What are AI agents?

AI agents refer to systems or software designed to autonomously perform specific tasks on behalf of users or other systems by generating their own action plans and utilizing available tools. These agents go beyond natural language processing boundaries, encompassing capabilities such as decision-making, problem-solving, interacting with surrounding environments, and executing practical actions.

AI agents can be applied in a wide range of scenarios, from automating IT operations to code generation and software design, as well as serving as conversational and smart assistants across many fields. These agents rely primarily on advanced natural language processing techniques used in large language models (LLMs), working to understand and analyze user input step-by-step and determining when to invoke external tools or sources to achieve desired objectives.

How do AI agents work?

At the core of AI agents are large language models (LLMs), which are considered the primary component for powering these agents. For this reason, they are sometimes known as LLM agents. Although traditional models like “IBM Granite” rely for their responses on the data they were trained on, making them limited in terms of knowledge and reasoning capabilities, AI agents excel through the ability to invoke external tools and operate flexibly to improve performance and deliver more comprehensive solutions.

Continuous learning

Agents are characterized by their ability to learn over time, using techniques such as storing and analyzing past interactions, which enables them to improve their performance and customize their responses to suit user needs. With the capability to automatically invoke tools without human intervention, these agents can adapt to user needs and real-world applications more efficiently.

The three stages of AI agent operation

1. Goal setting and planning

Although AI agents are capable of making independent decisions, they rely on specific goals and environments created by humans. These goals are established by three main parties:

• Development team: Designs the system and trains it on required tasks.
• Deployment team: Provides access to the agents and defines their operational environment.
• End user: Sets the goals and provides the tools necessary to achieve those goals.

After the goal is set by the user, the agents analyze the task and break it down into smaller subtasks, enabling complex tasks to be executed more efficiently. In cases where tasks are simple, upfront planning may not be necessary, as agents can progressively refine their responses without needing to establish a comprehensive plan for subsequent steps.

2. Using available tools

AI agents rely on available tools and resources to supplement their knowledge and execute tasks. Sometimes, an agent may not possess all the information required to achieve objectives, so it can use tools such as external databases, internet searches, application programming interfaces (APIs), and occasionally other agents to obtain missing information.

Practical example:
If a user asks an AI agent to determine the best time for a surfing trip in Greece next year, the agent can begin by analyzing past weather reports. If the agent does not specialize in weather patterns, it might invoke a specialized external agent to analyze optimal surfing weather conditions. Based on that, the primary agent gathers and analyzes information to provide accurate recommendations to the user.

3. Continuous learning and development

AI agents use feedback mechanisms to improve their performance. This feedback may come from the end user or from other agents invoked to achieve the goal. Comments and information learned during various operations are stored to improve future performance. For instance, if a user provides feedback on an agent’s recommendations, the model is adjusted to better align with user expectations.

The difference between smart and non-smart agents

Non-smart agents:

• Depend on instantaneous responses and lack memory to store data.
• Unable to learn from past mistakes.
• Require continuous user input to execute every step.

Smart agents:

• Possess the ability to learn over time and customize responses to match users.
• Capable of creating subtasks and working independently to achieve complex goals.
• Use available resources to bridge knowledge gaps and continuously update their memory.

Reasoning and decision-making methods in AI agents

ReAct model (Reasoning and Acting):

This model relies on a cycle of thinking before and after each action. Agents analyze each step based on their available tools. This model is used to solve problems incrementally and improve agent responses with each new interaction.

ReWOO model (Reasoning Without Observation):

Unlike ReAct, this model plans ahead for all actions to be taken based on initial user input. This approach reduces the need for repetition and ensures efficiency by minimizing the use of unnecessary tools.

Types of AI agents

1. Simple reflex agents:

Simple reflex agents are the most basic type of AI agents. These agents rely on immediate responses based on a predefined set of rules and conditions. When an agent encounters a situation matching these rules, it takes a specific action without any additional thought or analysis. However, these agents do not retain memory of any past interactions, meaning they cannot learn or improve their performance over time.

For example, a smart thermostat is programmed to turn on the heating when the temperature drops below a certain level. The condition here is the temperature drop, and the action is turning on the heating. This type of agent cannot adapt to new situations or improve its performance because it lacks any capability to learn or store information.

2. Model-based reflex agents:

Model-based reflex agents add an extra step to simple reflex agent functions. They use internal models that represent an understanding of the surrounding environment and continuously update these models based on changes occurring in the environment.

These agents do not rely solely on pre-programmed rules, but also on new data gathered during operation. For example, a robotic vacuum cleaner (such as a Roomba) can capture data about the location of obstacles inside a room, then adjust its path to clean the floor effectively. This type of agent enables handling dynamic and changing environments, though it remains limited by the boundaries set by rules and models.

3. Goal-based agents:

Goal-based agents are a more advanced evolution, adding an essential element: a specific goal they strive to achieve. These agents are not limited to responding only to conditions; they develop a comprehensive plan to reach that goal. They analyze available options and determine the actions needed based on a chronological or logical sequence ensuring the goal is achieved as efficiently as possible.

For example, a car navigation system aims to guide the driver to their destination as quickly and cost-effectively as possible. The agent calculates different routes based on current conditions such as traffic, distance, or time, then recommends the best route to achieve the goal.

4. Utility-based agents:

Utility-based agents add a new dimension consisting of evaluating options based on a set of criteria or values. This type of agent does not just achieve the goal, but also seeks the best possible outcome based on factors such as efficiency, time, resources, or even sustainability.

For example, an AI agent working in trip planning can recommend a route that not only provides the fastest travel time but also minimizes fuel consumption and toll costs. This is done by using a utility function that calculates the value of each option based on a specific set of criteria.

5. Learning agents:

Learning agents are the most advanced type of AI agents. These agents combine all previous characteristics while adding a fundamental feature: the ability to learn over time. These agents have the capacity to improve their performance through experiences they undergo and information gathered during operation.

This type of agent consists of several main components including:

• Learning element: Enhances the agent’s knowledge based on new data.
• Critic: A feedback system that provides the agent with information about the quality of its performance.
• Performance element: Determines how actions are selected and executed.
• Problem generator: Helps the agent explore new options to achieve goals.

For example, an AI agent on an e-commerce platform can learn from user habits, such as products they prefer or times they tend to shop. Based on that, it can provide more accurate recommendations to users over time, leading to improved customer experience and increased system efficiency.

Use cases of AI agents

1. Customer experience:

Customer experience is one of the most prominent fields benefiting from AI agents, where these agents are used to improve service quality and make interactions more efficient and seamless.

• Conversational support:
  Smart agents are used in chatbot systems to provide quick and accurate answers to customer inquiries. For example, an AI agent can help customers resolve issues related to a company’s products or services, such as tracking orders, processing complaints, or offering personalized suggestions based on customer needs.
• Functional simulation:
  In recruitment and training processes, AI agents can be used to simulate job interviews or realistic training exercises. This helps new employees better understand the work environment by practicing near-real scenarios.
• Psychological support:
  In mental health platforms, agents can provide initial psychological support to users by analyzing their emotions and offering preliminary guidance, such as breathing exercises or mood-improvement suggestions. These agents can also serve as a first point of contact for users experiencing psychological stress before transferring them to specialists.

2. Healthcare:

The healthcare sector is witnessing a major transformation with the entry of AI agents, offering solutions that help improve efficiency and reduce the burden on medical staff.

• Treatment schedule management:
  Smart agents can organize patient schedules, schedule medical visits, and send reminders regarding medications or treatment sessions. This type of agent helps reduce errors resulting from manual scheduling while saving time for both doctors and patients.
• Treatment recommendations:
  Agents are used to analyze patient data such as medical records and clinical history to provide accurate treatment recommendations. For example, an AI agent can suggest a treatment or medication based on analyzing a patient’s medical data and comparing it with massive databases containing medical research.
• Early diagnosis:
  Agents can use machine learning techniques to analyze radiology images, examine lab test results, and provide early disease diagnoses. For instance, a smart agent can detect cancer signs in medical scans faster than humans, assisting in early intervention.

3. Emergency response:

Natural disasters and emergency situations are among the scenarios where AI agents can make a significant difference in efficiency and response speed.

• Social media data analysis:
  During disasters like earthquakes or floods, smart agents can analyze social media posts to identify individuals needing assistance. This is done by using algorithms that understand published texts and images to determine the location and current status of users.
• Directing rescue teams:
  After locating individuals needing help, smart agents can transmit this information to rescue teams. For example, an AI agent can provide an accurate map showing gathering points needing assistance, contributing to more efficient resource direction.
• Disaster prediction:
  AI agents can use climate and historical data to predict the occurrence of natural disasters, such as hurricanes or floods. This prediction can help governments and organizations take proactive measures to protect populations and minimize damage.

Key benefits of AI agents

1. Task automation:

Task automation is one of the standout benefits offered by AI agents, giving users the ability to execute complex operations faster and at lower costs. This is demonstrated by the agents’ ability to analyze data and make decisions based on predefined criteria without needing continuous human intervention.

• Time-saving:
  When an AI agent takes over a complex task, such as processing a large volume of customer requests or organizing complex schedules, it does so in a fraction of the time it would take a human to do the same task. For example, in supply chain management, agents can predict product demand, reorganize inventory, and optimize the delivery process.
• Cost reduction:
  Since these systems can operate complex processes automatically, they reduce the need for a large number of human employees. Agents in manufacturing industries, for instance, can optimize production line efficiency, reducing waste and saving resources.
• Ability to handle routine tasks:
  Agents can execute tedious routine tasks with high accuracy, such as data entry or sending reminders, allowing humans to focus on creative and strategic activities.

2. Performance improvement:

AI agents’ ability to improve performance becomes clear when using multi-agent systems. These systems rely on collaboration between specialized agents, leading to better results than individual work.

• Agent collaboration:
  When different agents work together, each agent focuses on a specific part of the overall task. For example, in a smart city management system, a traffic management agent can communicate with an energy management agent to ensure smooth traffic flow while minimizing energy consumption at traffic lights.
• Data integration and analysis:
  These systems allow massive amounts of data to be collected and analyzed quickly and efficiently. AI agents in scientific research, for example, can analyze huge amounts of genomic data in a short time, accelerating the discovery of medical treatments.
• Increased flexibility:
  Multi-agent systems increase responsiveness flexibility to changing conditions. If one agent encounters a problem or breakdown, other agents can adjust their plans to compensate for this flaw, ensuring uninterrupted business continuity.

3. Response quality:

AI agents are characterized by their ability to deliver customized and accurate responses that go beyond what can be achieved using traditional models. This makes them an effective tool for improving user experience across a variety of fields.

• Instant personalization:
  Agents can analyze user behavior and provide recommendations or solutions tailored to individual needs. For instance, an AI agent on an e-commerce platform can offer purchase recommendations based on a user’s purchase history and interests.
• High accuracy:
  Thanks to machine learning techniques and big data analysis, agents deliver more precise and specific responses compared to traditional systems. This is especially useful in applications requiring high accuracy, such as medical diagnosis or financial data analysis.
• Continuous learning:
  Unlike traditional models, agents can improve their responses over time by learning from past interactions. This ensures that systems become smarter and more efficient with continued use, enhancing service quality.

Risks and challenges

1. Computational complexity:

Computational complexity is one of the prominent challenges facing AI agent development. Building these systems requires massive resources in terms of computing power, model training time, and technical expertise to manage the entire process.

• Resource consumption:
  Modern AI models, especially those based on large language models (LLMs), require huge amounts of data for training. Processing and analyzing this data requires sophisticated data centers capable of running complex algorithms at scale.
• Training time:
  Training a high-performing AI agent can take weeks or even months, depending on the model size and data used. This long timeframe impacts innovation speed, especially in sectors requiring immediate responses to changes.
• Development cost:
  Developing AI agents is not only slow but also very expensive. Between purchasing computing resources and paying cloud hosting costs, building an advanced agent can be a major barrier for startups or organizations with limited budgets.

2. Unproductive loops:

Unproductive loops are another issue that can arise when using AI agents, especially if tasks and plans are not carefully designed.

• Over-reliance on tools:
  When an agent encounters a lack of data or an incomplete understanding of its environment, it may enter a cycle of invoking tools or executing the same steps repeatedly without reaching a final result. This leads to unnecessary resource consumption and task completion delays.
• Lack of a stopping mechanism:
  In some cases, agents may lack a built-in system to determine when to stop trying. This can result in inefficient time and resource consumption, reducing overall system efficiency.
• Impact of errors in complex tasks:
  In environments relying on integration between multiple agents, a simple error in a single agent’s loop can disrupt the entire system. If the issue is not detected in time, it can halt critical operations.

3. Security risks:

Cybersecurity presents a major challenge when dealing with AI agents, as these agents rely on sensitive data and external tools that may be vulnerable to breaches.

• Data exposure:
  AI agents need access to massive databases and must analyze them. In the event of a breach, this data, including personal or financial information, can become vulnerable to exploitation.
• Reliance on external tools:
  Many agents rely on third-party tools or APIs to gather data or execute operations. If these tools are not adequately secured, they can become a vulnerability point for the entire system.
• Deliberate attacks:
  Attackers can exploit design vulnerabilities in AI agents to manipulate their outputs or disrupt their operations. For example, in AI systems used in security or healthcare, such attacks could have devastating effects.
• Lack of accountability:
  In the event an agent makes an undesirable decision based on misleading or flawed data, determining responsibility can be difficult. This raises legal and ethical concerns regarding the use of these systems in sensitive decisions.

Best practices for developing AI agents

1. Providing activity logs:

Activity logs are a fundamental best practice to ensure the transparency and effectiveness of AI agents. These logs help users understand the decisions made by the agent, building trust in the system.

• Clear decision visibility:
  Logs provide precise details about every step taken by the agent, including tools used, data collected, and analysis performed. This allows users to trace how final results were reached.
• Error identification:
  If a problem or error appears in agent performance, users or developers can refer to logs to quickly identify and fix the source of the issue.
• Enhancing transparency:
  When users have access to logs, it becomes easy for them to understand how the system works. This is especially important in applications requiring a high level of trust, such as healthcare or financial services.

2. Human supervision:

Despite AI agent advancements, human supervision remains necessary, especially in highly sensitive or high-risk tasks.

• Avoiding undesirable decisions:
  In some cases, an agent may make unexpected decisions due to data limitations or analysis errors. Involving humans in the decision-making process ensures final results align with required standards.
• Critical tasks:
  For tasks involving individual safety or large resource management, such as sending financial orders or medical decisions, the agent must obtain human approval before execution.
• Improving learning:
  Human supervision can provide immediate feedback to agents, helping them learn and improve performance over time.

3. Setting unique agent identifiers:

Using unique identifiers for each AI agent is an effective way to enhance security and accountability in AI systems.

• Origin tracking:
  By assigning a unique identifier to each agent, its origin, developers, and operating environment can be tracked. This helps determine responsibility when any issue or violation occurs.
• Reducing malicious use:
  When agents are equipped with distinct identifiers, detecting unauthorized use or unwanted system modifications becomes easier.
• Enhancing security:
  If systems are designed so agents cannot access tools or data without presenting their identifiers, security breach risks can be minimized.

4. Continuous feedback:

Feedback is one of the most important practices for improving AI agent performance, helping make the system smarter and more effective over time.

• Learning from mistakes:
  When feedback is provided to agents after each task, they can analyze these notes to avoid past mistakes and improve future performance.
• Adapting to user needs:
  By listening to user reactions, agents can adjust their responses and plans to better suit each user’s needs.
• Improving decisions:
  Continuous feedback can help improve the accuracy of decisions made by the agent, leading to increased efficiency and reduced task execution time.

Importance of applying these practices:

Following the aforementioned best practices ensures that AI agents are designed in safer and more efficient ways, enhancing their capability to meet user needs reliably and effectively.

Conclusion

AI agents represent a tremendous advancement in the field of intelligent automation and artificial intelligence. Thanks to their ability to continuously learn and adapt to changing user needs, these agents provide innovative and effective solutions across many domains. However, to maximize the benefits of this technology, best practices must be considered to ensure its safety and effectiveness while minimizing associated risks.