AI Agent In-depth analysis of how AI agents change future working modes

This article will take you to review the history of AI agent (AI agent), understand its technical principles, application scenarios, and how major companies currently import AI agents and AI agent workflows (AI Agentic Workflow), and finally discuss its future development trends.

AI Agent (AI Agent) is a software system based on artificial intelligence that can independently learn, perform tasks and interact with the environment. These agents can handle a variety of complex tasks, including data analytics, customer service, content creation, decision support, etc., and improve execution efficiency through machine learning and natural language processing (NLP) technologies.

Simply put, An AI agent is like a digital assistant, able to perform specific tasks based on instructions, and even optimize decisions based on learned information. Enterprises can use AI agents to automatically respond to customer questions, generate sales reports, and even assist in developing code.

The concept of AI agents can be traced back to the 1956 Dartmouth Conference, a conference initiated by John McCarthy and others, which is seen as the starting point for artificial intelligence. At that time, researchers began to think about how to enable machines to learn and make decisions.

Until 1994, AI research scholars Michael Wooldridge and Nicholas Jennings formally defined AI agents in the book Intelligent Agents. They proposed that AI agents are an intelligent system that can independently perceive the environment, make decisions and perform tasks, and can adapt and learn according to different situations.

Then, AI proxy technology began to flourish in the 2000s. With the advancement of deep learning, natural language processing (NLP), and cloud computing, AI proxy has been used in search engines (such as Google Search), personal assistants (such as Siri, Alexa), and financial trading markets.

A complete AI agent should have the following core capabilities:

• Environment perception and learning: AI agents learn and adapt to new environments through big data, sensors or network data.
• Natural Language Processing (NLP): AI agents should be able to understand human language, such as chatbots like ChatGPT and Google Bard.
• Decision making and action execution: Through machine learning and predictive analytics, AI agents can make the best decisions, such as financial transactions AI.
• Automation and task management: Ability to coordinate different systems to complete tasks from simple information search to complex business decisions.

Before discussing AI Agents, we must first understand another key technology - AI large language models (LLMs), such as OpenAI's GPT-4, Google's Gemini, and Meta's Llama. Many people tend to confuse the two, believing that AI agents are an application of the AI ​​large language model, but in fact, there are significant differences in their architecture, application scope and operation methods.

1. Main design objectives

AI Big Language Model (LLM): It is mainly used for language understanding and generation. Through huge corpus training, it can perform tasks such as text completion, dialogue, translation, and content generation. For example, ChatGPT is able to produce reasonable responses based on input questions and provide information in conversations.

AI Agent: It is a broader intelligent system that not only includes language processing capabilities, but also can make independent decisions, perform tasks, and interact with the environment. AI agents usually combine LLM as part of their capabilities, but it emphasizes more on mobility and automation.

2. The difference between passive and active

LLM is a "passive response type": it requires the user to input instructions to provide corresponding output, and is a passive AI system. For example, when you ask ChatGPT a question, it will give an answer without proactively performing any actions.

AI agents are "active execution": AI agents can automatically monitor the environment, make decisions and execute actions according to task requirements. For example, Tesla's autonomous driving system can automatically adjust speed and change lanes according to traffic conditions without the driver's manual operation.

3. Task scope and application

Main applications of LLM:

• Natural language understanding and generation (such as ChatGPT)
• Text summary and translation
• Content creation (blog, press release, product description)
• Code completion and error detection (such as GitHub Copilot)

The main applications of AI agents:

• Independent decision-making and workflow automation (such as enterprise management, supply chain optimization)
• Autonomous driving and robot control (such as Tesla FSD, Boston Powered Robot)
• Financial transactions and risk management (such as AI high-frequency transactions)
• Smart customer service and AI assistants (such as Amazon Alexa, Google Assistant)
• Medical diagnosis and monitoring (such as AI-assisted doctors in diagnosing diseases)

4. Training methods and learning ability

LLM trains through massive text: LLM mainly relies on a large amount of language data for training to learn the structure and patterns of human language, but it does not have real environmental perception ability and cannot understand the changes in the real world.

AI agents have reinforcement learning ability: In addition to using LLM as a language processing unit, AI agents will also learn environment changes through reinforcement learning or sensors and react according to different situations. For example, the autonomous driving AI agent will perform environment perception through camera, radar, and GPS data and adjust its driving strategies instantly.

5. Integration of action capabilities and tools

LLM lacks the ability to perform actions directly: While LLM can produce high-quality textual content, it itself has no control over external systems. For example, ChatGPT cannot send emails and manage files by itself, and must connect to other tools through the API.

AI agents can control applications and machines independently: AI agents can not only use LLM to understand languages, but also interact with external applications (such as Excel, CRM, and enterprise databases). , and even be able to control machines such as autonomous vehicles or robotic arms.

The rise of AI agents is closely related to several key technological breakthroughs in recent years, including cloud computing, big data analysis, reinforcement learning, etc. Major technology companies have also invested in the research and development and application of AI agent technology, such as:

• Google DeepMind - AlphaGo to AutoML AlphaGo, developed by Google's DeepMind in 2016, became the first AI agent in history to defeat a professional Go player in humans. now, Through AutoML technology, Google allows AI agents to independently learn and optimize machine learning models, allowing enterprises to deploy AI services faster.
• OpenAI - ChatGPT and Codex ChatGPT and Codex developed by OpenAI represent breakthroughs in AI agents in the fields of natural language processing and programming. These AI agents can understand the needs of users and provide corresponding responses or code generation, which are widely used in areas such as customer support, education assistance and software development.
• Tesla - Autonomous Driving AI Agent Tesla is the leader in the application of AI agent technology in automatic navigation of electric vehicles. Its Full Self-Driving (FSD) system uses AI agents to analyze road conditions, identify pedestrians, and adjust driving strategies. , enabling cars to perform autonomous driving without human intervention.
• Amazon - Alexa and the Smart Supply Chain Amazon develops Alexa Assistant through AI Agent Technology and will AI agents are used in their supply chain management systems to improve warehousing management and logistics distribution efficiency.
• Microsoft - Copilot AI Microsoft imports the AI ​​proxy "Copilot" in Office 365 and GitHub. Assist users to complete document writing, email processing and program development faster.

The core advantage of AI agents is to improve efficiency, reduce costs, and reduce human errors. It can automatically perform tedious tasks and provide more accurate results in data analysis, prediction, decision assistance, etc. Here are the main applications of AI agents in different fields:

1. Automate workflows to improve enterprise efficiency

• Automatically process emails and document sorting to improve the efficiency of administrative personnel.
• Automatic report generation, analyzing financial status or market trends based on enterprise data.
• Supply chain management, AI agents can predict demand and optimize logistics and transportation.

2. Financial and investment decisions

• High-frequency trading (HFT): AI agents automatically perform buying and selling operations through instant market analysis to improve the return on investment.
• Risk management: Monitor abnormal transactions and detect potential financial fraud.
• Personalized financial planning: AI agents provide personalized financial management advice based on users' consumption behavior and financial goals.

3. Medical and health management

• Medical image analysis: AI agents can analyze X-ray, MRI, CT scans and other images to assist doctors in early diagnosis of diseases.
• Intelligent health monitoring: Through wearable devices (such as Apple Watch), AI agents can monitor heart rate, blood pressure and other data to warning about health problems in advance.

4. Autonomous driving and smart transportation

• Environmental perception and path planning: analyze information such as pedestrians, traffic flow, traffic lights, etc. around the vehicle to determine the best driving route.
• Autonomous driving decisions: Make immediate responses based on weather, vehicle speed, and road conditions to improve driving safety.

5. Intelligent customer service and user experience

• AI chatbots: For example, Amazon Alexa, Google Assistant, provide instant responses and personalized suggestions.
• Intelligent recommendation system: Netflix and YouTube analyze user behavior through AI agents and recommend the most appropriate content.

The application of AI agents is constantly expanding, and in the future it will penetrate into all walks of life and become an indispensable digital assistant.

Despite the broad prospects for AI agents, they still face a number of technical and ethical challenges that must be solved in order to make AI agents more widely and securely applied.

1. Data Privacy and Security Risks

• Hacker attack: If the data accessed by AI proxy is hacked, it may lead to major privacy leakage.
• Enterprises abuse data: If an enterprise fails to properly handle the use of data from AI agents, it may infringe on user privacy.

2. AI bias and decision-making transparency

• Gender and Racial Bias in the Recruitment System: Amazon has suspended the program because the AI ​​recruitment system discriminates against female job seekers.
• Credit Score and Financial Decision: If the algorithms of AI agents lack transparency, it may affect fairness.

3. Regulatory and legal issues

• Responsibility for autonomous driving accidents: When an AI self-driving accident occurs, the responsibility should be borne by the car owner, manufacturer or AI agent?
• Copyright of AI-generated content: Who should own the smart property rights of pictures, articles, and music automatically generated by AI agents?

4. Impact of human-computer collaboration and employment

• Customer service personnel and data input personnel may be replaced by AI agents.
• The human-computer collaboration model needs to be redesigned to allow AI agents to assist humans rather than completely replace humans.

AI Agent Workflow refers to how AI Agents collaborate on multiple tasks to improve automation and decision-making efficiency. A typical process is as follows:

Perception phase

• Collect environmental data through cameras, sensors, voice recognition and other technologies.

Processing & Decision Making

• Use machine learning to analyze data and judge the best decisions, such as financial transactions or medical diagnosis.

Execution

• AI agents perform actions based on decisions, such as sending emails, conducting transactions, and autonomous driving operations.

Feedback & Learning

• AI agents continuously optimize their behavior through reinforcement learning to improve accuracy.

This workflow has been applied to finance, supply chain management, smart cities, drones and other fields, and may become a standard operating model in the future.

Although LLM and AI agents are technically different, the combination of the two will lead to more powerful applications. For example:

• Smart customer service system: AI agents can use LLM to conduct conversations, and automatically process orders, arrange after-sales service, etc. according to customer needs.
• Smart Medical Assistant: AI agents can interpret medical reports through LLM and provide personalized treatment advice based on patient health data.
• Enterprise Automation: AI agents can combine LLM to process email, scheduling, data analysis, etc. to improve work efficiency.

Future Outlook

The development of AI agents and LLM is still improving, and we may see:

• More autonomous AI agents can perform work independently based on the situation without human instructions.
• Stronger multimodal AI not only understands language, but also processes information such as images, audio, videos, etc., further improving decision-making capabilities.
• AI agents are combined with the Internet of Things (IoT) and are applied to smart homes, smart factories and other scenarios, allowing AI agents to directly control physical devices.

Although the AI ​​big language model and AI agents operate differently, they complement each other and jointly promote the development of artificial intelligence. In the future, AI will not only be able to "answer questions", but also "act independently", bringing a smarter life and work model to mankind.

There is no AI agent available for general users on the market, but you can try GenApe's AI Assistant Function, Customized AI assistants can meet your needs and help you save valuable time with simple settings. Customized AI assistants can provide personalized services based on your preferences and usage context. Whether writing a cover letter, a movie script, or planning a trip plan, making a recipe for cooking, or even replying to an email can do it for you.