“ The essence of science is to doubt everything ”
A few days ago, a comment asked me whether AI Agent and Agentic AI are the same and what the difference is; when I saw this question, I was a bit puzzled. I know what AI Agent is, but what is Agentic AI? This is the first time I’ve heard this term.
So in the past few days, I took some time during my breaks to look into the differences between AI Agent and Agentic AI, and I’ll simply record it here. My understanding may not be very accurate, but consider it a record of my learning.
The Differences and Connections Between AI Agent and Agentic AI
To be honest, I was quite confused when I first saw Agentic AI. It is so similar to AI Agent, and after researching and reading articles over the past two days, I found that this concept was proposed by a professor named Andrew Ng; the basic idea is that Agentic AI represents the future of artificial intelligence. Currently, a significant problem with artificial intelligence is that it lacks the ability to think independently and solve problems on its own; even the so-called AI Agent can only design different functions according to different application scenarios and cannot make artificial intelligence think and solve problems like a real human.
We all know that current large models are based on the concept of biomimicry, using neural network models to mimic the principles of human brain operation, and are a form of pseudo-intelligence realized through mathematical models based on probabilities and various disciplines. This means that the current large models are not true intelligent agents; they still have a long way to go to become real artificial intelligence. However, with the current technology, it remains the most likely method to achieve artificial intelligence.
If we start from the fundamental purpose of artificial intelligence, the goal of artificial intelligence is to achieve a system that truly possesses the ability to think independently and solve problems through some technology or method. How to make artificial intelligence possess human-like intelligence is the fundamental problem that artificial intelligence needs to solve.
Agentic AI belongs to a methodology or goal for achieving artificial intelligence that possesses independent thinking and problem-solving capabilities.
Although Agentic AI is a new concept, it is more about a higher-level abstraction of AI Agent; that is, it allows an agent that solves a specific problem to become generalizable, enabling it to solve more problems.
The development of technology is a process of continuous exploration, especially in these emerging fields, it is like crossing a river by feeling the stones; the proposal of the Agentic AI concept may prove to be correct in the future, but it may also be proven wrong; nonetheless, it is a thought and exploration of humanity towards achieving artificial intelligence.
So, now to answer the question, what is Agentic AI?
Agentic AI is an exploration in the field of artificial intelligence, a vision for achieving true artificial intelligence, and a concept; however, a concept is just a concept, and how to realize Agentic AI remains a question that all practitioners interested in artificial intelligence need to consider.
On the other hand, AI Agent is currently a proven methodology for enabling large models to possess preliminary independent thinking capabilities, and there are already specific implementation methods, meaning there are concrete landing scenarios.
Perhaps in the future, Agentic AI will be realized in other ways, or it may be realized through AI Agent.
Thus, Agentic AI is a goal; while AI Agent is a specific implementation plan.
Below is a response from chatGPT regarding the differences between Agentic AI and AI Agent, which I feel is quite accurate.
Finally, any lofty ideas must have practical implementations, let us strive together.
Agentic AI and AI Agent are related but have different emphases; here are their main differences:
Definitions and Emphases
| Characteristics | Agentic AI | AI Agent |
|---|---|---|
| Definition | Agentic AI describes the capability and behavior of artificial intelligence possessing autonomy. | AI Agent is a specific implementation form, referring to an artificial intelligence agent used to perform specific tasks. |
| Focus | Focuses on capabilities and characteristics, such as autonomous decision-making, goal orientation, and adaptability. | Focuses on entity form, typically an agent instance created to perform a certain task. |
| Scope of Action | A broader capability framework that can be applied to various scenarios and types of tasks. | A specific “tool” or “role” created to complete predefined tasks. |
Core Functions
Agentic AI
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More like a concept describing capabilities, emphasizing that AI systems can autonomously plan, decide, and execute tasks.
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Key Features:
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Autonomy: Can complete tasks without continuous intervention.
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Adaptability: Can respond to complex environments and adjust behavior.
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Goal Orientation: Actively achieves complex goals.
AI Agent
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Is a concrete implementation of Agentic AI, an independent program entity focused on completing specific tasks.
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Key Features:
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Task-Driven: Focused on predefined tasks.
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Environmental Interaction: Interacts with users or systems in real time.
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Modular Design: Typically a smaller, separately deployed component.
Practical Application Scenarios
Agentic AI
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Broad Framework, suitable for complex systems:
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Autonomous driving systems: Handle global planning and real-time changes in traffic conditions.
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Intelligent financial analysis: Dynamically analyze market data and autonomously adjust investment strategies.
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Martian exploration robots: Explore and make decisions autonomously under long-delay communications.
AI Agent
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Concrete intelligent entities, typically with single functions:
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Chatbots: Answer user questions.
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Voice assistants: Handle specific voice commands (e.g., checking the weather).
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Game AI: Interact as opponent characters in games.
Technical Implementation and Differences
| Characteristics | Agentic AI | AI Agent |
|---|---|---|
| Implementation Difficulty | Requires complex algorithm design, including long-term memory, learning, and reasoning capabilities. | Can be implemented on a smaller scale, using limited rules or strategies to complete tasks. |
| Scenario Complexity | Applied in dynamic, multi-task, and long-term environments. | More suitable for specific, short-term clear tasks. |
| Core Technology | Typically includes reinforcement learning, natural language processing, planning algorithms, etc. | Uses simple models or rules, possibly including supervised learning or logical rules. |
Relationships
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Agentic AI is a broader capability description, while AI Agent is an entity that implements this capability.
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Agentic AI can drive multiple AI Agents to work.
AI Agent is not necessarily Agentic AI:
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A simple chatbot can be an AI Agent, but it may lack the autonomy or adaptability of Agentic AI.
Agentic AI can build AI Agents:
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Systems with Agentic AI can generate multiple sub-agents to complete specific tasks.
Summary
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Agentic AI: More inclined to describe the capabilities of artificial intelligence, focusing on its autonomy, adaptability, and goal-oriented behavior.
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AI Agent: An application form of Agentic AI, manifested as agents with specific functions.
It can be understood that Agentic AI is a high-level capability, while AI Agent is a concrete manifestation of utilizing this capability to perform tasks.