The AI market has been growing rapidly and the segment of AI agents is trending the most. Impacting various sectors and business operations, the changes are prompted by several key factors
Companies are choosing AI agents that can scale up personalized responses due to the increasing complexities of customer interactions as well as the need for customized experiences
In artificial intelligence, an agent is a computer program or system that is designed to perceive its environment, make decisions and take actions to achieve a specific goal or set of goals. The agent operates autonomously, meaning it is not directly controlled by a human operator.Â
In this blog, we’ll decode different types of AI agents, and how they work. We’ll further discuss their benefits and downsides.
7 Types of AI Agents
01- Simple Reflex Agent
A simple reflex agent is the most basic form of artificial intelligence. It operates purely on condition-action rules, often referred to as ‘if-then rules’. It perceives the current environment through sensors and takes immediate action based on predefined rules without considering the agent’s history or future consequences.
Some examples of simple reflex agents include self-watering plants, email-auto responders, and vacuums.Â
How does it work?
Let’s understand step by step with an example of how a simple reflex agent, like a vacuum, works. The three main role players in simple reflex agents are sensors, condition-action rules, and actuators.
- Perception through environment
The agent uses sensors to gather information about its immediate environment
A vacuum cleaner detects furniture, obstacles, and dirt in the room.
- Condition checking
The perceived data is matched against the condition-action rules to determine what course of action is appropriate.
If dirt detected -> Turn on suction/Vacuum
If the wall is detected -> Turn right
If edge detected -> Reverse direction
If clean -> No operation/ Move to next square
- Action execution
Based on the condition matches, the agent uses its actuators to perform the actions.Â
Move forward, rotate, or stop.
- No learning or memory
The agent does not store past experiences or anticipate future scenarios, unlike today’s powerful AI agents. Simple reflex agents rely entirely on the current state of the environment.Â
Strengths of Simple Reflex Agent
- Simple and fast to implement.
- Works well in predictable and fully observable environments.
Weaknesses of Simple Reflex Agent
- Fails in complex, dynamic, or partially observable environments.
- It cannot adapt or optimize beyond its predefined rules.
02- Model-based reflex agents
Model-based reflex agents use internal models to make decisions. The model helps them keep track of the part of the environment that is not directly observable at any given moment and helps update its state based on current and past perceptions. This enables them to handle more complex situations compared to simple reflex agents.
Some examples are network monitoring tools, autonomous cars, and drones.
How does it work?
A model-based reflex agent uses a state tracker to monitor the environment, a world model to understand changes and predict outcomes, and a reasoning component to decide actions using predefined rules.
Let’s understand this through an example of an autonomous drone.
- State initializationÂ
The agent loads its world model, which includes environmental knowledge.
The drone initializes its map, no fly-zones, and capabilities i.e. battery life.
- Perception
Sensors perceive and collect real-time data about the environment.
The drone uses cameras, GPS, and sensors to detect obstacles and their positions.
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- State Tracker
The agent updates its internal state based on new sensory data and previous interactions with the environment. (sensor history)
It tracks its current location, obstacles, and battery status.
- Reasoning
The agent decides the best action based on its internal state.
The drone decides to avoid an obstacle by adjusting its path.
- Action
Based on the reasoning process, the agent takes action to influence the environment and move toward achieving its goal.
It alters its flight path to avoid an obstacle or continue on course.
- Continuous feedback
The process loops, updating, and adjusting as needed.
The drone constantly monitors and updates its course based on new data.
Strengths of Model-based reflex agents
- Can handle partially observable environments, making them effective even when not all information is available at once.
- Make more informed decisions than simple reflex agents due to access to an internal world model.
- Better adaptability to changing environments by continuously updating their internal models.
Weaknesses of Model-based reflex agents
- More complex to design and implement, requiring additional effort compared to simple reflex agents.
- Demand more computational resources to maintain and process the internal world model.
- Performance depends on the accuracy and reliability of the world model
03- Goal-based agents
Goal-based agents are intelligent systems designed to achieve specific objectives by planning a series of actions based on their understanding of the environment. They prioritize future outcomes and make decisions aimed at reaching a predefined goal, as opposed to relying solely on predefined rules or immediate feedback.
Some examples of goal-based agents include inventory management systems, smart heating systems, and industrial robots.
How does it work:
Goal-based agents use search and planning algorithms to find action sequences that lead to their goals.
- Perception: The agent senses its environment using sensors to gather information.
A warehouse robot scans the shelves to determine the locations of items.
- Define Goal: The agent sets a specific goal to achieve.
Delivering a package to a designated area within the warehouse.
- Model the Environment: The agent builds a world model, understanding how actions will impact its surroundings.Â
Mapping the warehouse layout and identifying potential obstacles.
- Plan Actions: Using algorithms, the agent determines the optimal sequence of actions to achieve its goal.
Calculating the shortest path to the target location while avoiding obstructions.
- Execute Actions: The agent carries out the planned actions and continuously monitors progress.Â
Navigating through the warehouse while adapting to changes like a blocked aisle.
Strengths of Goal-based agents
- Effective in achieving long-term goals in dynamic environments.
- Can adapt plans based on changing conditions.
Weaknesses of Goal-based agents
- Planning can be computationally intensive.
- Requires precise goal definitions and a robust world model
04- Learning based agents
Learning-based agents adapt and improve their behavior by interacting with the environment and learning from feedback. They use past experiences and data to refine their actions and optimize performance over time.
Some examples of learning-based agents are energy management systems, chatbots, and quality control systems.
How does it work:
Unlike simpler agent types, they can discover how to achieve their goals through experience rather than purely relying on pre-programmed knowledge.
- Perception: The agent collects data from the environment.
A chatbot records user queries and feedback during conversations.
- Learning Phase: The agent uses machine learning algorithms to process and analyze data, improving its model of the environment.Â
A chatbot updates responses to improve user satisfaction based on common queries.
- Action Selection: Based on its learned model, the agent selects actions that optimize its performance.
Chatbot suggests the most relevant answer to a user’s query.
- Feedback Integration: The agent evaluates the outcome of its actions and uses feedback to refine future behavior.Â
Chatbot will incorporate user ratings to improve future responses.
Strengths of Learning based agents
- Adapts to changing environments.
- Continuously improves through experience.
Weaknesses of Learning based agents
- Requires large amounts of data for effective learning.
- May overfit specific scenarios, reducing generalizability.
05- Utility-based agents
Utility-based agents prioritize actions based on a utility function, which assigns numerical values to the desirability of different outcomes. They aim to maximize overall utility, enabling them to make optimal decisions in scenarios involving trade-offs.
A few examples of utility-based agents would be scheduling systems, thermostats and used in traditional financial or decentralized finance like crypto trading to assess the desirability of financial outcomes (e.g., profit, risk reduction, portfolio optimization).
How does it work:
- Perception: Sense the environment and gather relevant information.
A smart building management system measures room temperatures and energy consumption.
- Utility Function Evaluation: Calculate the utility of various outcomes based on predefined criteria.
Balancing comfort and energy efficiency by assigning a score to each possible temperature setting.
- Action Selection: Choose the action that maximizes overall utility.
Adjusting the thermostat to achieve an optimal balance between comfort and energy savings.
- Execution: Perform the selected action and reevaluate as conditions change.
Lowering the temperature at night to save energy while maintaining comfort.
Strengths of Utility-based agents
- Handles complex trade-offs effectively.
- Allows for nuanced decision-making based on multiple factors.
Weaknesses of Utility-based agents
- Requires a well-defined utility function.
- Computationally expensive for scenarios with numerous variables.
06- Hierarchical agents
Hierarchical agents use a tiered architecture to break down complex tasks into smaller, manageable subtasks. Each level of the hierarchy focuses on specific tasks, enabling more efficient and organized control.
Some of the few examples of hierarchical agents are building automation, air traffic control systems, and robotic control systems.
How does it work:
- Task Decomposition: Divide a complex task into subtasks.
 A robotic arm splits its job into picking up objects, moving them, and placing them.
- Command Assignment: Higher-level agents delegate subtasks to lower-level agents.
The top-level system directs one subsystem to pick up objects and another to assemble them.
- Execution Coordination: Ensure all subtasks are performed in harmony.
The robotic arm synchronizes its movements to avoid collisions during assembly.
- Feedback and Adjustment: Monitor progress and adjust actions if necessary.
Adjusting the speed of the arm if the task is behind schedule.
Strengths of Hierarchical agents
- Scalable and efficient for handling complex systems.
- Simplifies the management of tasks through modular design.
Weaknesses of Hierarchical agents
- Designing optimal hierarchies can be challenging.
- Failures in lower levels can disrupt higher-level operations.
07- Multi-agent system (MAS)
Multi-agent systems involve multiple autonomous agents working together, either cooperatively or competitively, to achieve individual or shared objectives within a shared environment.
Warehouse management, resource allocation, and transportation are among a few examples of MAS.Â
How does it work:
Multi-agent as the name suggests, involves multiple agents or robots each assigned a different task working together by communicating with each other.
- Agent Identification: Define roles and responsibilities for each agent.
In a warehouse, one robot focuses on picking items, while another handles transportation.
- Communication: Establish protocols for agents to share information.
Robots exchange data about item locations and pathways to avoid collisions.
- Coordination: Ensure agents work collaboratively or manage conflicts.
Assigning robots to complementary tasks, like sorting and stacking items.
- Execution: Agents perform their tasks while adapting to changes.
Adjusting routes dynamically if a path becomes blocked.
Strengths of Multi-agent system
- Scalable for large, distributed problems.
- Promotes collaboration and efficiency.
Weaknesses of Multi-agent system
- Requires robust communication protocols to prevent errors.
- Potential for conflicts between agents if not properly managed
Summary of Type of AI Agents
| Agent Type | Best for | Examples |
|---|---|---|
| Simple Reflex Agents | Repetitive, rule-based tasks | Automatic lights or security alarms in smart homes |
| Goal-Based Agents | Long-term objectives | Task scheduling systems in logistics |
| Learning Agents | Dynamic environments | Customer chatbots learning from user feedback |
| Utility-Based Agents | Balancing competing priorities | Financial portfolio management, predictive pricing in retail |
| Hierarchical Agents | Complex systems | Autonomous vehicles like Waymo (route planning + obstacle avoidance) |
| Multi-Agent Systems | Collaboration or competition | Supply chain logistics, multi-robot coordination in warehouses |
FAQs
- What is the best type of AI agent?
There’s no “one-size-fits-all.” Choose based on your task:
- Reflex agents for simple, repetitive jobs.
- Goal-based or utility-based agents for decision-making scenarios.
- Learning agents for adaptive systems like fraud detection.
- Is ChatGPT an AI agent?
Not exactly. ChatGPT is a language model, not an agent. It generates human-like text responses but doesn’t interact with an environment or execute actions autonomously like AI agents do.
- What is the most popular type of AI agent?
This depends on industry needs:
- In autonomous driving, hierarchical agents are common.
- In customer service, learning agents are widely used to personalize interactions.
-
How is AI used in financial trading?
Utility-based AI agents are employed in:
- Algorithmic Trading: Balancing risk and reward in market predictions.
- DeFi Platforms: Automating market-making processes in platforms like Uniswap.
- Portfolio Optimization: Maximizing returns by evaluating the utility of different asset allocations.
Conclusion
AI agents are revolutionizing industries, automating tasks, and driving smarter decisions. From routine operations to complex innovations, they’re unlocking new possibilities.
At BlockApex, we combine AI with blockchain to deliver transformative solutions for your business. Contact us today to explore how we can help you innovate and scale!
