Ejen AI telah meletup dalam beberapa tahun kebelakangan ini. Dan dengan teknologi dan keupayaan mereka yang kompleks, terdapat banyak jenis ejen AI hari ini.
Ejen AI adalah perisian yang melakukan tugas. Tidak seperti chatbot standard, ia boleh mengambil tindakan bagi pihak pengguna.
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. Ejen Refleks Mudah
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”
.webp)
2. Ejen refleks berasaskan model
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. Ejen Pembelajaran
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. Ejen Berasaskan Utiliti
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. Ejen Hierarki
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
Bina Ejen AI Tersuai
It's not difficult to build a customized AI agent - and you can do it for free.
Botpress menawarkan pembina aliran visual seret dan lepas, keselamatan gred perusahaan, perpustakaan pendidikan yang luas dan komuniti Discord aktif yang terdiri daripada 20,000+ pembina bot.
Platform kami yang boleh diperluaskan bermakna anda boleh membina mana-mana chatbot tersuai dengan mana-mana penyepaduan tersuai — dan Hub Integrasi kami penuh dengan penyambung pra-bina kepada saluran terbesar.
Mula membina hari ini. Ia percuma.
Soalan yang kerap ditanya
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.
Apakah ejen pintar, dan bagaimana mereka beroperasi dalam persekitaran digital?
Ejen pintar adalah entiti yang direka untuk bertindak dalam pelbagai persekitaran digital. Mereka mengumpulkan pengetahuan dari persekitaran mereka, menilai keadaan semasa, dan melaksanakan tindakan untuk mencapai matlamat yang telah ditetapkan. Prestasi mereka dipengaruhi oleh tindakan luaran yang mereka ambil dalam persekitaran yang dapat dilihat.
Bagaimanakah kecerdasan buatan memainkan peranan dalam fungsi ejen?
Kecerdasan Buatan memberi kuasa kepada ejen pintar dengan menyediakan mereka dengan keupayaan untuk belajar, menaakul, dan menyesuaikan diri. Ejen menggunakan AI untuk meningkatkan asas pengetahuan mereka, yang membolehkan membuat keputusan yang lebih canggih dalam pelbagai persekitaran.
Apa yang menjadi asas pengetahuan ejen pintar?
Pengetahuan tentang ejen pintar merangkumi maklumat mengenai alam sekitar, peraturan yang telah ditetapkan, dan pemahaman asas tentang keadaan semasa. Pengetahuan ini menjadi asas bagi proses membuat keputusan mereka.
Apakah elemen prestasi dalam konteks ejen pintar?
Elemen prestasi ejen pintar merujuk kepada keupayaan mereka untuk mencapai matlamat dan membuat keputusan yang mengoptimumkan tindakan mereka dalam persekitaran tertentu. Ia adalah komponen penting yang menentukan kecekapan dan keberkesanan ejen.
Bolehkah ejen beroperasi dalam struktur hierarki?
Ya, ejen hierarki adalah sejenis ejen pintar yang beroperasi dalam tahap berstruktur. Ejen peringkat tinggi mengawasi pengambilan keputusan umum, sementara ejen peringkat rendah mengendalikan tugas tertentu dalam rangka kerja yang lebih luas. Struktur hierarki ini membolehkan operasi yang cekap dalam persekitaran yang kompleks.
Adakah ejen pintar beroperasi dengan kecerdasan terhad?
Ya, banyak ejen pintar beroperasi dengan kecerdasan terhad, yang bermaksud mereka mempunyai skop pengetahuan dan keupayaan yang ditentukan. Batasan ini membantu mereka memberi tumpuan kepada tugas dan persekitaran tertentu di mana kepakaran mereka paling relevan.
Senarai Kandungan
Kongsi ini pada:
.svg)
.webp)
