Introduction: Understanding Vibe Coding

Recently, there has been a lot of buzz online about Vibe Coding: AI programming revolution, one person outperforming a whole development team, no need to learn coding, mysterious prompts, AI black magic… Various marketing phrases have made a simple development tool seem complex and obscure.

Many friends have repeatedly asked me: What exactly is Vibe Coding? Is it really as amazing as people say?

Today, I will set aside technical jargon and empty concepts, and explain its underlying principles, components, and practical applications in plain language. Remember this core definition:

Vibe Coding = Human defines goals + AI executes tasks through toolchain + Human continuously checks and corrects in an interactive development process.

Essentially, it is large models (LLM) + toolchain (tools) + loop mechanism.

The underlying logic of how you used to develop software remains unchanged; however, the repetitive tasks that once required programmers to manually code, debug, and organize files can now be automated by an Agent, while still requiring human oversight and review.

Additionally, beyond software development, Vibe Coding Agents can also be used for tasks like PPT writing and video generation.

1. Simplified Explanation: What is Vibe Coding?

No complex definitions needed; in one straightforward sentence: You control the direction, break down requirements, and review results, while AI handles all the tedious development work.

In this development model, you do not need to write code line by line; you act as the project commander:

• Human’s work: Describe product features, break down business requirements, adjust development direction, review changes, and provide feedback to AI for corrections;
• AI’s work: Write code, fix bugs, read and write files in the file system, run test scripts, debug interfaces, and refactor code.

This is why many people feel that AI behaves like an automated development assistant when they first use it. Today’s Vibe Coding Agent is no longer just a chat bot; it is a professional development assistant with complete practical capabilities.

2. Core Principles: How Does AI Write Code Automatically?

2.1 Basic Architecture: General Underlying Logic

Currently, mainstream tools on the market, whether Claude Code, Codex, or Trae, share a similar core idea (LLM + tools + loop), but their implementation architectures and permission systems differ.

A standard AI programming agent essentially consists of three parts:

• Large Model: Responsible for thinking, understanding requirements, and organizing code logic;
• Tool System: Responsible for executing operations (reading files, modifying code, running commands, etc.);
• Agent Loop Mechanism: A closed-loop process of continuously “thinking → executing → observing results → correcting.”

2.2 Core Mechanism: Agentic Loop

This is the most critical core of Vibe Coding, and many articles make it seem extremely complex. In simple terms: AI works while self-checking, automatically correcting errors until the task is completed.

Taking Codex as an example, its operating mechanism can be understood as a cyclical process:

• Collect Context: Read project files, understand code structure and current state
• Execute Operations: Call tools (edit files, run commands, test, etc.)
• Validate Results: Determine success based on execution feedback
• Continue Iteration: If it fails, correct and repeat the loop

Simple tasks may conclude in one round, while complex bugs or large projects may require multiple iterations (typically between 1 to 20 rounds). Moreover, we can interrupt the AI at any time and correct its direction, maintaining complete control over the development pace.

2.3 Sandbox Environment: A Safety Barrier to Prevent AI Misbehavior

The sandbox is a concept that beginners often find confusing, but it is actually quite simple: a designated isolated safe workspace for AI.

Currently, large models are not mature and pose risks such as deleting files, altering code indiscriminately, or executing high-risk commands. The sandbox strictly limits AI permissions:

• Allowed Operations: Modify code, create files, install dependencies, run ordinary commands within the sandbox;
• Prohibited Operations: Cannot access the entire computer, cannot delete system files, prohibited from executing high-risk deletion commands, and no unrestricted internet access.

Even if the AI writes erroneous code, the impact will be confined to the sandbox and will not damage the original project. This is also why tools frequently prompt for permissions; it is not cumbersome but rather to protect your device and project security.

3. Practical Introduction: General Configuration Components for AI Programming

Currently, the industry has established a common standard for Agent configuration, where different Agents only differ in file names and storage paths, while the core components remain consistent. Understanding these configurations will completely dispel the mystery surrounding AI programming.

This article uses OpenAI’s Codex as an example for demonstration. In the Codex system, the core of an AI programming project is not just the prompts but a combination of prompts + toolchain + execution environment:

project-root/
├── AGENTS.md   # Core configuration (the only must-have)
└── optional configuration:
    ├── MCP config
    ├── Skills
    ├── Hooks
    └── Subagents

3.1 AGENTS.md: AI’s Long-Term Work Manual

AGENTS.md is a reusable system prompt file used to provide default behavioral constraints, project background, and development specifications for the AI programming Agent, automatically loaded into the context during each task execution.

A project can have multiple AGENTS.md files located at user, project, and submodule levels, providing different granular constraints. The system will combine these rules at runtime, typically exhibiting a “global rules + local overrides” context fusion mechanism (the specific implementation depends on different Agent systems).

Directory Structure:

~/.codex/
├── AGENTS.md                   # User-level (global default rules)

project-root/
├── AGENTS.md                   # Project-level (global project rules)             
├── src/
│   ├── frontend/
│   │   ├── AGENTS.md   # Frontend module override rules
│   ├── backend/
│   │   ├── AGENTS.md   # Backend module override rules

User-level AGENTS.md Example

# User Global AI Policy

## Default Behavior
- Always prioritize safety over speed
- Prefer readable code over clever code
- Ask before performing destructive operations

## Code Style Default
- Use TypeScript when applicable
- Prefer functional programming style
- Avoid global state unless explicitly required

## Communication Style
- Be concise
- Provide explanations only when necessary

Project-level AGENTS.md Example

# Project AI Instructions

## Project Context
This is a SaaS platform built with React + Node.js + PostgreSQL.

## Architecture Rules
- Frontend: React + TypeScript
- Backend: Node.js (NestJS preferred)
- Database: PostgreSQL only

## Global Constraints
- No direct database access from frontend
- All business logic must be in service layer
- All APIs must follow REST conventions

## Project Structure
- /docs/prd.md → Requirement document
- /docs/spec.md → API specification document
- /frontend → UI layer
- /backend → API layer
- /shared → shared utils and types

Module-level Front AGENTS.md Example

# Frontend Override Rules

## UI Strict Mode
- All components MUST use Tailwind CSS
- No inline styles allowed
- All pages must be responsive by default

## Interaction Rules
- Every button must have hover state
- All async actions must show loading state

## Performance Rules
- No unnecessary re-renders allowed
- Use memoization for heavy components

Module-level Backend AGENTS.md Example

# Backend Override Rules

## Security First Mode
- All inputs must be validated using schema
- SQL injection protection is mandatory
- No raw SQL allowed

## Data Rules
- Sensitive fields must be encrypted
- Never log passwords or tokens

## Execution Constraints
- No system-level commands allowed
- No external network calls without whitelist

3.2 Skills: On-Demand Capability Extensions

Similar to AGENTS.md, skills also serve as reusable prompt files but are called or injected as needed during task execution to extend the Agent’s behavioral capabilities, which may include instructions, examples, scripts, or workflow definitions.

The difference between it and AGENTS.md is:

• AGENTS.md: Focused on “reusable foundational rules and long-term constraints, loaded during each task execution”
• Skills: Focused on “reusable task capabilities and process templates, loaded on-demand during task execution”

You can customize exclusive capability packages to encapsulate specific task behavior patterns, such as UI style constraints, backend coding conventions, or testing process templates, allowing the Agent to exhibit consistent execution strategies in specific tasks.

Directory Structure:

project-root/
├── .codex/
│   └── skills/
│       ├── ui-design/
│       │   ├── SKILL.md
│       │   ├── examples/
│       │   ├── scripts/
│       │   └── assets/
│       ├── backend-code/
│       │   ├── SKILL.md
│       │   ├── scripts/
│       │   └── examples/
│       └── test/
│           ├── SKILL.md
│           ├── scripts/

UI Design Skill Example

---
name: ui-design
description: Frontend UI design system and styling rules
---

# UI Design Skill

## Design System
- Use Tailwind CSS
- All buttons must be rounded-2xl
- Use consistent spacing scale (4, 8, 12, 16)

## Component Rules
- All components must be reusable
- No inline styles allowed
- All pages must be responsive

## Interaction Rules
- Hover effects required on all clickable elements
- Loading states required for async actions

## Validation
Run before commit:
```bash
npm run lint
npm run type-check

**Backend Code Skill Example**

name: backend-code description: Backend engineering standards for API, service layer, and database design

Backend Code Skill

Architecture Rules

• Controller must NOT contain business logic
• Business logic must be in service layer
• Database access only via repository layer

API Design Standard

All APIs must follow format:

{ “code”: 0, “message”: “success”, “data”: {} }

Error Handling

• All service methods must use try/catch
• Standardized error format required
• No raw error exposure to frontend

Database Rules

• No direct SQL in controller
• All queries must use ORM layer
• Timeout default: 5000ms

Security Rules

• Validate all inputs via schema
• Sensitive fields must be excluded from logs
• No password/token logging

Automation Hooks

After creating API:

npm run generate:types
npm run lint

**Backend Code Script Example**

scripts（示例）

# scripts/check.sh

echo "Running backend validation..."

npm run lint
npm run test
npm run type-check

**Backend Code Examples Example**

POST /api/user/login

Request: { “email”: “test@example.com”, “password”: “123456” }

Response: { “code”: 0, “message”: “success”, “data”: { “token”: “xxx” } }

**Test Skill Example**

name: test description: Automated testing strategy and test generation rules

Testing Skill

Testing Strategy

• Unit test for all service functions
• Integration test for all APIs
• Critical flows require E2E testing