Types of AI Agents

AI Agents can be built with different levels of capability. The simplest ones are often enough for simple tasks and avoid unnecessary complexity.


There are 5 types of AI Agents, ordered from the simplest to the most complex:


  • 1. Simple Reflex Agents


    Simple Reflex Agents are the most basic type of agent and act based on what they perceive. They have no memory and don't interact with other agents if they lack information.

    They work through a set of preprogrammed rules or “reflexes” that trigger actions when certain conditions are met.

    If the agent encounters a situation it wasn't prepared for, it can't respond properly. They're effective in fully observable environments, where they have access to all the information they need.


    Example: a thermostat that turns on the heat every day at 7 pm.

    Simple Reflex AI Agent working with preprogrammed rules

  • 2. Model-Based Reflex Agents


    These agents act based on what they perceive, but they also have memory to keep an internal model of the environment.

    When they receive new information, they update that model and make decisions based on their prior perceptions, the current state and their rules.

    They can operate in partially observable environments, though they're still limited by the rules they were programmed with.


    Example: a robot vacuum that detects furniture, avoids obstacles and remembers which areas it already cleaned so it doesn't repeat spots.

    Model-Based Reflex Agent with memory and environment perception

  • 3. Goal-Based Agents


    Goal-Based Agents have an internal model of the world and also defined goals.

    These agents analyze different sequences of actions to reach a goal and plan before acting.

    Thanks to this planning ability, they're more effective than simple reflex agents and model-based agents.


    Example: a navigation system that analyzes different routes and recommends the fastest way to reach a destination.

    Goal-Based Agent analyzing routes and making decisions

  • 4. Utility-Based Agents


    These agents don't just aim to reach a goal, they aim to do it in the best possible way.

    To decide, they compare different options and choose the one that offers the greatest benefits or best results.

    They use a utility function, which measures how favorable each option is based on criteria like time, cost, resource consumption or efficiency.

    They're useful when there are several ways to achieve the same goal and the most convenient alternative needs to be chosen.


    Example: a navigation system that tries to save fuel, avoid traffic and reduce toll costs when recommending a route.

    Utility-Based Agent optimizing time, costs and resources

  • 5. Learning Agents


    Learning Agents have the ability to learn from experience.

    As they interact with the environment, they store information and automatically improve their performance.

    Thanks to this learning ability, they can better adapt to new or unfamiliar situations.

    These agents usually have four main components:

    • Learning element: gathers information from the environment and improves the agent's knowledge.
    • Critic: evaluates whether the actions or responses were correct.
    • Performance element: decides which action to take.
    • Problem generator: proposes new actions or alternatives to try.

    Example: personalized recommendations in online stores, where the system learns from each user's searches and purchases to offer increasingly accurate recommendations.

    AI Learning Agent adapting through experience