What Sets AI Apart from AI Agents?: From Core Concepts to Real-World Applications
- SmileShark Team
- Sep 25
- 7 min read
What Sets AI Apart from AI Agents?: From Core Concepts to Real-World Applications
Written by Eunmin Jeon
Hello, I’m Eunmin Jeon from the Brand Team.
Recently, terms such as AI agents and agentic AI have been appearing almost as frequently as “AI” itself. Although these names sound similar, their meanings differ in significant ways. In this article, we take a closer look at the concept that is attracting the most attention today—the AI Agent.
Note: In this article, AI refers to Generative AI (GenAI), and AI agent refers to an agent built on Generative AI.
Why Are AI Agents Gaining Attention?
As AI technology evolves, tools like ChatGPT have become part of everyday life, streamlining everything from work tasks to personal errands.
Now a new concept—the AI agent—is pushing these capabilities even further.
For example, there are demonstrations of an AI agent ordering a pizza end-to-end, including making the call and posting a review. Others show agents logging into an app and placing a hamburger order without human intervention.
Industry analysts agree this is more than a trend. Gartner’s Top 10 Strategic Technology Trends for 2025 lists nine AI-related technologies. OpenAI CEO Sam Altman described AI agents not as mere question-and-answer tools but as “virtual colleagues and work partners,” stating:
“The future of computing will be less about using apps and more about telling agents what to do.”
In short, AI agents are more than a passing trend—they represent a shift in how we will work. But what distinguishes an AI agent from conventional AI? Let’s explore the core concepts and practical use cases.
Defining the AI Agent
An AI agent is an autonomous AI system capable of processing data, making decisions, and executing tasks without direct human intervention. It interacts with its environment to accomplish user-defined goals. The essential attribute is autonomy: once the goal is understood, the agent plans and executes the necessary steps on its own.
How does this differ from traditional AI? The comparison below highlights key differences.
Category | AI | AI Agent |
Definition | Predictive model performing a specific task | Goal-driven system that acts autonomously |
Interface | Prompt | Goal / Task |
Operation | Input → Output | Continuous task execution |
Autonomy | None | Full (decides subsequent actions) |
Example | Document summarization, image generation | Business-trip planning, automated customer service |
Traditional AI follows a “prompt in, output out” pattern.
For example, if you ask an AI model to “Reserve a Korean barbecue restaurant near Gangnam Station for 7 p.m. tonight,” it cannot complete the reservation on its own.
You would instead need to request: “Recommend popular barbecue restaurants,” then manually click a booking link.
An AI agent, by contrast, can interpret the user’s intent from a single instruction and execute the entire workflow:
Identify the required subtasks (search, comparison, reservation, confirmation).
Gather candidate restaurants and analyze details such as price, menu, reviews.
Verify availability and book the table.
Provide a final confirmation (time, location, reservation ID).
AI Agent vs. AI Assistant vs. Chatbot
How do AI agents compare with familiar tools such as chatbots or AI assistants?
Category | Chatbot | AI Assistant | AI Agent |
Purpose | Provides predefined answers (FAQ) | Responds flexibly to user queries | Plans and acts to achieve a goal |
Intelligence | Low (rule-based) | Medium (LLM-driven natural language understanding) | High (planning, reasoning, action) |
Initiative | None (reactive only) | Partial (Primarily Response-Based) | High (actively completes multi-step tasks) |
Examples | Bank customer-service bots, parcel tracking | Siri, Google Assistant, Samsung Bixby | Auto-GPT, Rabbit R1, Devin, Microsoft Copilot |
Action Range | Text interactions only | App integrations (weather, alarms) | Executes tools, web navigation, app automation |
Autonomy | None | Limited | Full |
A chatbot delivers fixed responses to predefined inputs—for example, returning a shipping date when given a tracking number.
A AI assistant (think Apple’s Siri) can respond dynamically—“Hey Siri, what’s the weather tomorrow?”—but still relies on direct user prompts.
An AI agent, however, can orchestrate a complete workflow and take action without continuous user guidance, making it ideal for repetitive or automated tasks.
Core Characteristics of AI Agents
Autonomy
Unlike conventional AI systems that require continuous human involvement, an AI agent can act independently once a goal is defined. Traditional AI may provide information, but it typically relies on the user to complete the task. By contrast, an AI agent can schedule meetings, summarize files, or send messages without direct human intervention.
Goal Orientation
An AI agent does more than simply respond to input. It executes a sequence of tasks to accomplish a clearly defined objective. For example, when instructed to “schedule a meeting,” the agent can coordinate participants’ availability, send email invitations, and update the calendar automatically.
Tool Interaction
AI agents can access and integrate with a variety of tools—such as APIs, databases, or sensors—to solve problems. A common example is connecting to Google Calendar to check a user’s availability and reserve a time slot.
Adaptability
Rather than following fixed rules, an AI agent refines its approach as conditions change. It continuously learns from data and user feedback. If a morning calendar is fully booked, for instance, it will propose afternoon meeting times instead.
The Operational Loop: Think → Act → Observe
AI agents process tasks through the following workflow:
Think
The agent analyzes the current objective and determines the optimal sequence of actions. For example, it may consider whether to gather information first or perform a calculation before proceeding.
Act
Based on the plan, the agent executes the required tasks—invoking appropriate tools or sending requests to external systems (for instance, a payment gateway) to carry out the action.
Observe
The agent evaluates the outcome of its actions, reviews the results, and organizes the findings to inform the next step.
AI agents repeat this cycle until the specified goal is achieved. Over time, they incorporate user feedback, continuously refining their strategies to deliver a more tailored and effective response.
The following example illustrates how an AI agent can generate a personalized email based on user data.
Step 1. User → Agent
When a user initiates a specific action—such as requesting a movie recommendation email—the AI agent recognizes the request and begins processing.
Step 2. User Profile Verification (Feature Store)
Multiple customers exist, each with stored preferences for movie genres.
This information is maintained in a Feature Store and leveraged to generate personalized recommendations.
The AI agent accesses this data to prepare the newsletter content.
Step 3. Movie Information Aggregation (Metadata Store)
The Metadata Store contains metadata such as movie titles, descriptions, genres, and release years.
The AI agent organizes this information to select recommendations appropriate for each individual user.
Step 4. Language Model Execution
The language model generates a personalized recommendation email using the following inputs:
The curated movie list and descriptions
User profile information (genre preferences)
Step 5. Email Generation and Delivery
Finally, the personalized message created by the language model is sent to the user via email.
Example Email:
🔥 Immerse Yourself in the Thrill of Sci-Fi and Action!
Buckle up for a ride through time, space, and alternate realities with our top sci-fi action recommendations just for you.
In this manner, when the user requests “Create a personalized email for my customers,” the AI agent completes the entire workflow—from consolidating customer and movie data, to drafting the personalized email, and sending it. Leveraging both user data and contextual understanding, the AI agent autonomously plans and executes each step, continuously delivering increasingly tailored experiences.
Representative AI Agent Use Cases
Many companies are now integrating AI agents into their services. The following examples highlight some of the most prominent implementations:
OpenAI “Operator”
OpenAI’s Operator is a service that enables AI to interact directly with web interfaces. It leverages the Computer Use Agent (CUA) model, combining GPT-4o’s vision capabilities with reinforcement learning. This autonomous AI agent can engage with graphical user interfaces (GUIs), including buttons, menus, and text fields, enabling it to perform tasks that traditionally required human input.
Anthropic “Computer Use”
Anthropic’s Computer Use is a task automation tool integrated with the Claude 3.5 language model. It allows Claude to operate like a human, observing computer screens, moving the mouse cursor, and clicking buttons to perform tasks. Currently, Computer Use is deployed across various platforms, including the mobile task management app Asana, the graphic design platform Canva, and the food delivery service DoorDash.
Microsoft “Copilot”
Microsoft’s Copilot suite includes a range of AI agents designed to automate business processes, particularly document-related tasks:
SharePoint Agent: Assists users in quickly locating information by linking to specific sites, files, or folders.
Employee Self-Service Agent: Automates tasks such as leave requests, payroll and benefits inquiries, and equipment requests.
Translation Agent: Provides real-time translation of conversations across nine languages during video conferences.
Project Manager Agent: Automates the entire project lifecycle, from planning to execution.
Butterfly Effect “Manus ai”
Manus, the AI agent used in earlier examples, is an autonomous AI agent developed by the Chinese startup Butterfly Effect. What sets Manus apart is its ability to autonomously execute a wide range of tasks, including resume organization, stock data analysis, and New York real estate recommendations. In addition, Manus can operate entirely in the cloud, providing flexible, scalable deployment.
In conclusion
The era when AI functioned merely as a tool has passed. AI agents now stand at the core of digital transformation, driving operational efficiency and 24/7 scalability. Unlike conventional AI that simply provides answers, AI agents take action—integrating with internal systems to execute real business processes.
“We are entering an era in which AI agents, integrated with internal systems, can execute actual business operations.”
For organizations, the question is no longer “Should we adopt AI agents?” but rather “How should we deploy them effectively?”
References
Sam Altman's Blog, “Three Observations”, (2025.02.10), https://blog.samaltman.com/three-observations
AWS, “What Are AI Agents?”, https://aws.amazon.com/ko/what-is/ai-agents/
Korea Local Information Development Institute, “Trends in AI Agent Technologies and Service Development Abroad”, Vol. 145, http://klidwz.or.kr/webzine/vol145/sub_2_4.html
Tech42, “Anthropic Launches AI Agent Capable of ‘Using Computers Like Humans’”, (2024.10.23), https://www.tech42.co.kr/%EC%95%
AI Times, “The Next Stage of Artificial Intelligence: The Era of AI Agents – Building AI Agents Through MS Copilot Studio”, (2024.11.20), https://www.aitimes.kr/news/articleView.html?idxno=32885
Chosun Ilbo, “What Is ‘Manus’… Called the World’s First General-Purpose AI”, (2025.07.27), https://www.chosun.com/economy/weeklybiz/2025/07/24/CNF44UEWCVHW5PAUBMI4V2U5UY/
