An AI Agent refers to a system or program that can automatically perform tasks for users or other systems by setting workflows and utilizing existing tools to accomplish these tasks.
The functions of an AI Agent are not limited to natural language processing, but also include decision-making, problem-solving, interaction with the external environment, and execution of operations. These agents can be applied in various programs to solve complex tasks in a business environment, such as software design, IT automation, code generation tools, and conversational assistants. They utilize advanced natural language processing techniques from large language models (LLMs) to gradually understand and respond to user inputs, determining when to call external tools.
The core of an AI Agent lies in large language models (LLMs). Traditional LLMs primarily generate responses based on the data they were trained on, but their knowledge and reasoning abilities are limited. However, AI Agent technology optimizes workflows and autonomously creates sub-tasks to achieve more complex goals by using tool calls in the backend to obtain up-to-date information.
In this process, AI Agents can gradually adapt to user expectations over time. They remember past interactions and plan future actions, providing users with a personalized experience and comprehensive responses. This tool calling can occur without human intervention, greatly expanding the possibilities of these AI systems in practical applications. AI Agents typically go through the following three stages to achieve user-defined goals:
(1) Goal Initialization and Planning
Although AI Agents have autonomy in decision-making, they still require humans to define goals and set the environment. There are three main groups that influence AI Agent behavior:
The team of developers who design and train the AI Agent.
The team that deploys the AI Agent and provides user access.
The team of users who set specific goals for the AI Agent and provide available tools for its use.
Given the user’s goals and the tools available to the AI Agent, the AI Agent will decompose tasks to enhance performance. Essentially, the AI Agent creates a plan of specific tasks and sub-tasks to achieve complex goals. For simple tasks, planning may not be a necessary step. Instead, the AI Agent can iteratively adjust its responses and continuously improve without pre-planning the next step.
(2) Reasoning Using Available Tools
AI Agents take action based on the information they perceive. Typically, AI Agents do not possess the complete knowledge base required to handle all sub-tasks within complex goals. To address this issue, AI Agents will use the tools available to them. These tools may include external datasets, web search engines, APIs, and even other AI Agents. After retrieving the missing information from these tools, the AI Agent can update its knowledge base. This means that at each step, the AI Agent reassesses its action plan and self-corrects.
To illustrate this process more concretely, we can imagine a user planning their vacation. The user asks the AI Agent to predict which week next year might have the best weather for surfing in Greece. Since the LLM model at the core of the AI Agent does not specialize in weather patterns, the AI Agent collects information from an external database containing daily weather reports for Greece over the past few years.
Even after obtaining this new information, the AI Agent still cannot determine the best weather conditions for surfing. Therefore, it creates the next sub-task. For this sub-task, the AI Agent communicates with an external AI Agent that specializes in surfing. In doing so, the AI Agent learns that high tides and clear, almost rain-free weather provide the best surfing conditions.
Now, the AI Agent can combine the information learned from its tools to identify patterns. It can predict which week next year in Greece might have high tides, clear weather, and low chances of rain, presenting these findings to the user. This information sharing between tools makes AI Agents more versatile than traditional AI models.
(3) Learning and Reflection
AI Agents use feedback mechanisms, such as feedback from other AI Agents and human-computer interactions, to improve the accuracy of their responses. Let’s return to the previous surfing example to emphasize this point. After forming a response to the user, the AI Agent stores the information learned along with user feedback to enhance performance and adapt to user preferences for future goals.
If other AI Agents are used to achieve goals, their feedback can also be utilized. Multi-AI Agent feedback is particularly useful in minimizing the time human users spend providing guidance. However, users can also provide feedback throughout the AI Agent’s actions and internal reasoning process to make the outcomes more aligned with the expected goals.
The feedback mechanism enhances the reasoning capability and accuracy of AI Agents, often referred to as iterative refinement. To avoid repeating the same mistakes, AI Agents can also store data about previous obstacle solutions in their knowledge base. This way, when encountering similar problems, the AI Agent can find solutions more quickly and provide users with more efficient service.
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