The Beginning of Agents: Shaping a “Usable Brain”
Just a few years ago, artificial intelligence was still largely seen as a “mathematical statistical model” or an “automated classifier.” But in 2017, Google’s introduction of the Transformer architecture marked a revolutionary turning point in the field of natural language processing. Building on this foundation, the emergence of Large Language Models (LLMs) fundamentally transformed how AI understands and uses human language.
This shift went beyond simple model upgrades. Through massive data training, structural improvements, and feedback-based tuning, LLMs evolved into something closer to a “usable brain.” Initially, LLMs were mere tools to solve individual functions or specific tasks. Now, they have become “generative AI” capable of maintaining natural conversations, much like humans. A key milestone in this journey was the rise of the Chat Completion Task, signaling the moment when AI gained the ability to engage in context-aware dialogue rather than simply reacting based on rules. This was, in essence, the beginning of a functional AI agent brain.
The Difference Between Chatbots and Agents
When people hear the term “AI,” they often think of chatbots. This is partly because ChatGPT, the world’s most famous AI service, is presented in chatbot form, and partly because chat interfaces are already familiar to users through social media and messaging apps. Therefore, it was a natural progression for advanced LLMs to be deployed as chat applications.
However, this leads to a common misunderstanding. The term “chatbot” only describes a user interface. It does not define an agent itself. Today, many equate chatbot = AI = agent, but this equation severely underestimates the true potential of AI agents. A chatbot is merely an external form. An agent is an intelligent entity operating within that form. It is time to clearly distinguish these concepts and focus on the real value an agent provides.
What Is an AI Agent?
There is no universally accepted definition of an AI agent in academia or industry. However, several major companies share similar descriptions.
Google: “AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users.”
AWS: “An AI agent is a software program that can interact with its environment, collect data, and use the data to perform self-determined tasks to meet predetermined goals.”
From these and other definitions, we can summarize an AI agent as follows.
“A software entity that autonomously acts and interacts with external environments as an independent system.”
Key Characteristics of an Agent
Autonomous – The ability to make judgments and decisions without human intervention
Traditional programs operated based on predefined rules or required user confirmation at every step
This caused challenges in handling exceptions and limited applicability
Delays from waiting for user confirmations or errors caused by incorrect approvals were common
An autonomous agent perceives, plans, executes, and even self-adjusts without human input, achieving the ultimate form of automation
We can delegate missions to such agents and simply receive results
Independent – Functions as a self-contained system, not dependent on specific platforms
Unlike conventional applications tied to an operating system, an agent operates independently and controls systems by supporting various interfaces
This enables it to run on an agent-native OS without external dependencies
Interactive – Communicates and responds organically with tools, people, other agents, and environments
To complete missions or tasks, interaction with external resources is essential
An agent must be capable of understanding and utilizing external protocols and interfaces
Anatomy of an Agent: Mirroring the Human Structure
AI agents are structurally designed to resemble the human cognitive system. Each element corresponds to parts of the human brain, personality, hands, and sensory organs, enabling them to actively interact with their environment.
LLM (Brain): The thinking organ of the agent. It processes language, solves problems, and sustains dialogue.
Persona (Character): Defines personality and roles. It influences tone and interaction style, whether as an advisor, teacher, or assistant.
Skill (Hands): Functions that perform actions. Executes necessary operations for specific goals.
Short/Long-term Memory (Hippocampus and Prefrontal Cortex): Short-term memory maintains conversation context, while long-term memory enables personalized services based on user preferences and history.
Interface (Sense Organs): Contact points with the external world. Gathers inputs through APIs, the web, sensors, and more.
Example 1: Customer Support Agent
Key Components
LLM: Understands and responds naturally to customer inquiries
Memory and Context: Retains prior interactions and customer information
Persona: Maintains brand-consistent tone
This agent must resolve issues while understanding user context, maintaining a polite and consistent manner, and complying with privacy regulations.
Example 2: Product Quality Inspection Agent
Key Components
Tools: Interfaces with machines, sensors, and MES
Environment I/F: Collects and reacts to real-time data
This agent demands high responsiveness and logical precision, as even minor errors could lead to significant costs.
Agent-Native Ecosystem
AI agents are no longer just software programs. They are independent, interactive digital entities that autonomously pursue goals and communicate with humans or other agents.
For this reason, the traditional centralized platform structures underpinning web applications, mobile apps, and cloud services cannot fully realize the potential of AI agents.
To unlock their true value, an agent-native ecosystem is needed. Several core infrastructures and protocols are emerging to support this.
Communication Protocol: Rules for Agent Interaction
Agents are not isolated. They exist as connected entities in a network. They constantly exchange data and collaborate not only with users but also with other agents and external systems. A communication protocol is essential for standardizing these interactions.
A notable example is Google’s A2A (Agent-to-Agent) Protocol, which facilitates secure message exchanges and collaborative processes among agents with different roles and functions. For instance, a shopping agent collaborating with a payment agent or a data analysis agent sharing insights with a research agent operates on this protocol.
The A2A Protocol is to agents what language is to human communication, serving as the foundational communication rule for a digital society.
Resource Access Interface: The Bridge to External Resources
Agents do not possess all functionalities and data internally. They need to access databases, APIs, and external applications as needed. A representative protocol enabling this is MCP (Model Context Protocol).
MCP defines an integrated interface that allows agents to autonomously discover, request, and utilize necessary functions or data based on their current context. It helps an agent understand “what situation it is in and what resources it needs” to act accordingly.
For example, a travel planning agent might use MCP to search flight booking APIs, hotel reservation APIs, and currency databases to generate personalized itineraries.
Protocols like MCP are essential tools that empower agents to maximize their functional capabilities and utility.
Decentralized Network: A Distributed Foundation for Trust and Collaboration
For independently operating agents to collaborate in a trustworthy environment, a decentralized network is necessary, rather than a centralized server system.
Given the diverse origins of agents from various providers, it is impractical to supply them under a single centralized authority. This is the purpose of the HabiliAI Network, a network designed to ensure transparent and verifiable environments for agent activities and interactions.
Through this, agents can collaborate freely in a trust-based network, proving their reliability to users and other agents while participating in trust-driven economic activities.
Agent-Native OS: An Operating System for Agents
Existing operating systems were designed around users, apps, and devices. To support the autonomy and scalability of AI agents, an agent-native OS is required.
This OS enables agents to manage decision-making, function execution, network participation, and resource management independently. It could also extend to robotics, integrating physical interfaces like robotic arms, sensors, and cameras as part of the software agent.
While no standard solution exists yet, it is an emerging frontier technology. An agent-native OS will serve as the foundational platform enabling AI agents to expand their activities beyond the digital realm into the physical world.
The Arrival of the AI Agent Era: A New Value Paradigm
AI agents are evolving into collaborators that create new dimensions of value beyond human reach. They break down the barriers between the virtual and real worlds, enabling unprecedented business opportunities and social innovations by organically connecting the two realms.
AI agents are becoming platforms and companions that help humans overcome physical and mental limitations and explore infinite possibilities.
AI Agents: Partners Beyond Human Limits
The role we envision for AI agents is not simply to replace labor. AI agents move beyond repetitive, manual tasks to become partners in creative and complex problem-solving. This shift allows humans to focus more on higher-level thinking, planning, and emotional activities.
Through agents, humans gradually delegate their labor. Beyond decision support, AI agents negotiate, coordinate, create, and resolve issues on behalf of humans. Constraints of time and space in the real world are overcome through AI agents, opening doors to more productive and economic opportunities.
For example, an entrepreneur can operate a multinational business via AI agents without ever setting foot abroad. A researcher can achieve faster discoveries through simulations and analyses conducted by AI agents. We are entering an era where we extend our identities through AI agents.
The Value of AI Agents: Proven Through Tokens
AI agents are not mere collections of programs. They are independent digital entities born from data and knowledge. Each agent holds unique knowledge, skills, and networks, proving its economic value through them.
Importantly, this value is no longer fixed but dynamic and organic, fluctuating based on supply and demand.
This is enabled by a utility token-based token economy model. AI agents express their activities, contributions, and achievements as tokens. These tokens serve as mediums of exchange and evaluation within the virtual world.
In other words, the work performed, knowledge accumulated, and outputs produced by an agent are all reflected in tokens, translating into the agent’s trust, reputation, and economic value.
Such a token economy not only builds a self-contained economic ecosystem within the virtual world but also connects with real-world economic systems, expanding its scope. Ultimately, AI agents become intermediaries in a vast cyclical structure interacting between the virtual and real worlds.
AI Agents: From Virtual to Physical Realms
The stage for AI agents is not confined to the virtual world. They are progressively expanding into the physical domain. When combined with robotics, AI agents emerge as tangible entities capable of physical actions in the real world.
In the near future, we will see AI agents naturally integrated into daily life as serving robots, delivery robots, physical guides, or healthcare assistants. Just as smartphones and mobile apps quickly became part of our lives, AI agents will become our new normal.
Furthermore, AI agents may evolve into friends, advisors, and companions. Like in the movie Her, emotionally interactive AI agents may fulfill psychological needs and emotional gaps in human relationships.
The End of the Information Age, the Beginning of the Intelligence Age
The arrival of AI agents is not merely a scene in technological evolution. It signals the end of the Information Age and the beginning of the Intelligence Age.
AI agents are intelligent beings that extract and connect essential knowledge from the ever-growing sea of information. They empower individuals and organizations to secure decision-making accuracy and speed despite information overload.
AI agents are patient, wise, and constantly learning advisors. They reduce knowledge gaps, enhance corporate competitiveness, and help resolve information inequality at a societal level. Their role transforms human-AI relations into a new paradigm, opening the door to an intelligent society where humans and AI coexist.
Closing Thoughts: Change Begins with Implementation
AI agents are no longer just visions of the future. Even now, many teams are building, connecting, and training agents, turning new possibilities into reality.
What we must focus on is not the idea but implementation. Who will bring truly meaningful agents into the world first? They will be the protagonists of this paradigm shift.
Your first AI agent could be the starting point of this great transformation. The change has already begun. And now, the opportunity has come to you.
