Types of AI agents: Complete guide with examples

Why are AI agents classified into types?

The classification of AI agents is a practical necessity. As AI tools show up in more products, workflows, and decisions, it’s critical to understand how they operate. Knowing which type you're working with helps teams pick the right approach, avoid overengineering, and stay aligned across product, engineering, and leadership.

Many of the terms you'll see today, like “customer agent,” “creative agent,” or “data agent,” describe what the agent is used for, not how it works. Similarly, trends like LLM agents or workflow agents refer to new interfaces or capabilities rather than the underlying mechanics.

That’s why classical agent types still matter. They offer a clear way to think about how an AI system processes information, makes decisions, and handles dynamic environments. There are seven core types of AI agents, ranging from simple reflex-based systems following predefined rules to advanced agents capable of learning and breaking down complex tasks.

7 types of AI agents: Comparison of AI agent types

Below are the seven main types of AI agents, explained with clear definitions, characteristics, use cases, pros, and cons.

1. Simple reflex agents

Simple reflex agents operate on a basic idea: They respond to what's happening right now, without considering past experiences or future consequences. Their decision-making process is defined by fixed rules that map a specific input to a specific action.

Key characteristics:

• These agents operate using predefined rules ("if-then").
• They only react to current input.
• They have no memory, no internal model, and no consideration of future outcomes.

Pros:

• Very fast and efficient
• Easy to design and implement
• Reliable in predictable environments

Cons:

• Can’t handle unfamiliar or dynamic environments
• No capacity for learning from past experiences
• No collaboration with other AI agents

Common use cases:

• Thermostats
• Basic industrial sensors
• Light sensors that switch lamps on and off

Example: A thermostat turns the heater on when the temperature drops below a set threshold and switches it off once the target temperature is reached.

2. Model-based reflex agents

Model‑based reflex agents take things a step further by keeping track of what’s happening beyond the immediate input. They're built for environments where not everything is visible. 

Unlike simple reflex agents that react only to new signals, model-based agents maintain an internal model (a representation of how the world works and how their actions affect it) and adjust their actions accordingly.

Key characteristics:

• These agents use memory to track the environmental state.
• Their decision-making is based on both sensory input and internal context.
• The internal model reflects two things: how the environment changes over time, and how the agent’s actions affect those changes.

Pros:

• More adaptable than simpler agents
• Can handle partial data
• Useful in dynamic or unpredictable settings

Cons:

• Require more system resources
• More complex to design and maintain

Common use cases:

• Smart thermostats and home systems
• Robotic vacuum cleaners
• Obstacle avoidance in self-driving cars 
• Some customer support bots

Example: A robotic vacuum uses sensors and a stored map of a room to navigate efficiently.

3. Goal-based agents

Goal-based agents choose actions by asking a simple question: Which option gets me closer to my goal? Unlike reflex agents that react to the environment or follow fixed rules, they evaluate possible options and select the path that leads to the desired outcomes.

These agents are built for situations where rules aren’t enough, and decisions depend on context, objectives, or changing conditions.

Key characteristics:

• Agents make decisions based on explicit goals.
• To reach a goal, they consider future states rather than just the current one.
• They use search and planning algorithms to evaluate next steps.

Pros:

• Can navigate complex environments
• Adapt when a goal or context changes
• More strategic and intentional than reflex agents

Cons:

• Require clearly defined goals
• Slower than reflex agents due to decision-making overhead

Common use cases:

• Route planning for navigation systems
• Autonomous vehicles
• Non-player characters (NPCs) in video games
• Industrial process control

Example: Google Maps reroutes you based on traffic to reach your destination faster.

4. Utility-based agents

Utility-based agents go beyond simply reaching a goal – they aim to choose the best way to get there. These agents score each option using a utility function that considers multiple factors such as speed, cost, quality, and risk.

Utility functions make trade-offs explicit. They help agents balance competing objectives and make decisions that can be tuned, tested, and evaluated with clarity.

Key characteristics:

• These agents evaluate both the current and possible future states based on their utility.
• They use utility functions to reflect preferences and constraints.
• They select actions that deliver the highest expected benefit.

Pros:

• Nuanced decision-making when handling competing objectives 
• Adapt well to resource limitations or risk-based decisions
• Enable smarter choices in unpredictable situations

Cons:

• Designing utility functions is complex and requires thoughtful consideration of multiple factors
• Decision-making uses significant data and computational resources

Common use cases:

• Investment and trading bots
• Product or content recommendation engines
• Resource allocation systems

Example: An AI that recommends investments based on risk appetite, expected returns, and market volatility.

5. Learning agents

Learning agents are built to improve over time. Instead of relying on fixed rules, they adjust their behavior based on experience, using data from past actions to make better decisions in the future.

They’re built with two key parts: a performance element that handles actions in the moment and a learning element that refines those actions based on outcomes. Different learning methods are used (e.g., supervised and unsupervised learning and reinforcement learning) depending on the problem and the environment.

Key characteristics:

• These agents gather feedback and adjust over time.
• They use data to improve future decisions.
• They can operate in unknown or unstable conditions.

Pros:

• Highly adaptive
• Great for complex and dynamic environments
• Improve with continued use

Cons:

• Need time and data to learn effectively
• May behave unpredictably if not properly guided

Common use cases:

• Autonomous vehicles
• Fraud detection systems
• Medical diagnostics and risk prediction

Example: A customer service chatbot that uses natural language processing (NLP) to refine its answers based on how users respond, learning to adapt tone based on past interactions or escalate for human intervention when needed.

6. Multi-agent systems

Multi-agent systems (MAS) involve several AI agents working in the same environment. All agents operate independently but may coordinate or communicate with others when needed.

These advanced AI systems are useful when a single agent isn’t enough due to scale, complexity, or the need to solve different parts of a problem in parallel.

Key characteristics:

• Each agent acts on its own based on its goals and knowledge.
• Agents communicate, cooperate, or compete to achieve individual or shared objectives.
• Agents work together to solve complex problems more efficiently than they could alone.

Pros:

• Enhances decision-making processes
• Can solve problems too complex for one agent
• Scalable and flexible

Cons:

• Coordination is complex
• Agents may have conflicting goals or duplicate work

Common use cases:

• Smart power grids
• Real-time traffic optimization
• Multiplayer game AI
• Drone fleets or robotic swarms

Example: A group of drones assigned to deliver packages across different locations. Each drone chooses its route independently, but avoids collisions and overlap by sharing information with the others.

7. Hierarchical AI agents

Hierarchical agents are organized into tiers, with higher-level agents overseeing and managing lower-level agents. This structured approach breaks down complex tasks into manageable parts.

By structuring decisions across layers and reusing strategies for repetitive tasks, hierarchical agents support adaptable AI suited to practical business needs. This setup is useful when tasks require multi-stage planning, automation, and coordination, including many use cases of agentic AI.

Key characteristics:

• These systems are organized into layers (high-level agents for planning and low-level agents for execution).
• Each level handles a specific role or responsibility.
• They're designed for structured tasks with multiple steps.

Pros:

• Clear structure improves oversight and control
• Easier to debug, maintain, and scale
• Good balance between control and adaptability

Cons:

• Slower due to layer-by-layer processing
• More complex to design and maintain as systems grow

Common use cases:

• Robotics and industrial automation
• Workflow orchestration in business systems
• Strategic gameplay AI

Example: A smart factory system that helps with quality control, equipment maintenance, scheduling, and supply chain optimization.

Comparison of AI agent types

AI agents are classified based on their level of intelligence, decision-making approach, and how they interact with their environment to achieve specific goals. Understanding these differences is key to choosing the right one for your needs:

• Memory. Simple reflex agents respond only to current input. Model-based, learning, and hierarchical agents use memory to track past events and context.
• Decision-making. Reflex agents follow predefined rules. More intelligent agents (e.g.,  utility-based or hierarchical) evaluate options and strategize.
• Learning. Only learning agents improve over time.
• Complexity. Reflex agents are simple, while hierarchical agents are complex and capable of long-term planning.

If you're building an AI agent for a real-world application, selecting the right type from the start saves time, reduces technical debt, and ensures more predictable results.

How to choose the right type of AI agent

The best type of AI agent depends on what you need it to handle and how much the environment is likely to change. Different agents are built for different levels of complexity, autonomy, and adaptability.

Use this framework to decide:

• Is the environment stable and predictable? → Use a simple or model-based reflex agent for fast responses driven by set rules.
• Do you need to reach defined goals? → Go with a goal-based agent that can plan a path to the outcome.
• Are trade-offs or preferences important in decision-making? → Choose a utility-based agent to evaluate and balance different outcomes.
• Does the agent need to adapt and improve over time? → Use a learning agent that adapts based on data and feedback.
• Do you need multiple agents to solve the problem? → Consider a multi-agent system for distributed problem-solving.
• Are the tasks complex and structured into stages or subtasks? → A hierarchical agent is best for structured decision-making.

How nexos.ai can help

nexos.ai is an all-in-one AI platform that lets you build, run, and scale no-code AI agents to automate real business tasks without writing a single line of code. 

For any team using AI in the workplace, nexos.ai makes it easy to:

• Create custom agents that act on your behalf (e.g., analyzing sales calls, summarizing reports, and drafting posts) based on relevant performance elements.
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• Connect nexos.ai agents to external tools like Slack, SharePoint, or Google Drive.
• Use top-tier AI models like Gemini, Claude, and GPT – all from a single interface.

If you’re looking to automate the repetitive and finally focus on what matters, nexos.ai is built for that. You can even try it for free.