2. Process and analyze the collected information
3. Update internal knowledge or model based on new insights
4. Adjust decision-making strategies or parameters
5. Take actions based on updated knowledge
6. Evaluate the outcomes of actions
7. Use feedback to further refine the learning process.

Best Example

A prime example of a learning agent is DeepMind's AlphaGo, an AI system designed to play the complex board game Go. AlphaGo demonstrated remarkable learning capabilities, ultimately defeating world champion Go players.

Here's how AlphaGo operates as a learning agent:

• Perceives the Go board state and possible moves
• Analyzes millions of potential game scenarios
• Updates its strategic knowledge based on game outcomes
• Refines its decision-making process through reinforcement learning
• Executes moves based on learned strategies
• Evaluates the effectiveness of its decisions after each game
• Continuously improves its play style through self-play and analysis.

The learning process of AlphaGo involves complex neural networks and reinforcement learning algorithms.

Pros and Cons of Learning Agents

Pros

• ✅ Can adapt to changing environments and new situations
• ✅ Improves performance over time without explicit reprogramming
• ✅ Can discover novel strategies and solutions.

Cons

• ❌ May require large amounts of data and training time
• ❌ Can be computationally intensive, especially during the learning phase
• ❌ May produce unexpected or undesirable behaviors if not properly constrained.

Learning agents are particularly valuable in dynamic, complex environments where optimal strategies are not known in advance or may change over time. They excel in tasks that require continuous improvement and adaptation.

When to use learning agents:

Learning agents are ideal for situations where:

1. The environment is complex and dynamic
2. Optimal strategies are not known beforehand
3. The system needs to improve its performance over time
4. There's a need to discover new patterns or strategies.

In addition to game-playing AI like AlphaGo, learning agents find applications in recommendation systems, autonomous vehicles, and predictive maintenance systems in manufacturing. These applications demonstrate how learning agents can self-improve and change their performance.

6. Hierarchical Agent System

Hierarchical agents are AI systems that break down complex tasks into simpler subtasks, organizing them in a hierarchical structure. This type has two agents: lower-level agents and high-level agents. This approach allows the agent to manage different levels of abstraction and handle intricate problems more efficiently.

How It Works

Hierarchical agents typically operate through the following steps:

1. Decompose the main task into subtasks and sub-subtasks
2. Create a hierarchical structure for these tasks
3. Assign specialized sub-agents or modules to handle specific subtasks
4. Coordinate between different levels of the hierarchy
5. Aggregate results from lower levels to make higher-level decisions
6. Adjust the hierarchy or subtask allocation as needed

Best Example

A more specific example of a hierarchical agent in Amazon's technology ecosystem is the Amazon Go store's "Just Walk Out" technology. This system demonstrates a clear hierarchical structure in its operation:

• Top-level: Overall store management and inventory tracking
• Mid-level: Customer tracking and behavior analysis
• Low-level: Product recognition and interaction detection
• Lowest-level: Sensor data processing and fusion.

Here's how this hierarchical agent operates in the "Just Walk Out" system:

1. Top-level: Manages overall store operations, updates inventory, and handles high-level customer data.
2. Mid-level: Tracks individual customers throughout the store, analyzes shopping patterns, and manages virtual shopping carts.
3. Low-level: Recognizes specific products being taken or returned to shelves, and detects customer-product interactions.
4. Lowest-level: Processes raw data from various sensors (cameras, weight sensors, etc.) and fuses this information for higher-level analysis

This hierarchical structure allows the "Just Walk Out" technology to manage the complexities of a cashier-less store by breaking down the task into manageable components.

You can probably tell that this technology is a combination of different other types of AI agents. While we classified it as a hierarchical agent in this section, this technology–depending on the specific action and result that is taken–can also be considered part of the "model-based" and "learning" agent categories.

Pros and Cons of Hierarchical Agents

Pros

• ✅ Can handle extremely complex, multi-faceted problems
• ✅ Allows for specialized sub-agents to tackle specific subtasks
• ✅ Improves scalability and maintainability of AI systems.

Cons

• ❌ Can be challenging to design and implement effectively
• ❌ May require significant computational resources
• ❌ Coordination between levels can be complex.

Hierarchical agents excel in scenarios when there are too many steps, sources of raw data, and multiple objectives at each step. They're particularly valuable in robotics, autonomous systems, and complex decision-making processes.

When to use hierarchical agents:

Hierarchical agents are ideal for situations where:

1. The problem is too complex to be solved by a single, monolithic agent
2. There's a clear hierarchy or structure to the problem space
3. Different subtasks require specialized knowledge or approaches
4. The system needs to operate at multiple levels of abstraction.

In addition to autonomous driving, hierarchical agents also find applications in robotic control systems, complex game AI (like strategic video games), and large-scale industrial process control. These applications showcase how hierarchical agents can manage intricate, multi-layered problems by breaking them into more manageable components.

How to Choose the Right AI Agent Type for Your Business

When choosing the right AI agent type for your business needs, consider the following guidelines:

• Resources: Think about the computing power, energy it will consume, cost, and AI expertise you have available.
• Legal and ethical issues: Different AI types can affect privacy, data protection, and decision transparency differently.
• Scalability and compatibility: Make sure the AI can grow with your needs and work well with your current systems.
• Maintenance: Consider how much upkeep the AI will need over time.

Also, your decision should take these practical factors into account:

• User experience: How will the AI affect your customers or users?
• Human oversight: How much human supervision will the AI need?
• Handling unexpected situations: Can the AI deal with surprises or errors?
• Explainability: Can the AI explain its decisions, especially in regulated industries?
• Future-proofing: Can the AI adapt to your changing business needs?

Future-proofing also means finding the right partner to help you implement your AI systems. The thing is, you don’t have to implement everything by yourself. Partnering with companies like Arcee AI allows you to use pre-trained models and an end-to-end system that make execution processes consistent and repeatable.

Remember, the best AI type for you depends on your specific project needs, available resources, and the complexity of the problem you're trying to solve.

FAQs

What is the best type of AI agent?

There's no universally "best" AI agent type. The ideal choice depends on the specific task, environment, and constraints. Simple reflex agents work for basic tasks, while learning agents excel in complex, dynamic environments. Goal-based and utility-based agents are suited for decision-making scenarios. The best type is the one that most effectively solves your particular problem.

Is ChatGPT an AI agent?

No, ChatGPT is technically considered a large language model. Although a few Custom GPTs may be used as goal-based agents, ChatGPT is not an AI agent in the traditional sense. It cannot interact with its environment, make decisions, or take actions beyond generating text based on input. Instead, it's a sophisticated natural language processing model trained to understand and generate human-like text.

What is the most popular type of AI agent?

The most popular type of AI agent often depends on the application. In autonomous driving, for example, Waymo uses a combination of learning agents and hierarchical agents. Waymo's self-driving cars employ machine learning to improve driving decisions over time while using a hierarchical structure to manage different aspects of driving, from route planning to obstacle avoidance.

Are you ready to implement AI agents?

Now you know about the six types of AI agents, ranging from the most simple to agents that are so complex they're able to learn and continuously improve.

If you’re thinking of implementing AI agents in your organization, we recommend starting with something simple using the resources you already have, and keep scalability in mind.

And if you're ready to learn how to combine AI agents with the power of Small Language Models (SLMs), book a call with the Arcee AI team!