{"id":24963,"date":"2025-09-10T08:08:41","date_gmt":"2025-09-10T06:08:41","guid":{"rendered":"https:\/\/comunicagenia.com\/blog\/types-of-ai-agents-a-practical-guide-to-distinguish-them\/"},"modified":"2025-09-10T08:08:41","modified_gmt":"2025-09-10T06:08:41","slug":"types-of-ai-agents-a-practical-guide-to-distinguish-them","status":"publish","type":"post","link":"https:\/\/comunicagenia.com\/en\/blog\/types-of-ai-agents-a-practical-guide-to-distinguish-them\/","title":{"rendered":"Types of AI Agents: A practical guide to distinguish them"},"content":{"rendered":"

Artificial intelligence (AI) agents are autonomous entities that perceive<\/strong> their environment (whether physical or virtual) through sensors and act<\/strong> on it through actuators to achieve specific objectives.<\/p>\n

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

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<\/strong>, such as \u201copen a door\u201d, \u201cmove left\u201d or \u201csend a message\u201d. They are the basic blocks with which agents build more complex behaviors.<\/p>\n

With this clear foundation, we can explore the classification of agents proposed by Russell and Norvig<\/strong>, which ranges from simple systems to others capable of reasoning, planning and learning from experience.<\/p>\n

Simple reactive agents<\/h2>\n

They are the most basic. They follow rules like \u201cif X happens, do Y.\u201d They lack memory and do not consider future consequences: they react only to what they perceive at the moment.<\/p>\n

How \u200b\u200bthey work:<\/strong> using predefined condition-action rules.<\/p>\n

What they are for: <\/strong>simple tasks in fully observable environments.<\/p>\n

Real example: <\/strong>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.<\/p>\n

Model-based reactive agents<\/h2>\n

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.<\/p>\n

How \u200b\u200bthey work: <\/strong>they update an internal state based on past and present perceptions.<\/p>\n

What they are for:<\/strong> tasks in partially observable environments.<\/p>\n

Real example:<\/strong> an advanced Roomba vacuum cleaner that builds a map of the room, remembers areas already cleaned, and locates obstacles.<\/p>\n

##Goal-based agents<\/p>\n

These agents don’t just react: they plan. They evaluate different sequences of actions to achieve a specific objective.<\/p>\n

How \u200b\u200bthey work: <\/strong>they apply search and planning algorithms.<\/p>\n

What they are for: <\/strong>problems that require finding routes or strategies.<\/p>\n

Real example:<\/strong> 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.<\/p>\n

Utility-based agents<\/h2>\n

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.<\/p>\n

How \u200b\u200bthey work: <\/strong>they compare alternatives based on their expected utility.<\/p>\n

What they are for:<\/strong> Complex decisions with multiple variables.<\/p>\n

Real example: <\/strong>an autonomous car. It’s not enough to reach your destination: you must do so while maximizing safety, comfort, energy efficiency and speed.<\/p>\n

Learning agents<\/h2>\n

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.<\/p>\n

How \u200b\u200bthey work: <\/strong>they apply machine learning and reinforcement learning techniques.<\/p>\n

What they are for:<\/strong> dynamic and changing environments.<\/p>\n

Real example:<\/strong> Netflix or Spotify recommendation systems, which learn from your habits to suggest content that is increasingly tailored to your tastes.<\/p>\n

The future: emerging agents<\/h2>\n

Beyond this classification, the evolution of AI shows us new trends:<\/p>\n

    \n
  • Agents based on language models (LLM): such as ChatGPT with tools or plugins, capable of reasoning and adapting to multiple tasks.<\/li>\n<\/ul>\n
      \n
    • Multi-agent systems: where different agents collaborate or compete, as in Waymo’s traffic simulations to train autonomous vehicles.<\/li>\n<\/ul>\n

      These developments reflect a clear path: from simple reaction to continuous adaptation and complex reasoning.<\/p>\n

      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<\/strong>.<\/p>\n

      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.<\/p>\n","protected":false},"excerpt":{"rendered":"

      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 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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…","_links":{"self":[{"href":"https:\/\/comunicagenia.com\/en\/wp-json\/wp\/v2\/posts\/24963","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/comunicagenia.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/comunicagenia.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/comunicagenia.com\/en\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/comunicagenia.com\/en\/wp-json\/wp\/v2\/comments?post=24963"}],"version-history":[{"count":0,"href":"https:\/\/comunicagenia.com\/en\/wp-json\/wp\/v2\/posts\/24963\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/comunicagenia.com\/en\/wp-json\/wp\/v2\/media\/20535"}],"wp:attachment":[{"href":"https:\/\/comunicagenia.com\/en\/wp-json\/wp\/v2\/media?parent=24963"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/comunicagenia.com\/en\/wp-json\/wp\/v2\/categories?post=24963"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/comunicagenia.com\/en\/wp-json\/wp\/v2\/tags?post=24963"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}