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K-Culture GlossaryAWords you meet while using AI

AI Agent

AI that doesn't just answer — it does the work for you: searching, creating files, even booking things.

In plain words

An agent is one step beyond an AI that "gives you an answer" — it's an AI that "does the work for you." A chatbot might tell you "this flight is the cheapest," but an agent will open a flight search site, compare the options, and get everything ready right up to the point of booking.

The key difference is that it "uses tools and carries out multiple steps on its own." Without a person giving instructions at every step, it takes a goal, makes a plan — searching, creating files, running programs — and follows through to the end. Think of it like telling an assistant, "Get me ready for my business trip," in a single sentence.

Since 2025, this has been the biggest topic in the AI industry. Agents that operate a computer screen directly, agents that write code, agents that shop and make reservations — they're appearing everywhere. But mistakes are still common, so the question of "how much can we actually hand off to it" comes along with it. The standard by which an agent connects to tools is called MCP, and the instructions that teach it how to work are called Skills.

How it shows up in the news

"Company X unveils an AI agent that directly operates a web browser" — if a demo shows the AI clicking and typing in place of a human, it's an agent story.

Try it yourself

  1. Turn on the search (research) feature in Claude or ChatGPT and give it this task: "Plan a 3-day, 2-night trip to Jeju Island for next week. Include flight times, three lodging options, and the weather."
  2. Watch the AI search multiple times on its own and pull the results together — this is agent-style work, different from simple question-and-answer.
  3. One step further: the trick to trusting an agent is to "ask narrowly." "Just handle the whole trip" works far worse than "just compare flights in a table" — and that's the same conclusion working builders have reached: an AI that tries to do everything well loses your trust, while an AI that does one thing sharply and says "I don't know" when it doesn't know earns it. Try adding "if you're not sure, ask me instead of guessing" at the end of your instructions — it dramatically cuts down on the agent's mistakes.

Chatbot vs. AI Agent: What's the Difference

Does it finish the task?, Does it just answer?

Nowadays agents often run inside a chatbot-style window, so you can't tell them apart by looks. The interface looks the same; what differs is how much happens after a single request. A chatbot gives an answer and stops, while an agent decides the next steps on its own and carries them through to the end.

AspectAI AgentChatbot
When you give it one instructionIt strings together multiple steps on its own until the task is doneIt gives a single answer and stops
Who decides the next stepThe AI plans and retries on its ownA human has to ask again to continue
Does it touch the outside worldIt searches, creates files, even makes bookingsUsually just replies in words
When it failsIt carries out the task while wrong, which is why permissions and confirmation steps are neededIt gives a wrong answer, and the person notices right away
AnalogyA proxy who takes on a task and gets it doneA help desk agent who answers whatever you ask

Rule of thumbIf you have to decide what happens next every single time, it's a chatbot; if the AI decides and acts on its own, it's an agent.

AI Agent vs. Multi-Agent: What's the Difference

Does one agent see it through to the end, Do multiple agents split up the roles

Multi-agent sounds like it just means "running several agents at once," but the real point isn't the number, it's dividing roles and having them check each other's work. Splitting the researching side from the reviewing side reduces the problem of one agent missing its own mistakes. But the more they exchange with each other, the more it costs and the slower it gets.

AspectAI AgentMulti-agent
StructureOne agent plans and executesMultiple agents with assigned roles exchange work
Good forTasks with clear steps, like booking, organizing, editingTasks that need broad coverage and mutual review, like research and reports
Catching mistakesA human reviews the outputAnother agent filters it first
Cost and speedRelatively cheap and fastMore expensive and slower, the more exchange involved
AnalogyOne skilled employeeA team with divided roles

Rule of thumbIf roles are split and agents check each other's work, it's multi-agent. If one agent sees the task through to the end alone, it's just an agent.

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