Types of AI Agents to Optimize Your Workflows in 2025

Artificial intelligence and machine learning have transformed how machines interact with the world. These AI agents are autonomous systems that can perceive, reason, and act smartly to achieve specific goals.

The AI agent types vary from simple rule-based or condition-based systems to advanced self-learning systems. They use large language models (LLMs) that rely on machine learning (ML) and natural language processing (NLP) to handle various tasks. These AI agents have LLMs, tool integrations, and memory systems, enabling them to read, interpret, and generate human-like text.

The AI agent architecture is not limited to a standard type but has multiple architectures to solve complex problems, including ReAct and ReWoo. Based on these architectures, various AI agents are available to solve problems, ranging from simple to complex tasks.

Types of AI agents

There are 6 basic types of AI agents: simple reflex agents, model-based reflex, goal-based agents, multi-agent systems, learning agents, utility-based agents, and hierarchical agents. Each AI agent has a unique strength and abilities that can help you solve basic to complex queries. The types of AI agents with their real-world use cases:

Simple reflex agents

They are the most straightforward AI agent forms, performing tasks based on defined rules or conditions and immediate environmental stimuli. Simple reflex agents depend on predefined or conditional actions, controlling the agent’s response to sensory inputs, navigating to the environment, and executing actions.

These AI agents do not have internal memory; they focus on present sensory input to determine their subsequent actions. Even though these agents are simple, they can perform tasks in various domains, read sensory inputs, and use predefined functions.

Key components of simple reflex agents:

• Sensors – They are input agents for collecting information about the environment. They can be either sensors on robots or complex car cameras.
• Condition – action rules – Simple reflex agents use condition-action rules to perform a specific task based on the environment perception. This determines how an AI agent should act.
• Actuators – They translate the decisions of AI agents into actions, which impact the surroundings.

Applications of simple reflex agents in the real world are:

• Vacuum cleaning robots – They can navigate the simple home environment, sense dirt, and clean it.
• Thermostats – HVAC thermostats can sense temperature and activate heating or cooling based on predefined temperature thresholds.
• Automatic doors – Detect people in front of them and open and remain closed if no one is there.
• Traffic light control – They use time-interval rules or sensor inputs to change traffic lights and control traffic.
• Elevator control – Used in small buildings or low-traffic areas to manage elevator systems and respond to button presses and sensor inputs.

Model-based reflex agents

Model-based reflex agents can track evolving environmental conditions and depend on their model, reflexes, previous precepts, and current states to perform a task. These AI agents maintain an internal representation that allows them to know the consequences of their actions. They are designed to interact with partially observable or dynamic environments. The main difference between a simple reflex agent and a model-based reflex agent is that a model-based reflex agent has memory. The memory stores information on what the agent has perceived and uses it to predict future consequences and make informed decisions.

Key components of model-based reflex agents

• Sensors – They are the interfaces between the agent and the environment that collect information on current surroundings. They can be physical (cameras or temperature sensors) or virtual (database APIs), providing data to make decisions.
• Internal model – Understands the environment and knowledge of dynamics, rules, and potential outcomes. The internal model of a model-based AI agent uses past experiences, sensory inputs, and domain knowledge for reasoning.
• Reasoning component – It uses information collected from the sensors and internal model to make decisions. This component can be rule-based, logical reasoning, or machine learning to evaluate the environment, predict outcomes, and select actions.
• Actuators – Execute actions, whether motors or virtual interfaces, to translate decisions into environment changes and close the perception-action cycle.

Applications of model-based reflex agents in AI

• Robotics – They can navigate dynamic environments avoid obstacles, and achieve specific goals. Robots can predict the outcome of their movement and plan efficient paths.
• Gaming AI – The AI opponents use these AI agent types to anticipate player actions and respond accordingly.
• Autonomous vehicles – Self-driving cars that can interpret sensor data and make steering, accelerating, and braking decisions based on predicted traffic and road conditions.
• Industrial automation – In manufacturing, these agents can predict machine failures or material shortages to optimize production.

Goal-based agents

These AI agents have a more sophisticated approach than simple reflex and model-based agents and are programmed to achieve specific tasks. Goal-based agents can plan, execute, and adjust their actions to achieve the desired goal. They are suitable for complex environments that require flexibility and adaptability.

Components of goal-based agents

• Perception module – Collects data from the environment and processes it to understand the current condition.
• Knowledge base – A world model representing the environment, an AI agent’s understanding, and rules and facts that govern their actions. This is structured knowledge to help agents interpret sensory data and make logical decisions.
• Decision-making module – They define and update goals based on the current state and action selection. They choose actions based on current states, predicted outcomes, and goals, ensuring that the agent’s actions align with its objectives.
• Planning module – They plan and determine an optimal action sequence to achieve goals, and emergency planning, and create backup plans to minimize the risk of failing.
• Execution module – They carry out planned action in the environment and continuously monitor and adjust plans as needed, ensuring that the agent remains responsive to real-time changes.

Applications of goal-based agents in AI:

• Robotics – Navigate environments, perform tasks, and interact with humans and other robots. They work autonomously in dynamic settings, from industrial automation to household chores.
• Game AI – The agent controls non-player characters to exhibit intelligent behavior and strategies.
• Autonomous vehicles – Navigate roads, avoid hurdles, and follow traffic rules, ensuring safe and efficient operations.
• Resource management – Optimize resource usage, like logistics, energy, and manufacturing, make informed decisions, improve efficiency and reduce costs
• Healthcare – Assist in diagnostics, treatment planning, and patient monitoring, supporting medical professionals.

Multi-agent systems (MAS)

It involves several AI agents interacting with each other and their environment. They can be simple software programs or complicated robots with unique skills, knowledge, and objectives. MAS is a computer system where multiple independent agents work together or compete in a shared environment to achieve a common goal.

Components of multi-agent systems:

• Agents – Individual parts of the agentic system, each with its ability, knowledge, and goals, can range from simple bots to advanced robots that can learn and adapt.
• Environment – A space where agents operate which determines how agents will act and interact. It can be a factory or a virtual place (digital platform).
• Interactions – Various agents collaborate, work together, or compete to achieve a common goal.
• Communication – Agents communicate to share information, negotiate, or coordinate, ensuring efficient task completion.

Applications of multi-agent systems:

• Supply chain management – Transform operations, enabling autonomous agents – representing suppliers, manufacturers, distributors, and retailers for effective collaboration. They share real-time inventory data to minimize stockouts and overstocking and use predictive analysis for demand forecasting.
• Healthcare – They monitor patient health, allocate resources such as medical staff and equipment, and develop custom health plans based on patient data.
• Finance – They can detect fraud, assess risks, and monitor financials. Each agent operates with its own rules and objectives, shares data to identify fraudulent activity, and evaluates financial risks.

Learning agents

They are autonomous software that interacts with the environment, gathers knowledge from these interactions, and adapts its behavior to improve performance. They can dynamically change their decision-making processes based on their experience and not just with the predefined rules.

Components of learning agents:

• Sensors – Also known as preceptors – collect information from the environment and send it to the against.
• Critic – It assesses and offers feedback on the agent’s performance based on pre-established goals or a predetermined reward system.
• Learning element – It is the central cognitive hub of the agent for analyzing experience. This element uses machine learning algorithms like reinforcement learning or supervised learning to update internal models’ knowledge bases for improved decision-making.
• Performance element – It requires a learning element and a critic to manage the agent’s activities in an environment.
• Actuators – Also known as effectors, carry out performance element-selected tasks.
• Problem generator – Responsible for creating challenges for agents to compete, and improving ongoing learning and talent development.

Applications of learning agents in the real world

• Autonomous robots – Make agents more skilled in activities like manipulation or human contact to adjust to changing environments and gain experience.
• Personalized recommendation system – Evaluate user behavior and preferences to drive recommendation engines in social networking, streaming devices, and e-commerce platforms.
• Financial trading – Evaluate financial markets, spot trends, and forecast future events.

Utility-based agents

They are the AI agents that make decisions based on utility functions, which measure the degree of satisfaction or utility. They evaluate multiple potential actions and choose one that maximizes their overall utility.

Components of utility-based agents:

• Utility functions – Serve as a mathematical representation of the agent’s preferences, assigning utility or numerical value to each possible outcome. This reflects the satisfaction associated with that outcome.
• State space – Consists of all possible conditions and configurations that agents might encounter or exist in.
• Actions – Set of all operations that utility agents can perform to transit from one state to another.
• Transition model – Describes how an agent moves from one state to another due to its actions.

Applications of utility-based agents

• Robotics – Control various tasks, including investigating, controlling, and communicating between the robot and human, to optimize their performance and achieve their goals.
• Healthcare – Assist in diagnosis, treatment planning, and personalized medicines for better recommendations and patient care.
• Game playing – Make strategic decisions, optimize performance, and achieve victory. It can evaluate different moves and choose the best for the moment.

How does Master Software Solutions help you build your custom AI agents?

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