AWS Kiro Complete Guide

What is AWS Kiro?

Kiro (rhymes with "hero") is AWS's revolutionary AI-powered IDE that bridges the gap between rapid "vibe coding" and production-ready software. Unlike traditional code assistants that just provide autocomplete, Kiro takes an agentic approach with autonomous AI agents that can generate project plans, create files, run tests, and update documentation.

Kiro's core philosophy: "from vibe code to viable code" - emphasizing upfront planning through specifications, design documents, and structured implementation before writing code.

Core Features & Architecture

Technical Foundation

• Built on VS Code OSS - Full compatibility with existing extensions and settings
• Powered by Anthropic Claude - Uses Claude Sonnet 3.7 and 4.0 for advanced reasoning
• Multi-platform - Runs on macOS, Windows, and Linux
• Cloud-backed AI - Heavy processing happens in AWS cloud with guaranteed privacy

Key Capabilities

• Spec-Driven Workflow - Requirements → Design → Tasks → Implementation
• Agent Hooks - Intelligent automation triggered by file events
• Steering Rules - Project-specific AI guidelines and conventions
• MCP Integration - Extensible plugin system for external tools
• VS Code Ecosystem - Support for Open VSX extensions

The Kiro Spec System

At the heart of Kiro is its specification system - think of it as having a solutions architect and PM built into your IDE. Instead of diving straight into code, Kiro guides you through structured planning.

Three-Phase Workflow

1. Requirements Phase

Transform a simple prompt into structured requirements using EARS notation:

WHEN [condition] THE SYSTEM SHALL [expected behavior]

Example:
WHEN a user submits invalid form data, 
THE SYSTEM SHALL display validation errors next to relevant fields

2. Design Phase

Kiro generates technical design documents including:

• Architecture diagrams (Mermaid UML, ASCII art)
• Data models and interfaces
• Component interactions and API designs
• Non-functional considerations (security, performance)

3. Implementation Planning

Detailed task breakdown with:

• Granular implementation steps
• Requirement traceability
• Built-in quality measures (tests, documentation)
• Logical sequencing with dependency management

4. Execution

AI agents execute tasks one-by-one, providing:

• Code generation with full diff visibility
• Automated test execution
• Progress tracking and verification
• Timeline view of all changes

Agent Hooks - Automation Excellence

Hooks are Kiro's secret weapon for maintaining production-ready code. They're AI-powered automation triggers that handle the "boring but critical" tasks developers often forget.

Hook Types & Examples

File Save Hooks

Trigger: On File Save
Pattern: **/*.js, **/*.ts
Actions:
1. Scan for potential security issues (API keys, secrets)
2. Check coding standards compliance
3. Update related documentation
4. Generate/update unit tests if needed

Commit Hooks

Trigger: Before Commit
Actions:
1. Generate meaningful commit messages
2. Update CHANGELOG.md
3. Verify all tests pass
4. Check for breaking changes

File Creation Hooks

Trigger: New File Created
Pattern: **/*.component.ts
Actions:
1. Generate corresponding test file
2. Add to module exports
3. Update component documentation
4. Create Storybook stories

Steering Rules - Project Intelligence

Steering files are Kiro's memory system - markdown documents that store project-specific knowledge and keep the AI aligned with your standards.

Steering File Types

Product Context (`product.md`)

• Business objectives and user personas
• Feature priorities and roadmap
• Domain-specific terminology
• Compliance and regulatory requirements

Technical Standards (`tech.md`)

• Technology stack and architecture patterns
• Coding standards and best practices
• Testing strategies and coverage requirements
• Performance and security guidelines

Project Structure (`structure.md`)

• File organization conventions
• Naming patterns and conventions
• Module boundaries and dependencies
• Documentation standards

Model Context Protocol (MCP)

MCP extends Kiro's capabilities by connecting external tools and services. Think of it as Kiro's plugin system that allows the AI to interact with your entire development ecosystem.

Popular MCP Integrations

• AWS Documentation - Query AWS API docs directly
• Web Search - Research libraries and solutions
• Database - Query and analyze data schemas
• File System - Advanced file operations
• Git - Repository management and history

Example MCP Usage

# In Kiro chat:
"Set up an AWS Lambda with API Gateway"

# Kiro uses AWS-docs MCP to:
1. Look up current Lambda best practices
2. Check API Gateway integration patterns
3. Generate infrastructure-as-code
4. Create deployment scripts

Pricing & Plans

What Counts as an Interaction?

• Generating spec components (requirements, design, tasks)
• Executing individual implementation tasks
• Hook triggers that use AI
• Chat Q&A exchanges

Getting Started with Kiro

Installation

1. Download Kiro from the official AWS page
2. Install on macOS, Windows, or Linux
3. Sign in with AWS account (or Google/GitHub)
4. Import your existing VS Code settings

First Project Setup

1. Create Steering Files - Define your project context and standards
2. Set Up Hooks - Configure automation for your workflow
3. Create Your First Spec - Start with a small feature to learn the workflow
4. Install MCP Servers - Add integrations for your tech stack

Best Practices

• Start Small - Begin with simple features to understand the spec workflow
• Invest in Steering - Well-written steering files dramatically improve AI output
• Use Hooks Wisely - Start with basic hooks and gradually add complexity
• Review AI Output - Always verify generated code and specifications
• Iterate Specs - Don't expect perfect requirements on the first try