With the rapid advancement of technology and the AI storm sweeping the world, it is expected that AI Agent will become the next goal pursued by enterprises. AI Agent, also known as "AI agent", is an artificial intelligence system that can perform tasks autonomously. It has core functions such as planning goals, using tools to perceive environmental information, and memorizing and using past interaction information to improve performance.
What is AI Agent? How is it different from general traditional AI?
Compared with traditional AI, which usually requires inputting instructions to complete specific tasks, AI Agent can combine a variety of technologies, including large language models (LLM), tool calls, planning and reasoning frameworks, etc., to make decisions independently, dynamically adapt to user needs, and Execute complex workflows.
The core features of AI Agent include planning capabilities. It can break down tasks according to the user's goals, formulate plans and execute them step by step; it can also supplement knowledge through functions and tools such as search and calculation; it can also process information from the surrounding environment and store past events. interactive process and exploit it to improve subsequent performance.
These capabilities allow AI Agents to not only handle basic administrative tasks, but can even be used in travel planning, medical diagnosis, emergency response and other situations, becoming an efficient "digital colleague" or "digital companion".
The core features of AI Agent include planning capabilities, tool integration, perception and memory.
How does AI Agent perform tasks? See how AI Agent works from 3 major work stages
Phase One: Goal Setting and Planning
Specifically, how does the AI Agent complete its task? It can be mainly divided into 3 major work stages:
The first stage is Goal initialization and planning. Although the AI Agent is autonomous, it still needs humans to set clear goals and execution environments at the beginning. For relatively complex tasks, AI Agent will break it down into many small tasks and formulate specific action plans; for tasks with simpler goals, AI Agent can directly reflect and improve without prior planning. Finish.
- Developer: Design and train AI Agent systems.
- Deployer: Provides users with permissions to use AI Agent.
- User: Set specific goals that the AI Agent needs to accomplish, and provide usable tools and resources.
For example, suppose the user wants to plan a surfing trip to Greece and wants the AI Agent to help find the best weeks for surfing in 2025. AI Agent may perform tasks like this:
- Collecting information: The AI Agent needs to collect weather information in Greece, such as tides, rainfall, sunshine, etc.
- Determine surfing conditions: The AI Agent needs to know which weather conditions are suitable for surfing.
- Analyze data: The AI Agent needs to analyze the collected weather information and predict which week will be most suitable for surfing next year.
Phase 2: Reasoning using available tools
However, AI Agents don't know everything. During the execution of tasks, the AI Agent may encounter a situation where its own knowledge base is insufficient. At this time, the AI Agent will use external tools to supplement its knowledge.
Therefore, the second stage is "Reasoning using available tools". AI Agents will use external tools such as databases, APIs, and other agents to supplement their knowledge. Update your own knowledge base based on information provided by external tools, and adjust plans and self-corrections to improve subsequent performance. At this stage, multiple rounds of inquiry and interaction will be used to integrate newly acquired information to solve the problem.
- Database: AI Agent can query relevant information from the database, such as product information, customer information, historical data, etc.
- API: AI Agent can call other services through API, such as weather forecast, map navigation, translation, etc.
- Other agents: Just like humans cooperate with experts in other fields, AI Agents can cooperate with other AI Agents that specialize in specific fields to solve problems together. For example, the AI Agent responsible for planning travel can cooperate with the AI Agent responsible for booking air tickets and hotels to provide more complete services.
The third stage: learning and reflection
The last stage is learning and reflection. The AI Agent will use the reflection mechanism to integrate feedback from other AI Agents or humans to further improve the accuracy of goals and actions. Users can provide ratings or opinions on the products or services recommended by the AI Agent to help the AI Agent learn and improve.
In response to the feedback received, the AI Agent will reflect on its decision-making process, analyze the reasons, identify areas for improvement, and adjust subsequent execution strategies to avoid making the same mistakes again. AI Agent has iterative refinement and long-term learning capabilities, and can store solutions in the knowledge base.
Taking the aforementioned surfing trip to Greece as an example, in the third stage, the AI Agent may reflect and improve like this:
- Collect user feedback: AI Agent can ask users whether they are satisfied with the recommended itinerary and collect users' opinions and suggestions on the itinerary.
- Analyze feedback information: AI Agent analyzes user feedback to understand what areas need improvement. For example, users may feel that the itinerary is too tight, or they may wish to add a cultural experience to the itinerary.
- Adjust the itinerary plan: AI Agent adjusts the itinerary plan based on user feedback. For example, you can make your itinerary more relaxed, or add cultural experience attractions such as museums and monuments.
- Update the knowledge base: AI Agent will store the adjusted itinerary plan and user feedback information in the knowledge base, which will be used for reference when planning similar itineraries in the future.
The AI Agent work stage includes setting goals and planning, executing tasks, learning and reflection.
Is the AI Agent smart? What's so great about it? 5 major categories of AI Agents at once
According to functions and intelligence, AI Agents can be divided into 5 main types:
Simple Reflex Agents
It can only trigger fixed actions based on the currently perceived environmental conditions. It has no memory function and cannot handle unknown situations. It is suitable for fully observable and simple-state environments, relying on preset rules to trigger specific actions under specific conditions. Common uses include smart thermostats that automatically turn on the heating system at a set time.
Model-Based Reflex Agents
Previous perceptual results can be remembered and internal models built. The AI Agent's actions rely on internal model updates, preset conditional responses, and current status. More resilient than basic reactive agents, able to operate in partially observable environments. For example, a sweeping robot can adjust its actions based on the room layout model in memory to avoid repeated cleaning; self-driving cars can build a model of the surrounding environment and make driving decisions based on road condition information collected by sensors.
Goal-Based Agents
As the name suggests, goal-oriented agents have the concept of goals, can choose an appropriate sequence of actions based on the goals, and find the best way to achieve the goals through planning. Actions are more flexible and more suitable for situations that require multiple steps to achieve the goals. For example, general navigation systems can recommend the fastest route to the destination based on current traffic conditions.
Utility-Based Agents
The AI Agent of the efficiency agent not only aims to achieve the goal, but also calculates the best solution through the efficiency function (utility function). Efficient agents will consider factors such as benefits, costs, or completion time to select the action with the greatest utility, and are suitable for situations where it is necessary to choose the best option among multiple feasible options. For example, the advanced navigation system can calculate the optimal route based on factors such as fuel consumption, time and tolls; the resource allocation system can determine the best resource allocation method based on resource efficiency and cost.
Learning Agents
The learning agent has a learning mechanism that can improve performance from experience. It mainly consists of four components: learning, commenting, execution and problem generation. It can adapt to "unknown environments" and continuously improve its capabilities. For example, e-commerce recommendation systems can learn from user behavior to provide more accurate product recommendations; spam filters can learn from email characteristics to distinguish spam from normal emails.
An AI Agent can have multiple characteristics at the same time! What is a "multi-intelligent agent system"?
Different types of AI Agents are suitable for different situations. To briefly summarize, the basic reactive type is suitable for fully observable environments, while the efficiency and learning types are better able to handle complex or dynamic problems, while the learning agent is the most advanced type, with the ability to learn from the environment and continuously improve, especially Suitable for personalized services, such as recommendation systems.
It is worth noting that these types are not mutually exclusive, and an AI Agent can have multiple types of characteristics at the same time. For example, a navigation system can be a goal-oriented agent, or it can also use the logic of an efficient agent to calculate the best path; a self-driving car can be a model-based inverse agent, and also have the ability of a learning agent to continuously Optimize your driving strategy.
In addition, the new type of AI system "Multi-Agent Systems" composed of multiple AI Agents has further improved the performance of AI Agents. Through the interaction and collaboration of multiple independent agents, it can solve the problem of single AI Large-scale or complex problems that Agent cannot handle.
The multi-intelligent agent system has the characteristics of decentralization, autonomy, goal-oriented and interactive, and is suitable for situations that require high reliability and large-scale solutions. It also has the ability to dynamic reasoning, deal with complex problems and Enhanced memory and other features. For example, in the medical field, AI Agents can assist in formulating treatment plans or managing medication processes; and in natural disaster emergencies, they can quickly locate victims and provide rescue suggestions.
In the next 5 to 10 years, AI Agents will be able to complete 80% of people's daily tasks! What is the value of human beings?
Nancy Xu, founder and CEO of Moonhub, an artificial intelligence recruitment company, pointed out at the World Economic Forum that in the next 5 to 10 years, AI Agent will be able to complete 80% of people's daily work time, profoundly changing the way humans work and live.
Just imagine, when AI Agents can complete most daily tasks, what will the value of human beings be? The rise of AI Agents also forces people to redefine "what is unique human labor." The collaboration between AI and humans will further promote the "AI talent revolution". Workers need to learn more new skills to adapt to this change. The overall social structure and system also need to adapt to these changes to provide people with new opportunities and support.
However, the rapid development of AI agents also comes with risks. For example, AI Agent needs to use a large amount of data. How to ensure that the data is not abused, is safe and compliant is a major focus. In addition, many repetitive jobs may disappear, which will also lead to unemployment problems. When introducing AI Agents into high-risk situations such as medical and financial situations, how to strike a balance between AI autonomy and human supervision is also crucial.
Source: IBM, Microsoft, World Economic Forum
