====== An Introductory Knowledge Map ======

This document provides an introductory overview of Agentic Coding, presenting the current landscape of the field.

So, what is this article about?

The goal is to:

Summarize core concepts of Agentic Coding

Explain practical ways to work with and apply Agentic Coding in real projects

Clarify several non-obvious or confusing topics that many people often wonder about

Main sections:

Conceptual foundations (things you must understand to practice effectively)

The current chaos of Agentic AI tools

Why there are so many Kits and Skills

Achieving 5×–10× coding performance

===== I. Conceptual Foundations =====
(These are required to understand and practice Agentic Coding effectively)

The following concepts are organized from beginner level upward to make them easier to approach.

Most modern Agentic Coding tools are built around these ideas. The community has produced a large amount of documentation and tutorials—easy to find—but understanding what these concepts actually mean is the key.

==== I.1 Commands — Telling AI What to Do ====
Command /kəˈmɑːnd/
→ lệnh, chỉ thị

When you install an AI Agent IDE such as Cursor, Antigravity, or Claude, and type something like “create a new file”, that is a command.

This is the most basic interaction model:

One task → one instruction → AI executes it

==== I.2 Plans — Planning Before Execution ====
Plan /plæn/
→ kế hoạch

One drawback of simple command-based interaction is that you only see what the AI did after it finishes.

In Plan mode, the AI first explains:

What it intends to do

How it will do it

You can review, discuss, and modify the plan. Once the plan looks good, execution begins.

Example:
To store a light/dark theme setting, options include localStorage, sessionStorage, cookies, or backend storage.
Without a plan, the AI may choose an approach that seems reasonable to it—but not to you.

==== I.3 Rules — Always / Never ====
Rule /ruːl/
→ quy tắc

When coding with AI, you often repeat the same constraints.

For example:

Your team agrees on CamelCase naming

But code style conventions suggest snake_case

You keep reminding the AI: “Use CamelCase”

Instead, define this once as a rule, and the AI will consistently follow it.

Examples:

“Always use Tailwind CSS for styling”

“Never install new libraries without approval”

==== I.4 Skills — AI’s Expertise Pack ====
Skill /skɪl/
→ kỹ năng

If you keep repeating the same instructions across similar tasks, you may want to package them into a reusable skill.

Rules define what is allowed or forbidden.
Skills define how to solve a specific type of problem, in your preferred style.

Example:
Instead of repeatedly saying:

“Write REST APIs”

“Include try/catch”

“Log errors and retry if needed”

You define a skill called API_Standard, and simply invoke it.

==== I.5 MCP (Model Context Protocol) — AI’s Eyes ====
Protocol /ˈproʊtəkɒl/
→ giao thức

If you have ever had to describe UI designs to an AI step by step, MCP removes that friction.

MCP allows AI to:

Connect directly to Figma and read designs

Fetch up-to-date documents automatically

Instead of guessing, the AI can see and read external sources.

==== I.6 Sub-agents — Divide and Conquer ====
Sub-agent /sʌb ˈeɪdʒənt/
→ tác nhân phụ

AI agents have limited context windows (e.g., 200k tokens).
Large tasks with many sub-tasks can degrade performance over long conversations.

Sub-agents act like specialized assistants, each responsible for a specific domain or task.

==== I.7 Hooks — Final Checkpoints ====
Hook /hʊk/
→ móc nối, điểm chặn

If certain actions should always happen after the AI finishes a task—without requiring reasoning—you can use hooks.

Examples:

Automatically run Prettier after code generation

Run security scans after AI changes code

==== I.8 Workflow — The Working Process ====
Workflow /ˈwɜːrkfloʊ/
→ quy trình làm việc

Many people apply Agentic Coding by mimicking real-world software development:

Requirement → Design → Planning → Coding → Review → Testing → Deployment → Monitoring

Workflow connects all concepts into a logical sequence, defining when and how each concept should be used.

With good prompts + solid workflows, output quality improves dramatically.

==== I.9 Kit — Don’t Reinvent the Wheel ====
Kit /kɪt/
→ bộ công cụ

You may understand Rules, Skills, Plans—but still struggle to write effective prompts.

That’s where kits come in.

Instead of:

Writing React rules from scratch

Configuring MCP for PostgreSQL manually

Others have already bundled best practices into standardized kits.

=== Summary of Section I ===

Concepts (Rules, Skills, MCP, …): Tools

Workflow: How to use tools effectively

Kit: A standardized bundle of tools + workflow

===== II. The Chaos of Agentic AI Tools ======

There are many Agentic AI tools today: Cursor, Antigravity, Claude Code, OpenCode, Augment, etc.

This abundance can feel overwhelming.

However, discussions with practitioners reveal:

For ~90% of tasks and users, these tools are not fundamentally different.

If you are a typical developer working on company tasks, most tools will work just fine.

==== II.1 CLI vs GUI Agentic Tools ====
CLI (Command Line Interface) /ˌsiː el ˈaɪ/
→ giao diện dòng lệnh
GUI (Graphical User Interface) /ˌdʒiː juː ˈaɪ/
→ giao diện đồ họa

GUI tools: Friendly UI, easy onboarding, strong UI preview

CLI tools: Terminal-first, highly customizable

Choose based on preference.

==== II.2 Why the Claude Code FOMO? ====
FOMO /ˈfoʊmoʊ/
→ sợ bị bỏ lỡ

Common reasons:

Strong trend-setting (skills, workflows, kits)

Perceived higher intelligence

Better performance on long, heavy coding tasks

High code output with relatively low cost

For typical users: differences remain marginal.

==== II.3 Generating Large Code Volumes Cheaply ====

A practical trick:

Use Claude Code as a proxy

Connect it to cheaper third-party model subscriptions

Switch accounts when quotas are exhausted

This setup is ideal for:

Solo builders

Freelancers

Experts with frequent new projects

==== II.4 Tool Comparison ====

Cursor: Smooth UX, editor-first AI integration

Claude Code: Fast, terminal-based, strong agent behavior

Antigravity: Thought-flow and context control

OpenCode: Open-source and transparent

Augment: Optimized for large codebases

=== Summary of Section II ===

Don’t over-FOMO for most use cases

Special needs require special setups

===== III. Why Are There So Many Kits and Skills? ======

Every day, new Kits and Skills appear:

AI Dev Kits

Claude Kits

Workflow frameworks

Specialized UI/UX or React skills

These packages are powerful because:

They reduce setup overhead

They are tested and refined by their creators

If it feels overwhelming, remember:

Kits are meant to increase productivity, not create confusion.

Pick a few that match your workflow and update occasionally.

===== IV. 5×–10× Coding Performance ======
Performance /pərˈfɔːrməns/
→ hiệu năng

Claims of “5×–10× performance” should be compared against:

No AI usage

Or very basic command-only AI usage

Agentic Coding shines when concepts, workflows, and kits work together—not when AI is used as a simple autocomplete.