What Are AI Agents?

An AI agent is an intelligent system or program designed to autonomously perform tasks on behalf of a user or another system. By designing its own workflow and leveraging available tools, AI agents can execute a variety of complex tasks that extend beyond simple interaction, such as decision-making, problem-solving, and even engaging with external environments.

These agents are becoming an integral part of various industries, from software development and IT automation to code generation and conversational assistants. At their core, AI agents use advanced natural language processing (NLP) techniques powered by large language models (LLMs). This enables them to understand user input step-by-step and determine when to leverage external tools for enhanced functionality.

How Do AI Agents Work?

The functionality of AI agents relies heavily on large language models (LLMs). Because of this, AI agents are often referred to as LLM agents. Traditional LLMs, such as IBM® Granite™ models, generate responses based on pre-existing data and are limited by their knowledge base. On the other hand, AI agents are more advanced, using backend tool integration to retrieve up-to-date information, optimize workflows, and autonomously break down complex tasks into manageable subtasks.

This ability allows AI agents to continually adapt to user expectations over time, providing a more personalized experience and more comprehensive responses. By storing past interactions in memory and planning future actions, AI agents offer a unique, evolving interaction model.

The Three Key Stages of AI Agent Functionality

1. Goal Initialization and Planning

While AI agents are autonomous, they still require human-defined goals and environments. Three primary forces shape the behavior of AI agents:

The development team designing and training the agent.
The deployment team that enables users to interact with the agent.
The end user who sets specific goals and defines the tools the agent can use.

Once the agent has access to the goal and the available tools, it decomposes the goal into smaller tasks to improve efficiency. While complex tasks may require extensive planning, simpler tasks may not need a formal plan, and the agent can improve iteratively as it interacts with the user.

2. Reasoning with Available Tools

AI agents rely on available tools to supplement their knowledge. Since AI agents might not have complete information to accomplish every subtask, they access external databases, web searches, APIs, or even other agents. This dynamic tool usage allows them to continually update their knowledge base and self-correct as needed.

For example, if a user tasks an AI agent with planning a vacation and requests predictions about the best time for surfing in Greece next year, the agent may not have specialized weather data. It can call on external tools like weather databases and even interact with an agent focused on surfing conditions. This collaboration helps the AI agent make predictions based on both weather patterns and surfing conditions, providing a more informed and accurate response to the user.

3. Learning and Reflection

AI agents also improve through feedback mechanisms, which may involve direct user feedback or interaction with other agents. This process, called iterative refinement, enables AI agents to adapt over time and align their responses more closely with user preferences. Feedback collected from both the agent’s actions and from human-in-the-loop (HITL) systems helps improve the accuracy and effectiveness of responses.

Returning to the vacation example, once the AI agent has provided its response about the best time for surfing, it will store the learned information and user feedback for future use, refining its reasoning capabilities over time. This continuous learning process helps the agent avoid previous mistakes and better meet user needs.

AI Agents vs. Non-Agentic Chatbots

While AI agents and traditional AI chatbots both rely on natural language processing, there are distinct differences between them. AI chatbots typically respond to specific questions by automating pre-defined answers. These bots don’t have tools, memory, or reasoning capabilities, and they can only address short-term goals without planning for the future. They require ongoing user input to function and can’t learn from their mistakes.

In contrast, AI agents are far more dynamic and capable. They can adapt to users over time, store memory, and plan for long-term goals. Through iterative reasoning, AI agents can complete multi-step tasks, consider multiple pathways, and use their available tools to fill knowledge gaps—something non-agentic chatbots cannot do.

Reasoning Paradigms for AI Agents

There is no one-size-fits-all approach to building AI agents. Different reasoning paradigms are used to tackle multi-step problems, such as:

ReAct (Reasoning and Action)

The ReAct paradigm emphasizes “thinking” and planning after each action is taken, allowing the agent to adjust its next steps based on the most recent information. In this model, agents use Think-Act-Observe loops to address problems step by step and continuously improve their responses. This method allows agents to refine their reasoning through Chain-of-Thought prompting, where each decision-making step is explicitly considered and re-evaluated.

ReWOO (Reasoning Without Observation)

Unlike ReAct, the ReWOO paradigm encourages agents to plan ahead before interacting with tools. This pre-planning approach minimizes redundant tool calls and optimizes the workflow. The agent first plans its actions, then collects the necessary data using tools, and finally formulates a response. This approach is particularly useful from a human-centered perspective, as it allows users to review and approve the plan before execution, reducing computational complexity and preventing tool failures.

The Future of AI Agents

As the capabilities of AI agents continue to evolve, they will become increasingly proficient at solving complex, multi-step tasks autonomously. From enhancing business operations to providing personalized user experiences, the potential applications of AI agents are vast. By integrating reasoning with tool usage and continuous learning, AI agents represent a major step forward in AI development, offering more sophisticated and dynamic interactions compared to traditional AI models.

In conclusion, AI agents are poised to revolutionize industries by offering smarter, more efficient solutions. With their ability to plan, reason, and adapt, they are transforming how businesses and users approach problem-solving, decision-making, and task automation.

 

Types of AI Agents: From Simple Reflex to Learning Agents

AI agents come in various forms, each designed with different levels of capabilities depending on the complexity of the task at hand. The simplest AI agents are used for straightforward, well-defined tasks, while the most advanced can adapt, learn, and optimize their performance over time. Here are the five main types of AI agents, listed from the most basic to the most advanced:

1. Simple Reflex Agents

Simple reflex agents are the most basic form of AI agents, responding directly to the current state of the environment without any memory of past interactions. These agents rely entirely on predefined rules or “reflexes” that dictate specific actions based on the present condition.

These agents are highly efficient in fully observable environments where all the necessary information is available at once. However, they cannot handle situations they haven’t been explicitly programmed for and cannot make decisions beyond their rule set.

Example: A simple thermostat is a classic example of a reflex agent. It turns the heating system on or off based on a pre-set condition, such as activating the heating at 8 PM every night.

2. Model-Based Reflex Agents

Model-based reflex agents are more advanced than simple reflex agents because they have the ability to store and update internal models of the world. These agents can use both their current perception and memory to act more effectively in partially observable environments.

Unlike simple reflex agents, model-based reflex agents are able to handle changing environments, as they update their internal model with new information. However, they still rely on predefined rules to make decisions, limiting their ability to adapt to completely new situations.

Example: A robot vacuum cleaner is a great example of a model-based reflex agent. As it cleans a room, it maps out obstacles like furniture and updates its model of the area. It remembers which areas have been cleaned and avoids unnecessary repetition.

3. Goal-Based Agents

Goal-based agents are more sophisticated in that they not only maintain an internal model of the world but also have specific goals they aim to achieve. These agents search for a sequence of actions that will lead to their desired goal, allowing them to plan ahead and make better decisions compared to reflex-based agents.

By using search algorithms and planning, goal-based agents are able to break down complex tasks into a series of actions that will ultimately help them accomplish their goal.

Example: A navigation system that helps you reach your destination by calculating the best route. The system evaluates multiple possible routes and chooses the one that best aligns with your goal — arriving at the destination as quickly as possible.

4. Utility-Based Agents

Utility-based agents go a step further than goal-based agents by not only striving to reach a goal but also selecting the actions that maximize the “utility” or overall satisfaction from achieving that goal. They use a utility function, a mathematical model that assigns a value to different outcomes, to help them evaluate various possible actions.

These agents are particularly useful in situations where there are multiple ways to achieve a goal, but some methods are more desirable than others due to factors like efficiency, cost, or time.

Example: A more advanced navigation system that considers multiple factors, such as fuel efficiency, traffic conditions, and toll costs, in addition to just the fastest route. The system selects the route that offers the highest utility based on the user’s preferences.

5. Learning Agents

Learning agents represent the most advanced type of AI agents. Unlike the previous agent types, learning agents are capable of improving their performance autonomously by learning from their interactions with the environment. Over time, these agents refine their decision-making processes based on new experiences, allowing them to adapt to unfamiliar or changing circumstances.

Learning agents typically consist of four main components:

Learning: This component allows the agent to acquire new knowledge from its environment.
Critic: The critic evaluates the performance of the agent’s actions and provides feedback.
Performance: This element is responsible for selecting the best action based on what the agent has learned.
Problem Generator: The problem generator proposes potential actions that could be taken based on new learning.
Because they learn and adapt continuously, learning agents are ideal for applications that require ongoing refinement and personalization.

Example: E-commerce recommendation systems are a prime example of learning agents. They track user behavior, preferences, and past interactions to suggest products that are likely to interest the user. With each interaction, the agent learns more about the user’s preferences and becomes more accurate over time. 

Which Type of AI Agent is Right for Your Needs?

Choosing the right type of AI agent depends on the complexity of the task and the environment in which the agent will operate. For simple, rule-based tasks, simple reflex agents may be sufficient. However, if the environment is dynamic or requires more advanced decision-making, a model-based or goal-based agent might be a better fit. For situations where multiple factors must be considered or continuous learning is needed, utility-based or learning agents would be the ideal choice.

As AI agents continue to evolve, their ability to learn, adapt, and make complex decisions will only improve, enabling them to tackle increasingly sophisticated tasks across various industries.

Use Cases of AI Agents

AI agents are transforming industries by automating tasks, enhancing decision-making, and improving efficiency across various sectors. Here are some of the most prominent use cases of AI agents in different fields:

1. Customer Experience

AI agents are increasingly being used to enhance the customer experience across websites and apps. As virtual assistants, AI agents can engage users, answer queries, provide mental health support, simulate interviews, and assist with other customer-centric tasks. With no-code templates available, businesses can easily deploy AI agents to improve customer service without needing specialized coding skills.

For example, in the retail sector, AI agents can be integrated into e-commerce platforms to offer personalized recommendations based on user preferences, boosting engagement and sales.

2. Healthcare

The healthcare industry stands to benefit significantly from AI agents, especially in real-time decision-making and administrative tasks. Multi-agent systems, in particular, are ideal for solving complex problems in healthcare settings. From planning treatments in emergency departments to managing drug distribution, AI agents can reduce the workload of medical professionals, allowing them to focus on more critical aspects of patient care.

AI agents can also assist in diagnostic tools, helping healthcare providers analyze patient data faster and more accurately, thus improving patient outcomes.

3. Emergency Response

In the event of natural disasters, AI agents can play a pivotal role in saving lives. By using deep learning algorithms, AI agents can analyze social media platforms to identify individuals in need of rescue. By locating users based on their social media posts, these agents can help emergency services quickly identify high-priority locations, reducing response times and saving more lives.

This application of AI agents is especially useful in time-sensitive situations, where swift action can make a difference between life and death.

3. Emergency Response

In the event of natural disasters, AI agents can play a pivotal role in saving lives. By using deep learning algorithms, AI agents can analyze social media platforms to identify individuals in need of rescue. By locating users based on their social media posts, these agents can help emergency services quickly identify high-priority locations, reducing response times and saving more lives.

This application of AI agents is especially useful in time-sensitive situations, where swift action can make a difference between life and death.

Benefits of AI Agents

As AI agents continue to evolve, they offer several key advantages across various industries. Below are some of the main benefits:

1. Task Automation

One of the most significant advantages of AI agents is their ability to automate complex tasks. With advancements in generative AI, businesses are increasingly using AI agents to streamline workflows and reduce the need for human involvement in routine tasks. This automation leads to faster goal completion at scale, with fewer human resources required.

For instance, in IT operations, AI agents can handle routine maintenance tasks, such as software updates and troubleshooting, without human intervention, allowing employees to focus on higher-value work.

2. Greater Performance

When multiple AI agents work together, they can outperform singular agents. This is due to the enhanced learning and feedback mechanisms that occur in multi-agent frameworks. By collaborating and sharing knowledge, AI agents can achieve more comprehensive solutions, filling knowledge gaps and improving overall performance.

In industries like finance or supply chain management, AI agents working together can provide more accurate predictions and insights, making them invaluable tools for decision-makers.

3. Quality of Responses

AI agents offer responses that are more personalized, accurate, and comprehensive than traditional AI models. This is particularly important for improving user experiences. The ability of AI agents to exchange information with other agents, use external tools, and continuously update their memory allows them to adapt and provide higher-quality responses over time.

For example, customer support AI agents can refine their responses based on previous interactions, improving the quality of service each time a customer reaches out.

Risks and Limitations of AI Agents

While AI agents offer substantial benefits, there are also risks and limitations that must be carefully considered.

1. Multi-Agent Dependencies

Some tasks require the cooperation of multiple AI agents. However, when building multi-agent systems, there is a risk of system-wide failures if one or more agents malfunction. Since these agents often operate on shared foundational models, weaknesses in one agent can lead to failures in others. This highlights the importance of rigorous training, testing, and data governance when developing AI agents.

For instance, in healthcare, if a multi-agent system used for treatment planning experiences a bug or malfunction, it could lead to errors in patient care. Thorough validation and robust error-handling processes are critical.

2. Infinite Feedback Loops

One risk associated with AI agents is the possibility of infinite feedback loops. These occur when an agent continually calls the same tools or re-evaluates the same data without reaching a resolution. This can lead to inefficiency and computational waste. In some cases, it might require human intervention to break the loop and allow the agent to re-evaluate its approach.

To avoid such loops, it’s crucial to implement monitoring systems and establish conditions for interruptibility, allowing humans to intervene when necessary.

3. Computational Complexity

Building AI agents from scratch can be resource-intensive. High-performance agents require significant computational power and time to train, especially when working with large datasets or performing complex tasks. Depending on the complexity of the task, agents may take hours, days, or even weeks to complete certain actions, which can increase operational costs.

Businesses must weigh the potential benefits against the computational expense and ensure they have the necessary infrastructure to support AI agents effectively.

Best Practices for AI Agents

To mitigate risks and maximize the effectiveness of AI agents, it’s important to follow best practices in their design, implementation, and monitoring.

1. Activity Logs

To ensure transparency and accountability, AI agents should maintain activity logs that track their actions, including the use of external tools and the involvement of other agents. This transparency allows users to review the agent’s decision-making process, identify potential errors, and build trust in the system.

For example, in financial applications, where decisions could have significant consequences, maintaining detailed logs helps ensure that actions are traceable and that mistakes can be corrected.

2. Interruptibility

For high-stakes tasks, it’s crucial to allow human users to interrupt AI agents if necessary. This feature helps prevent unwanted consequences, such as infinite loops or malfunctions. However, interruption should be carefully considered, as prematurely shutting down an agent could cause more harm than good.

For example, in emergency response situations, it might be safer to allow an AI agent to continue assisting with data analysis, even if it is encountering errors, rather than interrupting it and risking a delay in response.

3. Unique Agent Identifiers

To prevent malicious use or unintended harm caused by AI agents, it is essential to implement unique identifiers for each agent. This makes it easier to trace the origin of the agent’s creators, deployers, and users, ensuring accountability. If an AI agent is misused, these identifiers can help identify the responsible parties and mitigate the risks.

4. Human Supervision

Human supervision is particularly important during the early stages of an AI agent’s learning process, especially when it is operating in a new environment. Providing occasional feedback ensures the agent’s actions align with expected standards. Moreover, for critical tasks like financial trading or healthcare decisions, human approval should be required before the agent takes action.

Conclusion

AI agents are poised to revolutionize industries by enhancing automation, decision-making, and customer interactions. By leveraging their unique capabilities, businesses can achieve greater efficiency, improved performance, and higher-quality responses. However, careful attention to risks such as multi-agent dependencies, feedback loops, and computational complexity is necessary for successful implementation. Adopting best practices like activity logs, interruptibility, and human supervision will ensure that AI agents continue to provide value while mitigating potential risks.

References

1. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang, “Expel: Llm agents are experiential learners,” Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, No. 17, pp. 19632-19642, 2024, https://ojs.aaai.org/index.php/AAAI/article/view/29936.

2. Yonadov Shavit, Sandhini Agarwal, Miles Brundage, Steven Adler, Cullen O’Keefe, Rosie Campbell, Teddy Lee, Pamela Mishkin, Tyna Eloundou, Alan Hickey, Katarina Slama, Lama Ahmad, Paul McMillan, Alex Beutel, Alexandre Passos and David G. Robinson, “Practices for Governing Agentic AI Systems,” OpenAI, 2023, https://arxiv.org/pdf/2401.13138v3.

3. Tula Masterman, Sandi Besen, Mason Sawtell, Alex Chao, “The Landscape of Emerging AI AgentArchitectures for Reasoning, Planning, and Tool Calling: A Survey,” arXiv preprint, 2024, https://arxiv.org/abs/2404.11584.

4. Gautier Dagan, Frank Keller, and Alex Lascarides, “Dynamic Planning with a LLM,” arXiv preprint, 2023, https://arxiv.org/abs/2308.06391.

5. Binfeng Xu, Zhiyuan Peng, Bowen Lei, Subhabrata Mukherjee, Yuchen Liu, and Dongkuan Xu, “ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models,” arXiv preprint, 2023, https://arxiv.org/abs/2305.18323.

6. Sebastian Schmid, Daniel Schraudner, and Andreas Harth, “Performance comparison of simple reflex agents using stigmergy with model-based agents in self-organizing transportation.” IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, pp. 93-98, 2021, https://ieeexplore.ieee.org/document/9599196.

7. Veselka Sasheva Petrova-Dimitrova, “Classifications of intelligence agents and their applications,” Fundamental Sciences and Applications, Vol. 28, No. 1, 2022.

8. Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, and Jirong Wen, “A survey on large language model based autonomous agents,” Frontiers of Computer Science, Vol. 18, No. 6, 2024, https://link.springer.com/article/10.1007/s11704-024-40231-1.

9. Jaya R. Haleema, Haleema, N. C. S. N. Narayana, “Enhancing a Traditional Health Care System of an Organization for Better Service with Agent Technology by Ensuring Confidentiality of Patients’ Medical Information,” Cybernetics and Information Technologies, Vol. 12, No. 3, pp.140-156, 2013, https://sciendo.com/article/10.2478/cait-2013-0031.

10. Jingwei Huang, Wael Khallouli, Ghaith Rabadi, Mamadou Seck, “Intelligent Agent for Hurricane Emergency Identification and Text Information Extraction from Streaming Social Media Big Data,” International Journal of Critical Infrastructures, Vol. 19, No. 2, pp. 124-139, 2023, https://arxiv.org/abs/2106.07114.

11. Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, and Deheng Ye. “More agents is all you need.” arXiv preprint, 2024, https://arxiv.org/abs/2402.05120.

12. Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein, “Generative agents: Interactive simulacra of human behavior,” Proceedings of the 36th Annual ACM Symposium on User Interface software and Technology, pp. 1-22, 2023, https://dl.acm.org/doi/10.1145/3586183.3606763.

13. Alan Chan, Carson Ezell, Max Kaufmann, Kevin Wei, Lewis Hammond, Herbie Bradley, Emma Bluemke, Nitarshan Rajkumar, David Krueger, Noam Kolt, Lennart Heim and Markus Anderljung, “Visibility into AI Agents,” The 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 958-973, 2024, https://arxiv.org/abs/2401.13138.

14. Devjeet Roy, Xuchao Zhang, Rashi Bhave, Chetan Bansal, Pedro Las-Casas, Rodrigo Fonseca, and Saravan Rajmohan, “Exploring LLM-based Agents for Root Cause Analysis,” arXiv preprint, 2024, https://arxiv.org/abs/2403.04123.