---
title: "Types of AI Agents: A practical guide to distinguish them"
url: https://comunicagenia.com/en/blog/types-of-ai-agents-a-practical-guide-to-distinguish-them/
date: 2025-09-10T06:08:41+00:00
modified: 2025-09-10T06:08:41+00:00
lang: en_US
---

# Types of AI Agents: A practical guide to distinguish them

Artificial intelligence (AI) agents are autonomous entities that **perceive** their environment (whether physical or virtual) through sensors and **act** on it through actuators to achieve specific objectives.

In a **physical agent**, the sensors can be cameras, microphones or proximity detectors, and the actuators, motors or speakers. In a **software agent**, sensors are input data (such as text, clicks, or activity logs) and actuators are their outputs (responses, recommendations, or actions in a system).

A common mistake when starting out in this field is confusing the term operator with a type of agent. In reality, **an operator is an elementary action that an agent performs**, such as “open a door”, “move left” or “send a message”. They are the basic blocks with which agents build more complex behaviors.

With this clear foundation, we can explore the **classification of agents proposed by Russell and Norvig**, which ranges from simple systems to others capable of reasoning, planning and learning from experience.

## Simple reactive agents

They are the most basic. They follow rules like “if X happens, do Y.” They lack memory and do not consider future consequences: they react only to what they perceive at the moment.

**How ​​they work:** using predefined condition-action rules.

**What they are for:** simple tasks in fully observable environments.

**Real example:** a thermostat that turns on the heating when the temperature drops below a threshold, or a robot vacuum cleaner that recoils when it hits an obstacle.

## Model-based reactive agents

They are a key improvement: they maintain an internal model of the environment. This allows them to act even when not all information is immediately observable.

**How ​​they work:** they update an internal state based on past and present perceptions.

**What they are for:** tasks in partially observable environments.

**Real example:** an advanced Roomba vacuum cleaner that builds a map of the room, remembers areas already cleaned, and locates obstacles.

##Goal-based agents

These agents don't just react: they plan. They evaluate different sequences of actions to achieve a specific objective.

**How ​​they work:** they apply search and planning algorithms.

**What they are for:** problems that require finding routes or strategies.

**Real example:** applications like Google Maps or Waze. Your goal is to get home, and the agent calculates the best route considering traffic, distances and closures. If conditions change, recalculate the plan.

## Utility-based agents

They go one step further: they not only seek to achieve a goal, but do it in the best possible way. They use a utility function that measures the quality of the result, balancing factors such as risk, cost or time.

**How ​​they work:** they compare alternatives based on their expected utility.

**What they are for:** Complex decisions with multiple variables.

**Real example:** an autonomous car. It's not enough to reach your destination: you must do so while maximizing safety, comfort, energy efficiency and speed.

## Learning agents

They are the most advanced: they improve with experience. They incorporate a learning mechanism that analyzes the results of their actions and adjusts their future behavior. This is the type of AI agents with the most development these days.

**How ​​they work:** they apply machine learning and reinforcement learning techniques.

**What they are for:** dynamic and changing environments.

**Real example:** Netflix or Spotify recommendation systems, which learn from your habits to suggest content that is increasingly tailored to your tastes.

## The future: emerging agents

Beyond this classification, the evolution of AI shows us new trends:

- Agents based on language models (LLM): such as ChatGPT with tools or plugins, capable of reasoning and adapting to multiple tasks.

- Multi-agent systems: where different agents collaborate or compete, as in Waymo's traffic simulations to train autonomous vehicles.

These developments reflect a clear path: from simple reaction to continuous adaptation and complex reasoning.

AI agents, in all their forms, show how a set of rules, objectives and learning can be transformed into **systems capable of tackling everything from routine tasks to open and changing problems**.

Understanding its types (and the role traders play as fundamental stocks) is the first step in exploring the future of artificial intelligence and its real-world applications.