近年來，人工智慧代理呈爆炸式增長。憑藉其複雜的技術和功能，如今有許多不同類型的 AI 代理。
AI 代理是一種執行任務的軟體。與標準聊天機器人不同，它可以代表使用者執行操作。
There's a wide range of AI agents, from smart thermometers and self-driving cars, to agents with chat interfaces. All of these use cases fall into one of the seven main categories of AI agents. In this article, I'll share the 7 main types of AI agent and some real-world examples of AI agents.
1. 簡單反射劑
A simple reflex agent is an AI system that makes decisions based only on the current input from its environment.
It uses a set of condition-action rules to map observed inputs to specific responses. When it detects a certain state in the environment, it executes the corresponding rule.
It has no memory or internal model of the world — so it can only operate effectively in fully observable environments where every decision can be made based on the current input alone.
Examples of Simple Reflex Agents
- A thermostat that turns on the heat if it’s too cold
- A robot that turns when it hits a wall (hello, Roomba with a cat on top)
- A basic chatbot that replies “Hello!” when a user says “Hi”
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2. 基於模型的反射代理
A model-based reflex agent is an AI agent that makes decisions based on both the current input and an internal model of the world.
Unlike simple reflex agents, this type keeps track of the environment’s state over time. It uses a model — essentially, stored information about how the world works — to fill in gaps when the environment isn’t fully observable.
When it receives a new input, it updates its internal state, consults its condition-action rules, and chooses the best response based on both the current percept and what it knows from previous interactions.
Examples of Model-Based Reflex Agents
- A robot vacuum that remembers the layout of a room and avoids areas it has already cleaned
- An LLM agent that continues a conversation while keeping track of past user inputs
- A game AI that reacts not only to what it sees but also to what it knows from earlier in the match
3. 學習代理
A learning agent is an AI agent that improves its performance over time by learning from its experiences.
It has four main components: a learning element, a performance element, a critic, and a problem generator.
The performance element chooses actions, while the learning element adjusts its behavior based on feedback. The critic evaluates the outcome of actions using a predefined standard, and the problem generator suggests new actions to try for better learning.
This structure allows the agent to adapt to changes, refine strategies, and operate effectively even in unfamiliar environments.
Examples of Learning Agents
- A crypto AI agent that adjusts trading strategies based on market performance
- A recommendation engine that gets better at suggesting products based on user behavior
- A healthcare chatbot that learns from patient interactions to improve triage accuracy
4. 基於實用程式的代理
A utility-based agent is an AI agent that chooses actions based on which outcome is expected to provide the highest overall value or “utility.”
Rather than just aiming to achieve a goal, this agent evaluates different possible outcomes and selects the one that maximizes a predefined utility function.
This allows it to handle situations where there are multiple ways to reach a goal, or where trade-offs must be made. It requires the ability to compare options, predict consequences, and rank outcomes based on preferences or priorities.
Examples of Utility-Based Agents
- A chatbot for sales that prioritizes leads based on likelihood to convert
- A stock trading bot that balances risk and return to maximize long-term gains
- A business chatbot that schedules meetings to minimize conflicts and maximize convenience
5. 分層代理
A hierarchical agent is an AI agent that organizes its decision-making process into multiple layers or levels, with higher levels handling abstract goals and lower levels managing specific actions.
This agent breaks complex tasks into smaller sub-tasks, with each level of the hierarchy responsible for a different scope of decision-making.
High-level layers may plan long-term strategies, while lower layers handle immediate sensor data and real-time responses. Communication flows between layers, allowing the agent to coordinate broad objectives with detailed execution.
This structure makes it easier to manage complexity and scale behavior across different time frames or priorities.
Examples of Hierarchical Agents
- In manufacturing, a high-level agent plans the assembly process while lower levels control robotic arms and timing
- In a smart factory, different layers manage production schedules, machine coordination, and physical operations
6. Goal-Based Agents
A goal-based agent is an AI agent that makes decisions by evaluating which actions will help it achieve a specific goal.
The agent is given one or more goals — desired outcomes it wants to reach. It uses search or planning algorithms to explore possible sequences of actions, then selects the ones that are most likely to lead to the goal.
Unlike reflex agents, it doesn't just react — it reasons about future consequences before acting. This makes it more flexible and capable in dynamic or unfamiliar environments, but also more computationally demanding.
Examples of Goal-Based Agents
- A navigation system that calculates the best route to a destination
- A puzzle-solving AI that searches for moves that will lead to a completed puzzle
- A robotic arm that plans a sequence of motions to successfully assemble a product
7. Multi-Agent Systems (MAS)
Last but not least: the multi-agent system.
A multi-agent system (MAS) is a system composed of multiple interacting AI agents that work together (or sometimes compete) to accomplish individual or shared objectives.
Each agent in the system operates independently, with its own capabilities, goals, and perception of the environment.
These agents communicate and coordinate — either directly through messages or indirectly by observing changes in the environment. The system as a whole can solve problems that are too complex or distributed for a single agent to handle.
Multi-agent systems can be cooperative, competitive, or a mix of both, depending on the design and goals.
Examples of Multi-Agent Systems
- Autonomous vehicles coordinating at an intersection to avoid collisions
- A set of finance bots manages invoicing, fraud detection, and reporting through AI workflow automation
- A supply chain system where different agents manage inventory, shipping, and demand forecasting
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常見問題
Is ChatGPT an AI agent?
Yes, ChatGPT can be considered an AI agent — it receives input, processes it, and generates responses, often using a goal or utility-driven approach depending on how it’s deployed.
What are the 7 types of AI agent?
The 7 types are: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, learning agents, hierarchical agents, and multi-agent systems.
什麼是智慧代理，它們如何在數字環境中運行？
智慧代理是設計用於在各種數位環境中運行的實體。他們從周圍環境中收集知識，評估當前情況，並採取行動以實現預定的目標。它們的性能受它們在可觀察環境中採取的外部操作的影響。
人工智慧如何在代理功能中發揮作用？
人工智慧通過為智慧代理提供學習、推理和適應的能力來增強智慧代理的能力。代理利用人工智慧來增強他們的知識庫，允許在各種環境中做出更複雜的決策。
智慧代理的知識庫由什麼構成？
智慧代理的知識包括有關環境的資訊、預定義的規則以及對當前情況的基本理解。這些知識構成了他們決策過程的基礎。
智慧代理上下文中的性能元素是什麼？
智慧代理的性能要素是指它們在給定環境中實現目標和做出優化其操作的決策的能力。它是決定代理效率和有效性的關鍵組成部分。
代理可以在分層結構中運行嗎？
是的，分層代理是一種在結構化級別中運行的智慧代理。高級代理負責監督一般決策，而較低級別的代理則在更廣泛的框架內處理特定任務。這種分層結構可在複雜環境中實現高效運行。
智慧代理是否在有限的智慧下運行？
是的，許多智慧代理在有限的智慧下運行，這意味著它們具有明確的知識和能力範圍。這種限制有助於他們專注於與其專業知識最相關的特定任務和環境。
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