Agen AI telah meledak dalam beberapa tahun terakhir. Dan dengan teknologi dan kemampuannya yang kompleks, ada banyak jenis agen AI yang berbeda akhir-akhir ini.
Agen AI adalah perangkat lunak yang melakukan tugas. Tidak seperti chatbot standar, agen AI dapat mengambil tindakan atas nama 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. Agen Refleks Sederhana
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. Agen Refleks Berbasis 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. Agen 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. Agen Berbasis Utilitas
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. Agen Hirarkis
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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Pertanyaan yang Sering Diajukan
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.
Apa yang dimaksud dengan agen cerdas, dan bagaimana mereka beroperasi di lingkungan digital?
Agen cerdas adalah entitas yang dirancang untuk bertindak dalam berbagai lingkungan digital. Mereka mengumpulkan pengetahuan dari lingkungan mereka, menilai situasi saat ini, dan menjalankan tindakan untuk mencapai tujuan yang telah ditentukan. Kinerja mereka dipengaruhi oleh tindakan eksternal yang mereka lakukan dalam lingkungan yang dapat diamati.
Bagaimana kecerdasan buatan berperan dalam fungsi agen?
Kecerdasan Buatan memberdayakan agen cerdas dengan memberi mereka kemampuan untuk belajar, bernalar, dan beradaptasi. Agen menggunakan AI untuk meningkatkan basis pengetahuan mereka, sehingga memungkinkan pengambilan keputusan yang lebih canggih di berbagai lingkungan.
Apa yang menjadi dasar pengetahuan dari agen cerdas?
Pengetahuan agen cerdas mencakup informasi tentang lingkungan, aturan yang telah ditetapkan, dan pemahaman mendasar tentang situasi saat ini. Pengetahuan ini menjadi dasar bagi proses pengambilan keputusan mereka.
Apa elemen kinerja dalam konteks agen cerdas?
Elemen kinerja agen cerdas mengacu pada kemampuan mereka untuk mencapai tujuan dan membuat keputusan yang mengoptimalkan tindakan mereka dalam lingkungan tertentu. Ini adalah komponen penting yang menentukan efisiensi dan efektivitas agen.
Dapatkah agen beroperasi dalam struktur hirarkis?
Ya, agen hirarkis adalah jenis agen cerdas yang beroperasi dalam tingkatan terstruktur. Agen tingkat tinggi mengawasi pengambilan keputusan secara umum, sementara agen tingkat rendah menangani tugas-tugas spesifik dalam kerangka kerja yang lebih luas. Struktur hirarkis ini memungkinkan operasi yang efisien dalam lingkungan yang kompleks.
Apakah agen intelijen beroperasi dengan kecerdasan yang terbatas?
Ya, banyak agen intelijen beroperasi dengan kecerdasan yang terbatas, yang berarti mereka memiliki cakupan pengetahuan dan kemampuan yang ditentukan. Keterbatasan ini membantu mereka fokus pada tugas dan lingkungan tertentu di mana keahlian mereka paling relevan.
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