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AI Agents vs. Agentic AI: Clarifying the Differences
Hello. Recently, words like "AI Agents" or "Agentic AI" have started appearing here and there. While thinking "they probably mean almost the same thing," I looked into it and found that they seem to have slightly different nuances. That said, definitions are ambiguous and they tend to be used interchangeably, so the choice of terms often depends on the sender's intention or perspective.
In this article, I've organized the differences between AI Agents and Agentic AI, partly to clarify the terminology for myself. This is based on my own research, so I'd appreciate it if you read it with the sense of "I see, there's this way of looking at it too."
Comparing AI Agents and Agentic AI with Analogies
First, to make it easier to understand, I've replaced them with everyday images.
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AI Agent = "A Secretary"
If you ask them to "book a meeting," they will check the necessary information, reserve a room, and send invitation emails to participants. It's an image of providing support strictly within the scope of the given instructions. -
Agentic AI = "An Autonomous Manager"
When you tell them "I want to move a project forward," they think of the necessary steps themselves, identify tasks, sequence them, contact stakeholders, and sometimes create materials on their own initiative. Instead of just executing commands, they think about how to achieve the goal and move based on their own judgment.
Does that give you a general sense? An "AI Agent" is like a secretary performing assigned tasks, while "Agentic AI" is like a manager capable of higher-level autonomy and decision-making.
First, the Basic Image of AI Agents (Agent AI)
Definitions and Examples
An AI Agent is a software entity designed to perform tasks on behalf of a user or other systems. It is a general term for so-called "autonomously acting AI programs" and includes a wide range of things, from chatbots to home virtual assistants (Siri, Alexa, etc.).
- Chatbots: AI that automatically answers Q&A prepared in advance for customer support.
- Voice Assistants: Like Alexa or Siri, which answer questions or operate home appliances according to voice instructions.
What I found characteristic here is that in many cases, they "operate according to data or rules prepared by humans." They are good at efficiently handling determined tasks like "I want you to sort emails based on certain conditions" or "I want you to find and return an answer from the FAQ," but flexibility beyond that is not really expected. My impression is that "automation within a fixed range" is the main focus.
How is Agentic AI Different?
Advanced Autonomy and Decision-Making Capabilities are Key
On the other hand, what is called Agentic AI seems to be a more advanced concept. The keywords are "Autonomy" and "Decision-making." This refers to AI systems that don't just wait for human instructions but set tasks themselves as needed and change their actions while learning flexibly according to the situation.
- Self-driving cars: Recognize the surrounding environment without human operation and autonomously make route and safety judgments.
- High-frequency trading systems: Instantly analyze market conditions and automatically determine which stocks to buy or sell.
In this way, the important part of Agentic AI is that it doesn't just "carry out commands" but thinks about "how it should move" on its own. It might be close to the image of "the AI becoming a single entity."
Organizing the Differences
Here is a table summarizing my understanding.
| Feature | AI Agent | Agentic AI |
|---|---|---|
| Main Role | Executes tasks on behalf of someone | Acts by making its own decisions |
| Autonomy | Limited (Follows defined instructions) | High (Thinks and moves on its own) |
| Adaptability | Handles fixed task scopes | Adapts while learning from new situations |
| Decision-making | Based on rules or programs | Judges by evaluating and analyzing the situation |
| Proactivity | Waits for requests from the user | Explores problems and solutions on its own |
| Data Requirements | Based on data previously organized by humans | Retrieves necessary information on its own |
Broadly speaking, Agentic AI is an AI that goes one step further than an "AI Agent" and can plan and move on its own. It's not that one is particularly superior to the other; rather, it's about using them according to the problem you want to solve.
What About from a Mechanism Perspective?
AI Agents (Conventional)
- A simple loop structure of "Perception → Judgment → Action"
- The judgment part is relatively limited, such as rule-based or machine learning models
- Weak against unexpected situations
Agentic AI
- A complex cycle of "Perceive → Reason → Act → Learn"
- Incorporates powerful AI models like Large Language Models (LLMs) and achieves results by accessing external tools and data
- Constantly improves by incorporating feedback from execution results
In short, Agentic AI can handle advanced interactions like "calling external tools by itself" and "determining the next move based on the results." Just as we humans use trial and error and association, the key is that the AI also flexibly adjusts its tasks to get closer to the goal.
Positioning in the Evolution of AI
Market analysis firms (such as Gartner) reportedly categorize this evolution of AI into "Everyday AI (AI that automates daily tasks)" and "Game-changing AI (AI that creates new value)." While these are not strictly separated, Agentic AI is clearly positioned toward the latter. It holds the potential to fundamentally change things that were difficult with previous systems, such as "autonomous driving" and "research support in drug discovery."
Over the next decade or so, AI is expected to become increasingly integrated into business processes. The practical implementation of Agentic AI could significantly transform our work styles and daily lives.
Various Frameworks and Services
Recently, various development frameworks and services for realizing this Agentic AI have been increasing. In particular, the following five seem to be actively developed and provided by major companies and communities.
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LangGraph
An agent building platform provided by LangChain. It is said to be a framework designed with deployment in actual services in mind, featuring systems for long-term memory and scaling out.
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AutoGen/Semantic Kernel
Frameworks released as open source by Microsoft's research department. A key feature is "collaboration," where multiple agents work together to progress tasks.
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crewAI
The system of forming a "crew" (multiple agents) where each has a role and works as a team is interesting. It seems worth playing around with for brainstorming or distributing complex tasks.
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OpenAI Agents SDK
OpenAI's official lightweight SDK. It allows for flexible agent creation by combining minimal primitives (Agent, Handoff, Guardrails). It looks easy to use for prototyping.
Article from when I tried it:
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Azure AI Agent Service
A platform that provides Agentic AI as a managed service on Microsoft Azure. It's convenient because tools like code execution environments and web search are provided as standard.
Article from when I tried it:
All of them still seem to be in the development stage, but they are expected to become even more comprehensive in the future. If you are interested, trying them out might allow you to experience the potential of Agentic AI.
Why Agentic AI is Drawing Attention
Lastly, I have summarized why "Agentic AI" has been gaining so much attention over the past year or two in my own way.
- Emergence of Large Language Models (LLMs): Models like GPT-4o have begun to achieve not only text understanding and generation but also flexibility in reasoning.
- Research on techniques like ReAct: Prompt designs were conceived where LLMs create their own action plans and call tools.
- The buzz around Auto-GPT and BabyAGI: These were released as open source in a form that anyone could try, which had a significant impact.
In short, the expectation that "we might be able to create an AI that doesn't just understand natural language like ChatGPT, but also thinks and acts on its own from there" is what's fueling the Agentic AI boom.
Summary
In conclusion, my impression is that AI Agents and Agentic AI are similar yet different.
- AI Agent: Automates predefined tasks or a fixed range.
- Agentic AI: The AI thinks of tasks and procedures itself, and continues to learn and improve while observing the results.
It's not that one is necessarily superior to the other; rather, they should be used based on the problem to be solved and the degree of flexibility required. A simple FAQ chatbot is well-suited for an AI Agent, whereas Agentic AI-like technology is essential if you want to autonomously handle complex tasks involving multiple steps.
In any case, the trend of these AIs increasingly blending into our work and lives seems certain. Personally, seeing AI evolve from a mere "tool" to something like a "partner" is quite exciting. I'll be keeping a close eye on future developments.
Disclaimer
This article is a memo summarizing information I've gathered during my own learning process. As technical and historical backgrounds are constantly evolving, please check official websites and community resources when actually implementing these technologies. The accuracy and completeness of the content are not guaranteed, and the author assumes no responsibility for any damages (including service interruption, data loss, business loss, etc.) resulting from the use of the information in this article.
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