What is AI Agent?

Recently, the development of AI technology has been changing with each passing day, especially in the field of AI intelligent agents, which is really dazzling.

I wonder if you, like me, are often a little dizzy with the concepts of various AI agents, AI assistants, and AI agents?

Don’t worry, today I will take you to unravel the mystery of AI agents step by step in the most easy-to-understand way.

This article will explain the following solutions:

• • What is an AI agent?
• • Why do we need AI agents?
• • The difference between AI agents and AI collaboration
• • Architecture of AI Agent
• • The relationship between AI agents and big models
• • Comparison of mainstream AI agent platforms

I believe that after reading this article, you will have a clear understanding of AI agents.

What is an AI agent?

AI agent, also known as artificial intelligence agent, is an artificial intelligence system that simulates human intelligent behavior, and its core engine is usually a large model (LLM). AI agent can perceive the environment, make decisions, and perform tasks to achieve specific goals.

Compared with traditional artificial intelligence, AI agents are autonomous, adaptable and interactive, and can operate independently in complex and changing environments.

AI agents can not only efficiently handle known tasks, but also flexibly respond to unknown environments. For example, traditional robots can only perform tasks according to preset programs, while AI agents can autonomously adjust strategies according to environmental changes and complete complex workflows.

Why do we need AI agents?

With the rapid development of technology, AI agents play a key role in improving efficiency, reducing costs and enhancing user experience.

Traditional large models (LLMs) such as ChatGPT, although they perform well in natural language processing, still have obvious limitations: they are prone to hallucinations, the output results are not reliable, it is difficult to grasp the latest current affairs, they cannot perform complex calculations, and they lack practical action ability and long-term memory ability.

To overcome these limitations, AI agents came into being. For example, when ordering takeout, traditional ChatGPT can only provide text suggestions, while AI agents based on ChatGPT can autonomously call applications and complete the entire process from selecting dishes to paying without human intervention.

This is because AI agents are able to break down complex tasks into specific steps and complete operations by calling search engines, operating apps, calling payment interfaces and other external tools.

More importantly, AI agents continuously improve their decision-making and execution capabilities through long-term memory and autonomous learning. They can not only efficiently handle current tasks, but also accumulate experience and continuously improve work efficiency and accuracy. With the advancement of technology, AI agents will surely become an important part of modern society and promote the intelligent transformation of all industries.

The difference between AI agents and AI collaboration

There is a significant difference between AI agents and the mode of collaboration between humans and AI. Traditional AI collaboration modes, such as Copilot, are more regarded as "co-pilots", providing assistance and advice to humans in specific tasks.

For example, GitHub Copilot provides real-time suggestions during the code writing process to help developers improve efficiency. However, Copilot relies on explicit user instructions, and its capabilities are limited by the specific needs of the user and the clarity of the prompts.

In contrast, AI agents have greater independence. Once a goal is set, AI agents can think and act autonomously, break down the task steps in detail, and use external feedback and self-generated prompts to achieve the goal.

For example, if the goal of an AI agent is set to “optimize the existing project management process,” the agent will autonomously analyze the existing process, identify bottlenecks, propose improvement plans, and perform related operations without the need for step-by-step guidance.

The architecture of AI agents

The architecture of AI agents usually includes four key components: perception, planning, memory, tools, and action. These components work together to give agents the ability to make autonomous decisions and perform tasks.

1. Perception

Perception is the basic interface for AI agents to interact with the external environment. It is responsible for collecting and parsing environmental data, including text, images, sounds and other forms.

Let’s take a “meeting assistant” AI agent as an example. The user says to the “meeting assistant”, “arrange a team meeting tomorrow afternoon, the topic is the team work arrangements for the first quarter”. The agent first needs to obtain voice data through the microphone and convert it into processable text information.

2. Planning

Planning, as the decision-making center of the AI ​​agent, is responsible for breaking down goals into executable steps and formulating implementation strategies.

Chain of Thoughts has become a standard prompting technique used to improve model performance on complex tasks. Models are asked to “think step by step”, breaking down complex tasks into smaller, simpler steps.

For the task of "arranging a team meeting", the agent needs to plan the specific steps and arrange the execution order reasonably. For example:

• •  Understanding user needs : When the user says “arrange a team meeting tomorrow afternoon”, the agent first understands that this is a task that requires scheduling, inviting participants, and determining the content of the meeting.
• •  Subtask decomposition : The assistant breaks down the meeting arrangement task into multiple stages: determine the meeting time, choose the meeting place, invite participants, prepare the meeting agenda, and send meeting invitations.
• •  Dependency check : If it is found that some participants have other arrangements at the specified time, the system will prompt users to select other times, or automatically find the best time slot through the meeting time detection tool.

The effectiveness of planning directly determines the smooth arrangement of the meeting and the satisfaction of the participants. Through reasonable task decomposition and optimization, AI agents can help users complete complex meeting arrangement tasks efficiently and orderly.

3. Memory

The memory module stores various types of information, covering historical interactions, knowledge accumulation, and temporary task data. It is divided into short-term and long-term. Short-term memory stores current session information, and long-term memory stores persistent data such as user preferences and historical records. AI agents access these memories through fast retrieval mechanisms to support the execution of complex tasks.

In the task of "scheduling a team meeting", the agent needs to remember the user's preferences, historical meeting data, and previous scheduling experience. Short-term memory can store current conversations and temporary information, while long-term memory relies on external databases or cloud records to store users' common meeting times, participants' preferences, and historical meeting records.

4. Tools Use

Tool usage enables AI agents to call on external resources to extend their capabilities, including APIs, code libraries, applications, or other services.

Simply relying on the internal knowledge of a large model cannot solve all problems. If the agent can autonomously call the API of a calendar application, email system, or conference platform, it can obtain more accurate and timely information, making the meeting arrangement process smoother. For example:

• •  Calendar API : When a user needs to schedule a meeting, the assistant can call the Calendar API to automatically check the free time of the user and participants and select the best time period.
• •  Mail System API : The assistant can automatically generate and send meeting invitation emails, including information such as meeting time, location or online link, agenda, and track participants' responses.
• •  Meeting platform API : If the meeting needs to be conducted online, the assistant can call the API of Feishu Conference or Tencent Conference to automatically create a meeting link and attach it to the invitation.
• •  Task management tool : If the meeting involves specific tasks, the assistant can call the API of the task management tool to automatically create relevant tasks and assign them to the corresponding personnel.

5. Action

Action is the specific manifestation of the AI ​​agent's execution of tasks and interaction with the environment. It performs specific actions based on planning and memory, responds to environmental changes and completes the given tasks.

After planning the steps for the meeting, the agent will eventually need to put those plans into action. This includes not only providing specific guidance and suggestions, but also directly operating the relevant tools to complete the task.

The relationship between AI agents and big models

Although AI agents and big models are closely related, they are essentially different. Big models are the core of AI agents, providing them with language understanding and generation capabilities. In addition to big models, AI agents also have multiple capabilities such as planning, memory, and tool use, which give them stronger autonomy and execution.

As the "brain" of the AI ​​agent, the big model is responsible for processing and generating natural language, and has the ability of logical reasoning and language comprehension. It can generate reasonable output based on input, such as ChatGPT can understand complex instructions and generate detailed plans. However, the big model itself cannot perform specific tasks and needs to rely on other components of the AI ​​agent to complete the operation.

The AI ​​agent achieves a higher level of intelligent behavior by integrating the big model and combining planning, memory, and tool use functions. It can autonomously call external APIs based on the plan generated by the big model to complete tasks such as booking restaurants and arranging meetings. At the same time, its memory module can store and retrieve long-term information to ensure contextual coherence in multiple rounds of conversations.

Comparison of mainstream platforms for AI agents

As AI agent technology develops, the platforms for building and deploying AI agents are evolving rapidly. These platforms provide a wealth of tools and frameworks that allow developers to easily create complex intelligent systems. The following are the current mainstream platforms:

1. Dify

Dify is an open-source large language model application development platform that supports hundreds of models such as GPT, Mistral, and Llama3. The platform provides a declarative development environment (using YAML to define applications), modular design, LLMOps functions (monitoring and optimizing application performance), and private deployment capabilities. Its positioning is to simplify the development process of complex AI applications, and is particularly suitable for scenarios that require deep customization or enterprise-level deployment.

Advantages:

• • International support: mainly targeting overseas markets, integrating multi-language models and internationalization tools.
• • Flexibility and scalability: Supports self-hosting and cloud services, and can seamlessly integrate with the company's existing systems to meet data security and compliance requirements.
• • Active developer ecosystem: The open source community provides rich templates and collaboration opportunities, supporting rapid iterative innovation (such as Workflow visual process).
• • Multi-model comparison: supports simultaneous testing of responses of different models (such as GPT-4 and Claude3) to optimize task adaptability.

Disadvantages:

• • High learning threshold: Model integration and configuration require technical background and are not friendly to novices.
• • Weak domestic ecosystem: Compared with Coze, domestic market share and plug-in support are limited.

Applicable scenarios:

Enterprise-level LLM infrastructure construction, private deployment, and developer-led complex AI application development.

2. Coze

Coze is a low-threshold intelligent agent development platform launched by ByteDance. It features a natural conversation experience, supports voice recognition/generation, a rich plug-in ecosystem, and can be embedded in web pages through the Web SDK. Its core user groups are C-end users and lightweight application developers.

Advantages:

• • Ultimate user experience: simple interface, smooth conversations, and precise voice interaction, making it easy for non-technical users to get started quickly.
• • Plug-in and ecological advantages: built-in multi-field plug-ins (such as e-commerce and customer service), relying on ByteDance’s technical resources, and strong domestic ecological support.
• • Free GPT-4 access: The international version supports free use of the GPT-4 model, which has high functional maturity.

Disadvantages:

• • Insufficient customization: It is mainly focused on standardized Bot development, its scalability for complex tasks is weaker than Dify and FastGPT, and it only supports cloud deployment.

Applicable scenarios:

Intelligent customer service, voice assistants, social media chatbots and other C-end applications that focus on interactive experience.

3. FastGPT

FastGPT focuses on the development of knowledge question-and-answer agents, optimizes knowledge base retrieval based on RAG technology, and is suitable for enterprise-level deep customization, but its ecosystem mainly focuses on the domestic market.

Advantages:

• • Vertical field advantages: Outstanding performance in knowledge base construction and complex question-and-answer scenarios, and support for highly customized functions.
• • Open source and scalability: attracts developer contributions and is suitable for teams that need independent optimization.

Disadvantages:

• • Complex deployment: requires technical background configuration and is not friendly to beginners.
• • Ecosystem limitations: Weak internationalization support, and fewer plug-in and model integration options than Dify and Coze.

Applicable scenarios:

Enterprise knowledge base management, professional question-and-answer systems, and industry solutions that require localized deployment.