Introduction: AI Agents refer to artificial intelligence systems that can autonomously execute tasks in specific environments. They not only receive tasks but also independently formulate and implement work plans while continuously self-evaluating and adjusting throughout the process, similar to human thinking and correction in creative tasks. The four key design patterns of AI Agents are the foundation for efficiently executing complex tasks and collectively constitute the capability framework of AI Agents. This article will delve into these four key design patterns.

The four key design patterns of AI Agents are as follows:

• Reflection: LLM reviews its own work to propose improvements.

• Tool Usage: LLM uses web searches, code execution, or any other functions to help gather information, take action, or process data.

• Planning: LLM proposes and executes a multi-step plan to achieve goals.

• Multi-Agent Collaboration: Multiple AI agents work together, assigning tasks and discussing and debating ideas, proposing better solutions than a single agent.

1. Reflection

As shown in Figure 1, the reflection pattern allows AI Agents to re-examine and evaluate their outputs after completing tasks. In this mode, AI Agents can not only execute tasks but also critically reflect on their work like human experts.

Figure 1 AI Agent’s Four Design Patterns – Reflection

Case:

An AI Agent might generate a piece of code and then self-check the correctness, efficiency, and structure of the code based on preset standards or feedback, proposing possible improvements. This self-supervision and correction ability allows AI Agents to continuously enhance accuracy and efficiency when executing tasks.

2. Tool Usage

Tool usage, as shown in Figure 2, empowers AI Agents with the ability to use external tools and resources to extend their functionalities and improve productivity. In this mode, AI Agents can search the web, generate and run code, analyze data, and utilize various tools to gather information and perform operations.

Figure 2 AI Agent’s Four Design Patterns – Tool Usage

Case:

An AI Agent might use image processing tools to analyze and process image data or call APIs to obtain and integrate external information. This capability allows AI Agents to interact with external systems, enabling them to better adapt to changing task requirements.

3. Planning

The planning pattern emphasizes that AI Agents can systematically plan and decompose steps when facing complex tasks. As shown in Figure 3, AI Agents can understand the overall goal of the task and formulate detailed action plans, gradually advancing the task process according to the plan. In this mode, AI Agents can demonstrate foresight and strategic thinking similar to humans. For example, an AI Agent might determine the main milestones of a project and then devise specific execution steps and timelines for each milestone to ensure the project proceeds in an orderly manner.

Figure 3 AI Agent’s Four Design Patterns – Planning

Case:

An AI Agent can automatically plan a path to achieve a given goal, such as planning a series of steps like research, design, coding, and testing when developing a new project, and automatically executing this plan, even re-planning to bypass obstacles when encountering issues.

4. Multi-Agent Collaboration

Agent collaboration highlights the cooperation and coordination between multiple AI Agents. As shown in Figure 4, in this mode, each AI Agent can play a specific role and collaborate with other AI Agents to complete complex tasks. This cooperation can simulate real-world team workflows, improving overall execution efficiency and innovation through the complementary and synergistic effects among agents.

Figure 4 AI Agent’s Four Design Patterns – Multi-Agent Collaboration

Case:

In an open-source software development project, one AI Agent might be responsible for writing code, while another AI Agent is responsible for code review and testing, collaboratively driving the project’s successful completion through such division of labor.

Conclusion

AI Agent intelligent workflows have demonstrated immense practical application potential across various industries. These agents’ applications in programming, research, and multimodal task processing will significantly expand the boundaries of AI capabilities in the coming years. Undoubtedly, the combined use of these four design patterns can enhance AI Agents’ execution capabilities in individual tasks and provide possibilities for collaboration and innovation in broader application scenarios. With the further development and refinement of these patterns, AI Agents will play an increasingly critical role in future workflows, driving various industries towards intelligent transformation.

If you want to learn more about AI Agents, including their technical principles, industrial applications, and commercial value, we recommend reading Professor Wang Jiwei’s new book “Understanding AI Agents: Technology, Applications, and Business”.

Author Bio:

Wang Jiwei is a senior observer, analyst, and evangelist in the AIGC field, with systematic sorting and in-depth thinking on the development and application of AI Agents. He currently focuses on the landing applications and entrepreneurial innovations of AIGC, AI Agents, and hyper-automation across various industries. His works are featured on major content platforms, academic websites, and magazines. He is the author of “Understanding AI Agents: Technology, Applications, and Business”.

Source:

This article is excerpted from “Understanding AI Agents: Technology, Applications, and Business“, by Wang Jiwei, published by the Machinery Industry Press, authorized for publication by the publisher. Please indicate the source when reproducing.

▼

Further Reading

“Understanding AI Agents: Technology, Applications, and Business“

Authored by Wang Jiwei

Comprehensive explanation from five dimensions: technology, applications, business, investment, and trends

Whether you are an enthusiast, researcher,

or an entrepreneur, businessperson, or investor

A comprehensive guide to understanding intelligent agents for everyone!

Content Summary:

This book explains AI Agents from the dimensions of technical principles, industry applications, commercial value, investment entrepreneurship, and development trends, possessing both popular science and business book attributes.

The book first details the technical paths of AI Agents and their applications in 11 major fields, with rich application cases helping readers deeply understand the product forms and service methods of AI Agents;then it delves into the commercial value and ecosystem of AI Agents, providing much thought and summary on enterprise-level applications and investment entrepreneurship, inspiring readers in application and entrepreneurship.This book integrates technology, applications, and business concepts, combining theoretical and practical aspects, making it a comprehensive guide suitable for industry insiders and outsiders to quickly understand AI Agents and enhance industry awareness, receiving unanimous praise from many industry professionals, experts, and entrepreneurs.

• Article Source: Original, Image Source: Original

• Editor: Wang Ying, Department Leader: Ning Shan

• Publisher: Bai Yu

Thanks to the Machinery Industry Press for sponsoring 4 copies. As usual, leave a message at the end, and I will select 4 peopleto receive “Understanding AI Agents: Technology, Applications, and Business” (Deadline: 12 PM on November 29).