How to Build AI Agent: Skills, Prompt, Instructions, Hook

Learn how to build AI agents using prompts, instructions, skills, hooks, and memory. Includes real workflows for VS Code, Copilot, Laravel, SaaS, and autonomous engineering teams.

08 May 2026 2:18 PM
How to Build AI Agent: Skills, Prompt, Instructions, Hook

How to Build AI Agent: Skills, Prompt, Instructions, Hook

AI coding agents have evolved from simple autocomplete tools into autonomous development teammates that can understand context, follow standards, execute workflows, and even automate repetitive engineering tasks.

If you are building AI workflows inside VS Code, GitHub Copilot, Cursor, or any agent-compatible IDE, understanding the difference between prompts, instructions, skills, agents, and hooks is the key to building a scalable AI development system.

In this article, we’ll break down the full architecture with real examples, best practices, and production-ready workflows.

🚀 Why AI Agent Architecture Matters

Most developers start with plain prompts.

Example:

Build a Laravel payment API

This works for small tasks.

But once your project grows into:

  • SaaS
  • microservices
  • real estate marketplaces
  • payment systems
  • AI dashboards

plain prompts become inconsistent.

That’s where a layered AI architecture becomes powerful.

The professional stack looks like this:

Prompt → Instructions → Skills → Agent → Hooks → Memory

Each layer solves a different problem.

🧠 1) Prompt — The Immediate Task Request

A prompt is the direct task you give the AI.

It defines what you want right now.

Example

Create a Bootstrap 5 property details page from selected Figma frame

Best use cases:

  • one-time tasks
  • generating components
  • quick debugging
  • refactoring a file
  • writing tests

Best Practice

Prompts should focus on the goal, not the whole system.

✅ Good:

Generate Laravel API Resource for Property model

❌ Bad:

Use clean architecture, DTOs, repository pattern, tests, documentation, Docker, CI, and also optimize SQL

That belongs in instructions and skills.

📘 2) Instructions — The Global Rules

Instructions define the permanent standards your AI must follow.

Think of this as your:

engineering law

Examples:

  • code style
  • architecture rules
  • naming conventions
  • folder structure
  • framework preferences
  • testing rules

Example

Use Laravel service layer, repository pattern, DTO mapping, thin controllers, and PHPUnit tests.

Perfect for:

  • team standards
  • project-wide consistency
  • frontend design systems
  • backend clean architecture
  • coding conventions

Real Example

For your real estate frontend system:

Bootstrap 5 + SCSS 7-1
BEM naming
mobile-first
Blade-ready partials
no inline styles

This ensures every prompt and every skill stays aligned.

🛠️ 3) Skills — Reusable Workflows

Skills are the repeatable HOW layer.

A skill teaches the AI:

how to perform a specific workflow correctly every time

Examples:

  • Figma → Bootstrap conversion
  • Laravel API resource generation
  • PHPUnit generation
  • code review workflow
  • PR analysis
  • CI validation

Example Skill

name: figma_to_bootstrap5_scss
purpose: Convert Figma frames into Bootstrap 5 + SCSS architecture
rules:
- mobile-first
- semantic HTML5
- Blade partial extraction
- BEM naming

This is reusable across:

  • homepage
  • property details
  • upload flow
  • dashboards

Best Use Case

Create a skill when:

  • the workflow repeats often
  • the AI usually makes the same mistakes
  • your team has a preferred way of doing something

🤖 4) Agent — The Decision Maker

The agent is the specialized persona and orchestrator.

It decides:

  • which skill to use
  • how to break the task
  • what files to generate
  • how to validate the result

Think of it as:

senior engineer AI teammate

Example

real_estate_frontend_architect

Responsibilities:

  1. inspect selected Figma frame
  2. identify page type
  3. use conversion skill
  4. apply design system
  5. generate Blade-ready code
  6. optimize responsive behavior

Best Examples

Great agent personas:

  • Senior Laravel Engineer
  • Senior Frontend Architect
  • CTO Agent
  • Product Manager Agent
  • QA Reviewer Agent
  • DevOps Agent

⚡ 5) Hooks — Automatic Triggers and Automation

Hooks are the WHEN layer.

A hook automatically runs an action when something happens.

Examples:

  • after code generation
  • before commit
  • after PR creation
  • after Figma sync
  • after API schema change
  • after tests fail

Example Hook

After generating a Laravel controller:
→ auto generate FormRequest
→ auto generate DTO
→ auto generate Feature test

This removes repetitive manual follow-up prompts.

Best Hook Ideas

For your projects:

Real estate frontend

After component generation → create skeleton loader variant

Laravel backend

After model creation → generate migration, factory, seeder, resource

Payments

After webhook service creation → generate retry queue + failed job alerts

Hooks make the system feel autonomous.

🏗️ How All Layers Work Together

This is the professional architecture:

Prompt = what to do now
Instructions = permanent rules
Skills = repeatable workflows
Agent = orchestrator persona
Hooks = automatic follow-up tasks
Memory = project learning over time

Example Flow

Prompt:
Build property details page

Agent:
real_estate_frontend_architect

Uses skills:
- figma_to_bootstrap5_scss
- real_estate_design_system

Applies instructions:
- Bootstrap 5
- BEM
- SCSS 7-1

Triggers hook:
- generate skeleton loader
- create Blade partials

This is how elite AI-assisted teams scale.

🎯 Best Folder Structure

A clean repo structure might look like:

.github/
  copilot-instructions.md
  instructions/
    laravel.instructions.md
    frontend.instructions.md

.ai/
  skills/
    figma_to_bootstrap5_scss/
      SKILL.md
    laravel_test_generator/
      SKILL.md

agents/
  frontend_architect.agent.md
  laravel_engineer.agent.md

hooks/
  post_component_generation.hook.md
  post_model_generation.hook.md

This keeps everything modular.

🏆 Best Practices

To build truly useful AI agents:

✅ Keep prompts short

Describe the task only.

✅ Put standards in instructions

Never repeat architecture rules in every prompt.

✅ Create skills for repeated workflows

If you repeat it 3+ times, make it a skill.

✅ Use agents as personas

Specialize by role, not by single task.

✅ Add hooks for repetitive follow-up work

This creates automation loops.

✅ Add memory later

Store project decisions and preferred patterns.

🚀 Real Use Cases for Your Business

This architecture is perfect for:

🏠 Real estate platform

  • Figma → frontend generation
  • property page SEO
  • image gallery loaders
  • owner dashboard flows

💳 Payment SaaS

  • webhook services
  • queue retries
  • fraud AI agents
  • Laravel microservices

🧠 AI SaaS

  • RAG agents
  • Slack assistants
  • document pipelines
  • auto evaluation hooks

🎯 Final Thoughts

The secret to great AI engineering is separating responsibilities.

  • Prompt = task
  • Instructions = law
  • Skill = workflow
  • Agent = expert persona
  • Hook = automation trigger

Once these layers work together, your IDE stops feeling like autocomplete and starts behaving like a real engineering team.

This is the future of serious AI-assisted software development.