近年、AIエージェントは爆発的に増加している。そして、その複雑なテクノロジーと能力により、最近では様々なタイプのAIエージェントが存在する。
AIエージェントはタスクを実行するソフトウェアである。標準的なチャットボットとは異なり、ユーザーの代わりにアクションを起こすことができます。
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.単純反射剤
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.モデルベース反射エージェント
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.学習エージェント
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.ユーティリティ・ベースのエージェント
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.階層エージェント
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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よくある質問
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.
インテリジェント・エージェントとは何なのか？
インテリジェント・エージェントは、様々なデジタル環境で行動するように設計されたエンティティである。周囲の環境から知識を収集し、現在の状況を評価し、事前に定義された目標を達成するために行動を実行する。その性能は、観測可能な環境内で彼らが取る外部行動によって影響される。
人工知能はエージェントの機能においてどのような役割を果たしているのか？
人工知能は、学習、推論、適応の能力を提供することで、知的エージェントに力を与える。エージェントはAIを活用して知識ベースを強化し、さまざまな環境においてより高度な意思決定を行えるようにする。
知的エージェントの知識ベースを構成するものは何か？
インテリジェント・エージェントの知識には、環境に関する情報、あらかじめ定義されたルール、現在の状況に対する基本的な理解が含まれる。この知識が意思決定プロセスの基礎となる。
インテリジェント・エージェントの文脈におけるパフォーマンス要素とは？
知的エージェントのパフォーマンス要素とは、与えられた環境において目標を達成し、行動を最適化する決定を下す能力のことである。エージェントの効率と有効性を決定する重要な要素である。
エージェントは階層構造の中で活動できるのか？
そう、階層型エージェントは、構造化されたレベルで動作する知的エージェントの一種である。上位エージェントは一般的な意思決定を監督し、下位エージェントはより広い枠組みの中で特定のタスクを処理する。この階層構造により、複雑な環境でも効率的な操作が可能になります。
知的エージェントは限られた知能で動くのか？
そう、多くのインテリジェント・エージェントは、限定されたインテリジェンスで動作する。この制限により、エージェントは専門知識が最も必要とされる特定のタスクや環境に集中することができます。
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