7 Types of AI Agents with Features and Examples

Conversational AI applications have been developed from simple chatbots to more complex AI agents. By taking the power of generative AI, these multiple agents are businesses’ strategic partners in a particular expertise. They support companies making decisions and taking actions autonomously.

AI agents set specific objectives and take action to achieve them, operating independently without constant human control. Once deploying AI agents, businesses can optimize operations and improve customer experiences, responding to market changes effectively. Consequently, it can lower operational costs, provide 24/7 availability, and improve the accuracy of data-driven decisions.

Types of AI agents may vary significantly in complexity, possessing different characteristics and applications. Based on the models’ architecture and approaches to interact with the environment, AI agents include seven types:

• Simple reflex agents
• Model-based reflex agents
• Goal-based agents
• Utility-based agents
• Learning agents
• Hierarchical agents
• Multi-agent systems (MAS)

This article will outline the advantages, disadvantages, and applicable use cases of various AI agent types. A practical framework for selection will wrap up this post. Let’s get started now.

1. Simple Reflex Agents

Simple reflex agents represent a fundamental type of AI agent, characterized by their immediate, rule-based responses to environmental stimuli. Unlike other agents, these agents are unable to learn from past interactions and maintain memory due to operating on predefined condition-action rules. Consequently, their environment’s current perception exclusively determines their actions. This design framework prioritizes reactivity over adaptability, limiting its effectiveness in a complex or changing environment.

Businesses across various domains make use of simple reflex agents for predictable tasks, straightforward rules, and required immediate responses. Automatically turning heating or cooling systems, controlling non-player characters (NPCs) to follow predefined paths, or forcing the assembly lines to stop when detecting misalignment are some of the most common applications of these agents.

Advantages

This AI agent type offers several advantages for specific automation and AI-driven tasks, including:

• Instant response time: Basic customer service AI agents provide immediate responses for time-sensitive applications. They include motion-activated security, industrial safety, and real-time automation, ensuring minimal decision delays.
• Low computational cost: Requiring minimal processing power by operating on straightforward, predefined rules without memory or complex algorithms. These AI agents offer high efficiency and cost-effective implementation in embedded systems. Vending machines, barcode scanners, and basic robotics, to name a few.
• Reliability in fully observable environments: The agent offers consistent and predictable performance through its strict compliance with if-then logic. This advantage makes the agent highly reliable for structured automation applications such as thermostats, traffic signals, and assembly lines.
• Error reduction: Minimizing human error in repetitive tasks, enhancing accuracy and consistency, particularly in precision-critical industries such as manufacturing and finance.
• Automation of repetitive tasks: Automating routine operations without human intervention. These agents can enhance productivity and reduce labor costs by freeing human workers for strategic tasks.

Limitations

However, these intelligent agents encounter limitations in adapting to complex or dynamic environments. These limitations originate from their reliance on predefined condition-action rules and inability to use memory or adapt to new situations.

• Lack of memory and learning: Being ineffective in environments where context or history.
• Rigid, rule-based behavior: These agents cannot respond appropriately to unforeseen situations, requiring manual rule updates and limiting their scalability and flexibility.
• Dependency on complete and accurate sensory input: Due to limited ability to identify missing details or compensate for faulty inputs, these agents may fail to act if sensors malfunction or provide incomplete information.

With the above competitive advantages, simple reflex agents perform well for tasks that require immediate, rule-based responses to specific inputs. Amazon used this AI agent in warehouse robots to sort and move packages based on barcode scans or sensor input. Furthermore, platforms like Zowie and Ada are powered by simple reflex agents and natural language processing to provide instant answers to repetitive questions and reduce cart abandonment for e-commerce brands by providing rule-based customer service chatbots.

2. Model-Based Reflex Agents

Model-based reflex agents are sophisticated AI systems designed for complex and changing environments that don’t allow for complete direct observation. By building and updating an internal understanding of their surroundings, they can generate intelligent decisions even with incomplete information.

Functioning through a continuous cycle, these AI agents work by acquiring sensory input to inform and refining their operational environment’s internal model. Subsequently, they leverage this comprehensive model and established protocols to determine and execute appropriate actions, updating through new sensory input and the observed consequences.

Advantages

This agent type supports addressing intricate and unpredictable real-world scenarios with the following advantages:

• Improved decision-making: Enhancing decision-making and improving performance in complex environments by leveraging real-time sensory data and accumulated past experiences.
• Context awareness and adaptability: Continuously updating their internal understanding of the environment enables these agents to adapt and respond effectively to unfamiliar situations.
• Avoiding repetitive mistakes: Mitigating the risks of repeating errors by retaining a memory of past states and actions.
• Complex real-world task applicability: Their capacity for outcome prediction and environmental reasoning makes them well-suited for intricate real-world applications such as robotic navigation, autonomous driving, industrial automation, and gaming AI.
• Flexibility for improvement: Refining and expanding the internal model over time allows this agent to be enhanced and tailored for increasingly complex tasks.

Limitations

However, these agents encounter challenges regarding computational demands, model accuracy, and limited learning.

• Computational expense: Maintaining and updating an internal environmental model requires significant computational resources, potentially increasing the cost and reducing the processing speed.
• Model accuracy and timeliness: Outdated or inaccurate internal models, particularly in rapidly changing environments, can cause the AI agent to make poor decisions based on imperfect assumptions.
• Limited learning capability: Despite storing past information, these agents don’t perform effectively in learning and refining their decision-making rules over time. Instead, they rely on predefined condition-action rules, restricting their adaptability in new or intricate scenarios.

3. Goal-Based Agents

Goal-based agents are intelligent systems designed to achieve specific objectives. This agent type strategically plans actions by evaluating potential outcomes and selecting choices that align with its predefined goals, allowing it to adapt to complex environments beyond immediate reactions.

Goal-based agents formulate a specific objective and subsequently select and execute actions strategically anticipated to achieve that objective. Throughout this process, they continuously monitor their progress and adjust their strategies dynamically to ensure alignment with the goals.

Advantages

This AI agent offers several advantages across different AI applications:

• Focused on specific outcomes: Strategically prioritizing actions that directly contribute to clearly defined objectives. This targeted approach optimizes their performance and enhances efficiency in task completion.
• Easy to design and understand: Facilitating straightforward implementation and evaluation due to its rule-based structure and goal-driven nature, particularly within well-defined operational environments.
• Efficient planning and decision-making: By leveraging search algorithms and heuristics, goal-based agents can efficiently determine optimal strategies and plan effective sequences of actions to achieve their objectives.
• Performance evaluation: The direct correlation between goal completion and success in these agents allows for straightforward and quantifiable performance evaluation.

Limitations

In contrast, goal-based agents also demonstrate their limitations in the following aspects:

• Limited to specific goals: Encountering limitations when meeting with unclear, ambiguous, or frequently changing objectives, reducing their effectiveness in dynamic or poorly defined environments.
• Complex implementation: Requiring significant domain expertise and meticulous programming to formulate clear, complete, and appropriate goals, resulting in complicated implementation processes and extended development timelines.

Examples of goal-based agents across various industries include robotic vacuum cleaners, smart home devices, financial trading bots, and healthcare AI. Regarding real-world applications, IBM Watson Health uses goal-based agents to analyze medical data, assist in diagnostics, and recommend personalized treatment plans. Alibaba also employs goal-based agents in its logistics division to automate package delivery, considering goals such as route optimization, package handling, and timely delivery.

4. Utility-Based Agents

Utility-based agents make decisions by maximizing a utility function, which measures the desirability of outcomes. In complex environments, these agents achieve optimal decision-making by evaluating potential actions and selecting the one that provides the highest expected utility.

These intelligent agents demonstrate their versatility across diverse applications, including optimizing energy consumption, allocating resources in logistics, and maximizing profits in financial trading. In essence, utility-based agents focus on maximizing overall satisfaction or utility, moving beyond simple goal achievement.

Advantages

Utility-based agents support businesses effectively in complex and dynamic environments with the following significant aids:

• Handling uncertainty: Utility-based agents can navigate observable or uncertain environments by weighing the expected utility of various actions.
• Complex problem solving: Utility-based agents excel at addressing complex problem-solving scenarios that demand a balance of competing factors. This capability enables applications such as resource allocation, pricing strategies, and personalized recommendations.
• Optimization of outcomes: These agents optimize outcomes in multi-criteria decision problems by balancing competing objectives like cost, safety, efficiency, and user preferences to identify top-performing solutions.
• Learning and improvement: Incorporating learning mechanisms in various utility-based agents enables the refinement of their utility functions and decision strategies through experience, embracing performance over time.

Various companies have leveraged utility-based agents to streamline business processes and customer experience. Streaming services like Netflix apply these agents to suggest movies, products, or services.

5. Learning Agents

A learning agent in AI autonomously interacts with its environment to acquire knowledge and adapts its behavior for improved performance over time. By employing algorithms to analyze data and identify patterns, these agents can enhance their performance based on experience and feedback.

A leaning agent’s architecture includes a learning component that uses data to improve, a performance component for actions based on learned information, a critic for outcome evaluation, and a problem generator to introduce new learning situations. These components collaboratively enable the agent’s adaptive capabilities.

Advantages

From adaptability and continuous improvement to efficiency and autonomy, learning agents provide a range of benefits for AI and real-world applications:

• Scalability: Learning agents are suitable for large-scale deployments in education, business, and industry by simultaneously handling large volumes of data and users.
• Continuous improvement: These agents ensure continuous improvement when learning from every interaction to refine approaches and enhance performance over time, remaining relevant and effective as conditions change.
• Personalization: By analyzing data and adapting services, learning agents personalize user experiences and improve engagement, especially in education and customer service.
• Data-driven insights: Learning agents can provide valuable analytics and insights that inform better decision-making and strategy development for organizations and educators.
• Support for human experts: These agents help automate routine tasks, provide performance feedback, and offer personalized recommendations, enabling teachers and business analysts to focus on higher-value activities.

Limitations

However, some disadvantages prevent learning agents from performing optimally:

• Lack of human interaction and personalization. Learning agents often struggle to provide the empathy, emotional intelligence, and nuanced understanding inherent in human interactions. It results in diminished user satisfaction in scenarios that prioritize human connection.
• Ethical concerns and bias. Learning agents can inherit and amplify biases from their training on sensitive data or algorithms, raising concerns about fairness, accountability, and transparency, especially in sensitive applications like healthcare or finance.
• Technical limitations and errors. Algorithmic errors in learning agents cause significant negative consequences, particularly in ambiguous, novel, or highly complex situations demanding human intuition.

Outstanding examples of businesses using learning agents include Netflix, Spotify, Nike, and Starbucks.

Practically, Netflix incorporates learning agents into its recommender system to analyze user viewing habits and suggest personalized content. Meanwhile, Spotify also leverages this AI agent to personalize playlists and year-end summaries.

6. Hierarchical Agents

Hierarchical agent systems represent a layered approach to artificial intelligence architecture. In this framework, higher-level agents function as supervisors, directing and synchronizing the activities of subordinate agents.

This layered AI system operates similarly to a team with different roles. Specifically, high-level agents set the strategic goals, and mid-level agents oversee the specific operations and teams to achieve those goals. Finally, individual contributors execute the detailed tasks as directed by higher-level agents.

Advantages

Hierarchical agent systems provide several notable benefits for developing sophisticated AI solutions.

• Enhanced Efficiency: These systems assign tasks based on priority and expertise across different levels to optimize resource use and accelerate the delivery of more precise results.
• Scalability: Their tiered design manages large and intricate problems effectively by dividing them into smaller, manageable parts, facilitating easier expansion without overburdening individual agents.
• Simplified Decision-Making: Separating strategic planning at higher levels from task execution at lower levels lightens the cognitive load and promotes quicker, more effective decisions at each level.
• Improved Specialization and Adaptability: Each level can specialize in specific functions, enhancing the system’s ability to adapt to new tasks or environmental changes.
• Better Communication and Coordination: The structured organization minimizes unnecessary communication and confusion by ensuring information flows efficiently and is relevant to each agent’s role.

Limitations

Despite their advantages, hierarchical agent systems also present certain drawbacks.

• Rigidity and Lack of Flexibility: These systems can be overly rigid, hindering agents’ ability to rapidly adjust to novel or unexpected circumstances, which can impede adaptability in dynamic settings.
• Slower Communication and Decision-Making: The necessity for information and approvals to pass through multiple levels can lead to slow and cumbersome decision-making processes, potentially delaying responses to critical issues.
• Stifled Collaboration and Innovation: Their structure can discourage collaboration across different levels, potentially limiting information sharing and external input, which may hinder innovation and problem-solving.

Hierarchical agent systems are employed across various industries to manage complex operations. As they’re seen in AI travel agents planning trips and robotics in manufacturing, overseeing production lines. Business automation platforms and contact center solutions utilize these layered structures for workflow management and customer service. Furthermore, specialized AI providers and the autonomous transportation sector leverage hierarchical agents for tailored automation and coordinated logistics.

7. Multi-Agent Systems (MAS)

A multi-agent system (MAS) is a computerized system comprising multiple autonomous agents that interact and collaborate to address complex problems or perform tasks for a single agent or a centralized system.

Multi-agent systems decompose complex tasks into smaller, manageable subtasks, assigning each to the most capable agent. Through collaboration, information sharing, and coordination mechanisms, these autonomous agents collectively solve problems and make decisions.

Advantages

Multi-agent systems offer several key advantages by distributing tasks and enabling collaborative problem-solving:

• Adaptability and Scalability: MAS can dynamically adapt to changing environments and handle increased workloads by easily adding, removing, or modifying agents.
• Enhanced Problem-Solving and Efficiency: Agents enhance problem-solving by sharing knowledge and resources, improving efficiency through parallel processing.
• Specialization and Modularity: Agents’ specialization in specific domains improves accuracy, while modular design simplifies system maintenance through independent updates.
• Resilience and Fault Tolerance: The system maintains operation despite individual agent failures, ensuring robustness in critical applications through redundancy.

Limitations

Multi-agent systems, while bringing numerous benefits, also present several notable weaknesses:

• Security Vulnerabilities: The interconnected nature of multi-agent systems makes them susceptible to various cyber threats and malicious actors, potentially compromising the entire system.
• Performance Variability: Due to factors like environmental changes and communication delays, the distributed operation of multi-agent systems can result in unpredictable and inconsistent performance.

Airlines and ReviveTech are two brands leveraging multi-agent systems to enhance their operations. Airlines use MAS to automate and personalize customer communications by coordinating specialized agents to create tailored follow-up emails after bookings. ReviveTech employs a MAS in its marketing department, where AIProdPad, AIBrandPad, and AIContentPad agents collaborate to analyze trends, manage brand perception, and generate personalized content.

How to Use Different Types of AI Agents in Business

Businesses can strategically develop and implement various types of AI agents to optimize diverse operations and achieve specific objectives.

• Simple Reflex Agents: Organizations find these agents beneficial for automating repetitive, rule-governed tasks, such as chatbot responses to common customer queries or automated email replies for general inquiries.
• Model-Based Reflex Agents: Leveraging these within CRM systems allows for the recall of customer preferences during interactions, or support bots can modify their answers based on a user’s recent engagement, thus enhancing personalized assistance.
• Goal-Based Agents: Enterprises find these useful for process optimization, whether adjusting marketing campaigns to hit specific sales figures or empowering logistics agents to pinpoint the most cost-effective delivery paths.
• Utility-Based Agents: Implementing AI-powered pricing mechanisms enables the adjustment of product costs by considering demand, competition, and profit margins; similarly, investment portfolio managers can employ them to balance risk and potential yields.
• Learning Agents: Their application in fraud detection systems allows for adaptation and identification of novel scam methods, or recommendation engines can refine their suggestions based on changing user habits and choices.
• Hierarchical Agents: Within supply chain management, their use involves a high-level agent supervising overall logistics, while lower-level agents handle tasks like inventory management or transport route optimization for better efficiency.
• Multi-Agent Systems (MAS): E-commerce platforms can utilize these to manage inventory, pricing tactics, and customer service inquiries; likewise, utility providers can employ them in smart grid management to coordinate energy distribution and optimize resource allocation.

Which Types of AI Agents Are for Your Business?

This article details seven types of AI agents, from simple reflex agents reacting to immediate inputs to complex multi-agent systems. Businesses can leverage these agents to automate routine processes, personalize customer interactions, optimize operations, and improve decision-making through data analysis.

Selecting the optimal AI agents gains you significant advantages, including streamlined operational efficiency and enhanced customer engagement. However, addressing the complexities inherent in this crucial decision requires expert guidance. If you seek assistance in navigating the complexities of AI agent selection, contact Neurond for a professional consultation.