AI Agent

What is AI Agent

An AI Agent is an intelligent system that can autonomously perceive its environment, reason and make decisions, and take action to achieve a goal. In the modern AI context, an Agent typically uses a large language model (LLM) as its "brain" and has three core capabilities: perception (understanding user intent and the environment), reasoning (decomposing tasks and planning steps), and action (calling tools, executing code, reading and writing files). Unlike traditional "one question, one answer" AI, an Agent can autonomously complete multi-step, long-horizon complex tasks—such as "analyze this codebase and fix all type errors".

In one sentence: Agent = LLM brain + tools as hands and feet + memory + planning.

Core Components

A complete AI Agent typically consists of four core components: the brain (LLM, responsible for understanding and reasoning), memory (short-term conversation context + long-term vector memory), tools (calling external APIs, databases, search engines, etc. via Function Calling), and planning (decomposing complex goals into executable subtasks). These four work together to evolve an Agent from "able to chat" to "able to get work done".

# A typical Agent work loop
while not goal_reached:
    1. Perceive: read the current state (user input, tool returns, environment info)
    2. Think: the LLM reasons about the next step (ReAct / Plan-and-Execute)
    3. Act: call tools (Function Call / MCP)
    4. Observe: get the tool's execution result and update memory
    5. Loop until the goal is reached or human confirmation is needed

Agent vs Chatbot

A Chatbot does "you ask, I answer"—single-turn or limited multi-turn dialogue, with no proactive action. An Agent does "you set a goal, I complete it autonomously"—proactively calling tools, executing multi-step flows, and correcting errors. For example, asking "what's the weather today" is a Chatbot task; saying "check the weather, book a flight, add it to my calendar" is an Agent task. Claude Code, Cursor Composer, and Codex are all typical coding agents.

Key difference: a Chatbot only outputs text; an Agent can change the state of the world (edit files, run commands, call APIs).

Examples

Mainstream AI Agent tools today: in coding, Claude Code, OpenAI Codex, Cursor Composer, and Aider; in general domains, AutoGPT, Devin, and Manus. They all follow the "LLM + tools + planning" paradigm and differ in their toolsets, models, and workflow design. Understanding the Agent concept is the foundation for mastering modern AI coding tools.

# A typical coding agent task
User: "Add a user login feature to this project"
Agent executes autonomously:
  → Read the project structure and understand the stack
  → Plan: create User model → write auth endpoints → add a frontend login page → configure routing
  → Create/modify files one by one
  → Install dependencies, run tests, fix errors
  → Commit to Git and report completion