Artificial Intelligence (AI) has become an integral part of modern software development. Various AI coding assistants can help developers generate code snippets, explain program logic, and accelerate debugging. However, software development involves much more than writing code. Before implementation begins, developers need to understand business requirements, design the system architecture, break work into manageable tasks, and ensure the entire team follows consistent development standards.
To address these challenges, Amazon introduced Kiro, an AI-powered IDE built around the concept of Spec-Driven Development. Unlike traditional AI coding assistants that generate code directly from prompts, Kiro guides developers through the entire software development lifecycle—from understanding project context and defining specifications to automating workflows and integrating AI with external services.
In this article, you’ll learn how to start your first project in Kiro while exploring its four core capabilities: Steering, Specs, Hooks, and Model Context Protocol (MCP).
Getting Started with Kiro
Once Kiro is installed, the first step is to open an existing project or create a new one.
After the project is loaded, Kiro analyzes the project structure, programming language, dependencies, and configuration files. You can then access the Kiro Panel from the sidebar to begin interacting with the AI.
Through this panel, developers can:
- Ask questions about the source code
- Create new features
- Generate documentation
- Build feature specifications
- Execute automated workflows
Although the interface resembles a traditional AI chat assistant, Kiro takes a different approach by generating implementations based on project context and structured specifications.
Understanding Project Context with Steering
One of the biggest challenges for AI coding assistants is the lack of project context. While AI may understand the syntax of a programming language or framework, it often lacks knowledge about the application’s architecture, coding conventions, or team development practices.
To solve this problem, Kiro introduces Steering.
Steering is a collection of documents that provides essential project context before the AI generates code. These documents may include:
- Application objectives
- Technology stack
- Project structure
- Coding standards
- Naming conventions
- Team workflows
- Development best practices
For example, a React project might include the following Steering document:
# Coding Standards
– Use TypeScript for all new files.
– Prefer functional components.
– Use React Hooks instead of class components.
– Store API logic inside /services.
– Follow ESLint and Prettier configuration.
Whenever a developer asks Kiro to generate a new component, the AI automatically follows these guidelines without requiring them to be repeated in every prompt.
This approach produces more consistent implementations, especially for large projects maintained by multiple developers.
Building Features with Specs
The core capability of Kiro is Specs.
Instead of generating code immediately, Kiro first helps developers define a feature specification through three stages: Requirements, Design, and Tasks.
This workflow is known as Spec-Driven Development, where implementation begins only after the feature has been clearly defined.
Step 1: Requirements
The first stage focuses on defining business requirements.
For example, suppose you want to add an authentication system to your application. You can simply enter the following prompt:
Create a user authentication feature that supports login, logout, password reset, and JWT-based authentication.
Kiro then transforms the request into structured user stories and acceptance criteria.
Example:
User Story
As a registered user,
I want to log in using my email and password,
so that I can securely access my account.
This ensures that the feature requirements are clearly documented before implementation begins.
Step 2: Design
Once the requirements are finalized, Kiro generates a technical design.
For example, it may propose an architecture similar to the following:
Authentication Service
│
├── Login API
├── JWT Generator
├── Password Reset Service
└── User Repository
At this stage, developers can review and validate the proposed architecture before moving on to implementation.
Step 3: Tasks
The final stage breaks the design into smaller implementation tasks.
Example:
☐ Create Login API
☐ Create JWT Middleware
☐ Create Login Page
☐ Add Validation
☐ Write Unit Test
☐ Update Documentation
Developers can then complete each task individually, making progress easier to manage and track.
Example Implementation Generated from Specs
Once the specification has been completed, developers can ask Kiro to implement individual tasks.
For example, Kiro might generate the following authentication endpoint:
import jwt from “jsonwebtoken”;
export async function login(req, res) {
const user = await authService.login(req.body);
const token = jwt.sign(
{ id: user.id },
process.env.JWT_SECRET
);
return res.json({ token });
}
This example illustrates that implementation takes place only after the AI understands the project context, feature requirements, and technical design, resulting in code that is generally more consistent with the application’s architecture.
Automating Development Workflows with Hooks
Beyond feature planning and implementation, Kiro also includes Hooks, a feature designed to automate repetitive development tasks.
Hooks allow the AI to execute predefined actions whenever specific events occur, such as creating or modifying files.
For example, you can configure a Hook using the following instruction:
When a new React component is created,
automatically generate a corresponding test file
using React Testing Library.
With this configuration, creating:
Button.tsx
automatically generates:
Button.test.tsx
Hooks can also be used to:
- Run code formatters automatically
- Update documentation
- Generate changelogs
- Execute unit tests
- Run linting after code changes
By automating repetitive tasks, Hooks help improve both productivity and code quality.
Extending AI Capabilities with Model Context Protocol (MCP)
Source code is only one part of a software project. Developers often rely on internal documentation, databases, APIs, knowledge bases, and DevOps tools throughout the development process.
To connect AI with these external resources, Kiro supports Model Context Protocol (MCP).
MCP is an open protocol that enables AI models to communicate with external tools and services through a standardized interface.
For example, a developer can instruct Kiro to use an internal API reference when implementing a client library.
Use the internal API documentation to implement the Product API client.
If MCP is configured, Kiro retrieves the required information before generating the implementation.
MCP can integrate with various external resources, including:
- Internal documentation
- Knowledge bases
- Search services
- Databases
- Internal APIs
- DevOps platforms
With richer context, the AI can produce more accurate recommendations and implementations.
Kiro Development Workflow
A typical Kiro workflow can be summarized as follows:
Open Project
│
▼
Generate Steering
│
▼
Create Specs
│
├── Requirements
├── Design
└── Tasks
│
▼
Implement Feature
│
▼
Hooks Execute Automatically
│
▼
MCP Retrieves External Context
Rather than functioning solely as a code generator, Kiro acts as an AI development partner throughout the software development lifecycle.
Best Practices for Using Kiro
To maximize Kiro’s effectiveness, consider the following best practices:
- Keep Steering documents up to date whenever the project structure, technology stack, or coding standards change.
- Create Specs before implementing new features to ensure requirements and technical designs are well documented.
- Use Hooks to automate repetitive tasks such as linting, formatting, unit testing, and documentation updates.
- Integrate MCP with your team’s existing tools and services so the AI has access to broader and more relevant context.
Conclusion
Kiro introduces a new approach to AI-assisted software development. By combining Steering, Specs, Hooks, and Model Context Protocol (MCP), developers can build applications using a structured workflow rather than relying solely on prompt-based code generation.
By understanding project context, defining requirements before implementation, automating repetitive tasks, and leveraging information from external systems, Kiro helps teams improve consistency, maintainability, and overall development productivity.
For developers and engineering teams looking to integrate AI more deeply into their workflows, Kiro offers more than just code generation—it provides end-to-end support throughout the entire software development lifecycle.
