AI Agent

A method where a large language model (LLM), when creating an answer, does not rely only on its own knowledge but retrieves and uses relevant information from external data.
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What Is an AI Agent?

  • Unlike an LLM (large language model) that simply generates answers, an AI Agent is an intelligent system designed to interact with an environment and achieve a specific goal.
  • In other words, it is AI that judges for itself, uses necessary tools, takes action, and improves results.

Core Elements of an AI Agent

  1. Goal
    • The task the agent must perform, such as answering customer questions, writing reports, or modifying code.
  2. Perception
    • The stage of understanding the environment or input, such as user input, sensor data, or API responses.
  3. Action
    • Measures taken to achieve the goal, such as search, calculation, external API calls, or DB updates.
  4. Feedback Loop
    • The process of evaluating results and modifying the next action when needed.

Difference Between an AI Agent and a Simple LLM

  • LLM: Question -> answer (simple Q&A)
  • AI Agent: Question -> plan -> search/use tools -> execute multiple steps -> final answer

For example:

  • An LLM may answer “Tell me the weather in Seoul” based on past training data.
  • An AI Agent can call a real-time API and answer with the current temperature and weather.

Technologies Used by AI Agents

  • LLM (Large Language Model) -> Natural language understanding and reasoning
  • RAG (Retrieval-Augmented Generation) -> External knowledge retrieval
  • Tool use (Plugins, APIs) -> Calculators, browsers, databases, and more
  • Planner -> Breaks complex tasks into multiple steps and executes them
  • Memory -> Remembers past conversations or states to perform continuous tasks

Representative AI Agent Examples

  • ChatGPT + Tools (OpenAI) -> Can run code, browse the web, and analyze data
  • AutoGPT, BabyAGI -> Open source autonomous agents
  • LangChain Agents -> Connect multiple tools to automate workflows
  • Microsoft Copilot, Google Gemini Agents -> AI assistants integrated with productivity tools

Application Areas of AI Agents

  • Work automation: Email summarization, schedule management, document generation
  • Customer support: FAQ answers and consultation support
  • Research and analysis: Paper search, data analysis, report writing
  • Development assistance: Code generation, testing, debugging
  • Robotics: Autonomous driving, drones, smart factories

Summary

An AI Agent goes beyond an LLM that simply answers questions. It is an intelligent system that sets goals, uses tools, and interacts with its environment.
In short, it can be understood as “actionable AI.”