In recent years, generative AI has been gaining momentum, and products such as ChatGPT and MidJourney have become the focus of public discussion. Recently, from Google's 40-page white paper on "AI Agent" to the comprehensive layout of Microsoft, OpenAI and other giants on the B-side and C-side, AI Agent has become the hottest topic in the technology field. It is not only an extension of the large language model, but also a key path to general artificial intelligence (AGI).

This article will give you an in-depth understanding of the core concepts, working principles, application scenarios and future trends of AI Agent, revealing the logic and opportunities behind this intelligent revolution.

1. What is AI Agent?

Definition and core features

AI Agent is an agent that can independently plan, make decisions and execute Intelligent systems for tasks. It combines the powerful understanding ability of large language models (LLM) with functions such as tool use, memory management, and task planning, so that it can not only "understand" human instructions, but also "hands-on" complete tasks. For example, AI Agent can automatically book restaurants, generate reports, and even complete complex programming tasks based on user needs.

Differences from large language models

Large language models (such as ChatGPT) are more like A "super brain" that is good at generating content and answering questions, but lacks the ability to act. The AI ​​Agent is a "complete body" that not only has a "brain", but also has "hands, feet" and "tools". For example, when a user asks to "compare the differences between company A's products and ours and send a report to an email address," the AI ​​Agent will actively call search engines, databases, and email tools to complete the entire task process.

2. The technical architecture of AI Agent

According to Google’s white paper, the technology of AI Agent The architecture consists of three key modules:

Reasoning Layer

As the core of decision-making, it supports instruction-based reasoning and logical framework. This is the "brain" of the AI ​​Agent. Based on a large language model (such as LLM), it can understand the complex requirements of user input and perform logical reasoning. For example, When you tell it: "Help me arrange a three-day trip to Dubai suitable for the whole family", it can generate a practical plan based on your needs.

Tool Layer

Extension: connect API and agent, support dynamic selection of appropriate tools

Function: executed on the client. API calls provide more granular control

Data storage: Provides access to structured and unstructured data through vector databases, supporting retrieval enhanced generation (RAG). ) 16.

AI Agent does not work alone. It can call external tools and data sources, such as calendars, emails, search engines, and even link with smart home devices. In this way, it can perform tasks such as "making an appointment with a doctor" and "managing schedules." Specific tasks.

Orchestration Layer

This is AI The Agent's "command center" is responsible for scheduling the reasoning layer and tool layer to ensure that tasks are carried out in an orderly manner. For example, when completing a three-step task, it can ensure that all steps are connected smoothly without any omissions or confusion.

< p style="text-align: left;">3. The difference between AI Agent and model

AI Agent significantly improves the model's capabilities through tools and orchestration layers, enabling it to handle more complex tasks

4. How AI Agent works

The AI ​​assistant Jarvis in "Iron Man" shows mankind's ultimate imagination of intelligent assistants: it can not only connect to any computer terminal and control the complex Iron Man suit, but also assist in formulating action plans and become a Tony Stark's "Digital Companion" This vision has long existed only in science fiction.products, but in reality voice assistants (such as Siri, Alexa) have limited functions and are far from Jarvis’s level of intelligence. However, with the breakthrough progress of large language models (LLM), AI Agent (artificial intelligence agent) emerged as the times require. It can independently plan tasks, perform operations and seamlessly integrate with other services, truly realizing efficient collaboration between humans and artificial intelligence.

AI Agent is an intelligent system that can plan, make decisions and execute tasks independently. Its core lies in combining the powerful understanding capabilities of large language models (LLM) with functions such as tool calling, memory management, and task planning, so that it can not only understand human instructions, but also actively complete complex tasks. The following is a detailed analysis of the workflow and logic of AI Agent.

(1) AI Agent’s workflow

The AI ​​Agent’s workflow can be summarized into three core steps: perception and reception → understanding and reasoning → planning and execution.

a. Perception and reception

AI Agent uses multi-modal input (such as text, images, voice, sensor data) to receive information. For example, when the user enters "Will it rain tomorrow?", the AI ​​Agent can recognize that this is a query request about the weather.

b. Understanding and reasoning

AI Agent uses knowledge base and reasoning framework (such as ReAct , thinking chain, thinking tree) to analyze the received information. For example, it calls the weather API to obtain the latest meteorological data and determines the probability of precipitation through logical reasoning.

c. Planning and execution

AI Agent can not only generate text answers, but also call External tools get the job done. For example, it will output: "According to the current weather data and forecast, the probability of precipitation tomorrow is 80%. It is recommended that you bring an umbrella." In addition, the AI ​​Agent can also control physical equipment (such as automatic umbrella delivery) to further meet user needs.

(2) Example of technical logic of AI Agent

Scenario: The user asks "Will it rain tomorrow?"

Perception and reception: AI Agent passes text, voice or imageReceive user questions.

Understanding and reasoning:

Call the weather API to query the latest weather forecast data.

Analyze data and determine the probability of precipitation.

Develop an action plan, such as reminding users to bring rain gear.

Planning and execution:

Generate text answer: "The probability of precipitation tomorrow is 80%. It is recommended that you bring an umbrella. ”

If equipped with a physical device, the AI ​​Agent can also automatically deliver an umbrella or adjust smart home devices (such as closing windows).

(3) Logical advantages of AI Agent

a. Autonomy and task planning

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AI Agent can plan tasks and execute them independently without the need for step-by-step guidance from the user. For example, when a user says "I want to travel to Sanya", the AI ​​Agent will automatically plan the itinerary, book air tickets and hotels, and generate a personalized travel plan.

b. Tool calling and environment adaptation

AI Agent can call external tools and data sources , complete complex tasks. For example, it can query real-time weather data through APIs, or control smart home devices (such as adjusting air conditioning temperature). In addition, AI Agent can learn to use new software tools by observing human operations, further expanding the boundaries of its capabilities.

c. Multi-step task processing and dynamic adjustment

AI Agent can efficiently handle multi-step tasks tasks and ensure each step flows seamlessly. For example, when completing a workflow containing multiple subtasks, the AI ​​Agent can execute each step in sequence and dynamically adjust the plan according to changes in the environment.

5. Application scenarios of AI Agent

AI Agent has shown strong application potential in many fields:

Finance: automatically execute transactions, generate financial reports, and optimize investment portfolios11 .

Medical care: auxiliary diagnosis, medical record management, and surgical support to improve the efficiency and accuracy of diagnosis and treatment11. left;">E-commerce: Optimize product recommendation, automated customer service, and intelligent marketing strategies14.

Game: Introduce independent AI NPC to enhance player immersion8.< /p>

Legal: Automated legal document drafting, case research, contract review 11.

6. Industry. Dynamic and giant layout

Google

The 40-page AI Agent white paper released by Google details the architecture and application of Agent, emphasizing its potential in the field of generative AI. Google's Vertex AI platform provides developers with tools to build and deploy agents, supporting the rapid implementation of complex tasks

Microsoft

Microsoft passes Copilot Studio has built the world's largest enterprise-level AI Agent ecosystem. Microsoft's AI Agent has been used in multiple industries to help enterprises improve efficiency and innovation capabilities.

OpenAI

OpenAI plans to launch Operator AI Agent to support complex tasks such as automated code writing and travel booking. OpenAI's AI Agent has advanced capabilities in natural language processing and task planning. Significant advantages

Zhipu AI

Zhipu AI has launched AutoGLM, GLM-PC and other intelligent agents, covering mobile phone, PC and web page operations. Zhipu AI The Agent performs well in personalized services and multi-modal interaction.

7. Future trends of AI Agent

2025, the first year of commercialization

2025 is considered the first year of commercial application of AI Agent. As the technology matures, AI Agent will find a wide range of application scenarios in finance, medical, legal and other fields, significantly improving efficiency and reducing costs.< /p>

Stronger autonomy and intelligence

Future AI Agents will have stronger autonomous decision-making capabilities and can complete autonomous tasks in more scenarios Tasks. For example, through continuous learning and environmental adaptation, AI Agents will be able to handle more complex multi-step tasks

Ethical and safety challenges

With AI With the improvement of Agent's capabilities, its security and ethical issues have also received unprecedented attention. The research community is developing new security frameworks to ensure that the behavior of AI Agents always complies with predetermined ethical guidelines.

The emergence of AI Agent marks the transition of artificial intelligence from "tool" to "intelligent partner". Its application prospects are broad and exciting, from the workplace to life. Smartphones reshape the way we communicate, AI Agent may become a "new necessity" in our lives and work, deeply integrated into daily life, bringing unprecedented convenience and efficiency to everyone.

However, technology has changed. Development never stops at being amazing, it also requires prudent reflection and planning. While enjoying the dividends brought by AI Agent, we must face up to important issues such as privacy protection and security to lay a more solid foundation for its popularization and application. Promote artificial intelligence towards a more reliable and humane future

The era of AI Agent has quietly begun, and it is changing the way we understand and use technology. Are you ready to join hands with it to move towards a new intelligent future?