Tag: Kiro

🧑‍💻 Kiro – When the AI IDE Becomes a Software Architect

Posted on by hello@scuti

In recent years, AI coding assistants have reshaped how we build software. From GitHub Copilot to Cursor and Windsurf, developers can now write code faster, debug less, and “vibe code” any idea with just a few prompts.

But alongside the convenience comes a major issue: lack of structure. Prototypes are spun up quickly but are hard to scale. Code lacks documentation, design artifacts, and tests, often leading to technical debt.

Amazon AWS introduced Kiro to solve exactly this. Instead of being just a “coding companion,” Kiro positions itself as an AI software architect – guiding you from requirements to design, implementation, and validation. It marks a shift from prompt-driven development to spec-driven development.

 

🌍 Why Do We Need a “Spec-Driven AI IDE”

Most AI coding tools today focus on speed & productivity for individuals. Cursor, Copilot, and Windsurf enable a single developer to prototype an MVP in days. But when it’s time to scale or work in a team, these prototypes often become liabilities:

  • No clear requirements → miscommunication when tasks are handed off.
  • No design docs → difficult to refactor or onboard new devs.
  • Poor test coverage → bugs slip through QA.
  • Lack of best practices → fragile architectures.

AWS identified this gap and proposed a philosophy: AI should not only be a coding assistant but a software architect. Kiro is designed to follow a full development lifecycle: Requirement → Design → Implementation → Validation.

🔑 Core Features of Kiro

1. Spec-Driven Development

You describe requirements in natural language, and Kiro generates:

  • Requirement documents (user stories, acceptance criteria).
  • Design documents (system architecture, ER diagrams, sequence diagrams).
  • Task lists (implementation steps, tests).

 

2. Agent Hooks – Your “Senior Dev on Autopilot”

Agent hooks in Kiro act like a senior developer running in the background:

  • Automatically generate unit tests when you save files.
  • Check code smells against SOLID principles.
  • Update README and API specs.
  • Scan for accidentally committed secrets or credentials.

3. MCP & Steering Rules – Context Integration

Kiro supports the Model Context Protocol (MCP), allowing AI to access company docs, API specifications, or database schemas for richer context. Steering rules ensure AI stays aligned with project goals.

4. Agentic Chat – Context-Aware Conversations

Beyond inline completions, Kiro’s chat agent understands the entire codebase, requirements, and design docs. You can request:

  • “Add OAuth2 login flow with Google.”
  • “Generate a sequence diagram for password reset.”
  • “Write integration tests with Postgres.”

5. Familiar Foundation

  • Built on Code OSS, fully compatible with VS Code extensions, themes, and settings.
  • Powered by strong models (Claude 3.7, Claude 4.0) with future support for GPT and Gemini.
  • Pricing (Preview): Free tier (50 interactions/month), Pro $19 (1,000 interactions), Pro+ $39 (3,000 interactions).

⚔️ Kiro vs Cursor – A Fascinating Duel

Criteria Kiro – Spec-Driven IDE Cursor – Prompt-Driven IDE
Philosophy Requirement → Design → Code → Test Prompt → Code
Automation Agent Hooks (docs, tests, best practices) Manual, one task at a time
Context Handling MCP: APIs, DBs, external docs Mainly codebase indexing
Output Production-grade, standardized Quick prototypes, vibe coding
IDE Foundation Code OSS (VS Code ecosystem) VS Code fork
Best Fit For Enterprise teams, large-scale development Startups, solo prototyping

 

🛠️ Walkthrough: How Kiro Handles a Requirement

To see Kiro in action, I asked it to:

“Create a REST API for login/signup with JWT. You can use documents accounts for logic login/signup.”

Kiro then generated three artifacts: requirements, design, and tasks — essentially doing the work of a product manager, software architect, and tech lead in one.

You can see more in the folder .kiro in the source code on GitHub

1. Requirements

Kiro produced a requirements.md file describing the system as user stories with acceptance criteria:

  • Signup: Users can register with email and password. Validation rules include unique email, proper format, and a minimum 8-character password. On success, the system returns a JWT and user info.

  • Login: Users log in with valid credentials to receive a JWT. Invalid or non-existent accounts return a 401 Unauthorized.

  • Token Validation: Protected routes require a valid JWT. Expired, missing, or malformed tokens are rejected.

  • Security: Passwords must be hashed with bcrypt, never stored in plain text, and tokens signed with a secure secret.

  • Error Handling: The API must return clear but secure error messages, avoiding user enumeration.

This structured requirements file ensures that the authentication system has a clear scope and testable outcomes before any code is written.

2. Design

Next, Kiro generated a design.md document, laying out the NestJS architecture:

  • Modules & Services: AuthModule, AuthService, AuthController, JwtStrategy, AuthGuard.

  • DTOs for input validation (signup.dto.ts, login.dto.ts).

  • Data Model: An extended AccountSchema with unique userId/userName fields, password hashing middleware, and timestamps.

  • Security Setup: bcrypt with 12 salt rounds, JWTs signed with HS256, 24-hour expiration.

  • REST Endpoints:

    • POST /auth/signup → register new accounts

    • POST /auth/login → authenticate and return token

    • GET /auth/profile → protected endpoint returning current user info

The design document also detailed error handling policies (e.g., generic “Invalid credentials” messages), validation strategies, and a test plan (unit + integration).

3. Tasks

Finally, Kiro produced a tasks.md file — essentially an implementation plan:

  1. Setup dependencies: Install @nestjs/jwt, passport-jwt, bcrypt, and validation libraries.

  2. Create DTOs for signup/login validation.

  3. Enhance the Account model with password hashing and secure comparison methods.

  4. Implement JWT strategy for validating tokens in requests.

  5. Build AuthService methods for signup and login, returning JWTs.

  6. Build AuthController endpoints: /signup, /login, /profile.

  7. Add AuthGuard to protect routes.

  8. Wire everything into AuthModule and integrate with the app.

  9. Error handling via custom exception filters.

  10. Unit & integration tests for flows like signup, login, and token validation.

This task list reads like a well-prepared Jira board — ready for developers to pick up and implement step by step.

✨ The Result

In just one prompt, Kiro produced a requirements spec, a detailed design doc, and an actionable implementation plan.

Instead of jumping straight into code, the system starts with clarity:

  • What needs to be built

  • How it should be structured

  • How to test and validate it

This demonstrates how Kiro goes beyond “AI autocomplete” and into end-to-end engineering workflow automation.

🧪 Real-World Feedback from Early Users

1. Dev.to – Testing Kiro in Real Projects

  • Kiro produced clear design docs and structured task lists.
  • Agent Hooks auto-generated basic tests (though not deep coverage).
  • Strongest feature: spec-first workflow → immediate blueprint for the project.
  • Limitation: complex logic still requires developer intervention.

👉 Takeaway: Kiro feels more like a senior PM + junior dev than a pure coder.

2. Substack – Developing with Kiro

  • After just a few lines of description, Kiro generated detailed user stories broken into assignable tasks.
  • Docs and code stayed in sync — changes to requirements updated design and code automatically.
  • Saved several days of manual documentation work.
  • Still requires developer review for security and performance concerns.

👉 Takeaway: Perfect for small teams or startups without a dedicated product manager.

3. AWS Re:Post – Beyond a Coding Assistant

  • Positions Kiro as a tool for full-stack development from spec to deployment.
  • Biggest strength: reducing communication overhead between devs, PMs, and QA.

👉 Takeaway: The real value lies not just in code generation, but in process standardization.

🎯 Insights from Real Use Cases

  • Biggest Strength: End-to-end sync from requirements → design → code, saving huge time on documentation and planning.
  • Main Limitation: Complex logic still needs developer oversight, especially for security and performance.
  • Ideal Use Cases: Startups that need speed and structure, or enterprise teams looking to minimize technical debt.

📌 Conclusion – Is Kiro the “Future IDE”?

Kiro is not just another AI IDE. It represents a new philosophy: Spec-Driven Development, where AI doesn’t just write code but participates in the entire software development lifecycle.

  • Cursor remains fantastic when you need to code fast and iterate rapidly.
  • Kiro is for when you want to elevate AI from “assistant” to “software architect.”

đź’ˇ My take:

Kiro may not replace Cursor immediately. But in the next 2–3 years, as enterprises demand standardized, testable, documented code, spec-driven IDEs like Kiro are likely to become the norm.

👉 Have you tried Kiro yet? Do you think the future of AI IDEs should lean more towards speed (Cursor style) or structure (Kiro style)?

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