AI IDE Review: Kiro IDE vs VS Code - Hands-On Testing of Spec-Driven Development

First Impressions: More Than Just Another VS Code Fork

Amazon's Kiro AI IDE immediately stands out with its charming ghost mascot icon - a refreshing departure from the abstract logos dominating the AI IDE space. But beyond its appealing visual identity, this AI IDE introduces three groundbreaking features that set it apart from other AI IDE competitors like Cursor and Windsurf.

While built on the familiar VS Code foundation, this AI IDE transforms the traditional VS Code experience with intelligent automation and structured development workflows. After extensive hands-on testing of this AI IDE, here's what makes Kiro unique and where it currently falls short compared to other VS Code alternatives.

Getting Started: Seamless Setup Experience

Setting up Kiro IDE is refreshingly straightforward. Simply visit kiro.dev and download the appropriate version for your operating system. The installation process mirrors other desktop applications, and the welcome screen guides you through initial configuration.

Key Setup Features:

• Mandatory Authentication: Unlike some IDEs, Kiro requires login (Google integration available)
• VS Code Configuration Import: Seamlessly transfer your existing VS Code settings
• Theme Selection: Choose your preferred interface theme
• Shell Command Setup: Optional terminal integration for command-line access

The AI IDE interface will feel immediately familiar to VS Code users, confirming that this AI IDE is indeed built on the VS Code foundation. For developers transitioning from traditional VS Code to an AI IDE, Kiro provides the perfect bridge between familiar VS Code functionality and advanced AI IDE capabilities.

The Three Pillars of Kiro's Innovation

1. Spec-Driven Development: Structure Before Code

Kiro's most distinctive feature is its Spec mode, which enforces a "plan before build" methodology. Instead of jumping straight into code generation, Spec mode guides you through a structured development process:

Requirements Phase: Kiro analyzes your prompt and generates a comprehensive requirements.md file, ensuring clear project understanding.

Design Phase: The AI creates detailed architectural documentation, including system components, interfaces, and implementation strategies.

Task List Creation: A structured tasks.md file breaks down the project into manageable, sequential tasks.

Implementation: Only after completing the planning phases does Kiro begin actual code generation, following the established roadmap.

2. Agent Hooks: Automation That Responds to Events

Agent Hooks represent Kiro's most innovative feature - AI agents that automatically trigger based on specific events:

• File Creation: Automatically generate documentation or tests for new files
• File Save: Update related files or run quality checks
• File Deletion: Clean up dependencies or update references
• Manual Triggers: On-demand execution for specific tasks

This automation extends your workflow capabilities, allowing AI to handle routine tasks like documentation updates, unit test generation, and performance optimization without manual prompting.

3. Enhanced AI Integration

Kiro exclusively uses Claude Sonnet 3.7 and 4.0 models, leveraging Amazon's investment in Anthropic. The interface provides:

• Autopilot Mode: Allows agents to execute actions without approval requests
• Context Management: Easy addition of documentation, codebase files, and folders
• Image Attachment: Visual context support for complex requirements
• Chat History: Comprehensive interaction tracking

Real-World Testing: Authentication Implementation

To evaluate Kiro's capabilities, I tested its Spec-Driven Development with a practical Next.js authentication system using NextAuth.js.

The Test Project

• Base: Fresh Next.js 15 installation with TypeScript and Tailwind
• Goal: Implement complete authentication with login/logout functionality
• Requirements: Hardcoded credentials, protected dashboard route

Spec Mode in Action

Requirements Generation: Kiro successfully analyzed the prompt and created comprehensive requirements documentation, understanding the existing project structure and technical stack.

Design Documentation: The AI generated detailed architectural plans, including component relationships and implementation strategies.

Task Breakdown: The system created a logical sequence of implementation tasks, from basic setup to final testing.

Implementation Results and Challenges

Initial Success: The first four tasks completed successfully, with Kiro generating clean, well-structured code that followed modern development practices.

Critical Failure: Task 5 encountered repeated "unexpected error" messages, preventing completion despite multiple retry attempts.

Error Pattern: The chat history revealed 7-8 failed attempts on the same task, suggesting either model limitations or API restrictions.

Secondary Test: Theme Implementation

To verify Kiro's reliability, I conducted a simpler test: adding light/dark theme switching to a Next.js application.

Results

Complete Success: All tasks completed successfully, with Kiro implementing a functional theme switcher in the top-right corner.

Minor Issues: The logo disappeared in dark mode, but this represented a minor styling issue rather than functional failure.

Intermittent Errors: Even this simpler project experienced some unexpected errors, though fewer than the authentication test.

Agent Hooks: Promise vs. Reality

Testing the Agent Hooks feature revealed both potential and current limitations:

Setup Process

1. Click the ghost icon to access Kiro's sidebar menu
2. Select "Agent Hooks" and click the plus button
3. Choose from templates or describe custom automation
4. Configure trigger events (file creation, save, deletion, manual)

Test Results

Hook Creation: Successfully created a documentation update hook Execution Failure: The hook triggered correctly when saving a modified package.json file but ended with an error Monitoring: The system properly detected file changes and initiated the AI agent

Kiro vs. Cursor: A Practical Comparison

After extensive testing, here's how Kiro compares to its primary competitor:

Kiro's Advantages

• Structured Planning: Spec-Driven Development provides superior project organization
• Innovative Automation: Agent Hooks offer unique workflow enhancement possibilities
• Free Preview: Currently available at no cost during the preview period
• Claude Integration: Access to cutting-edge AI models optimized for code generation

Cursor's Strengths

• Reliability: More consistent execution without unexpected errors
• Mature Features: Polished user experience with fewer rough edges
• Proven Track Record: Established user base and community support
• Broader Model Support: Access to multiple AI models beyond Claude

Current Limitations and Concerns

Technical Issues

• Frequent Errors: Unexpected failures interrupt complex tasks
• Model Restrictions: Limited to Claude models only
• Incomplete Features: Agent Hooks show promise but lack reliability

Workflow Challenges

• Mandatory Authentication: No option to skip login requirements
• Error Recovery: Limited options for handling failed tasks
• Complex Setup: Spec mode requires more initial investment than direct coding

Who Should Try Kiro IDE?

Ideal Candidates

• Planning-Oriented Developers: Those who value structured development approaches
• Team Leaders: Projects requiring comprehensive documentation and clear task breakdown
• Automation Enthusiasts: Developers interested in event-driven AI assistance
• Early Adopters: Those willing to work with preview-stage software

Consider Alternatives If

• Reliability is Critical: Production environments requiring consistent performance
• Model Flexibility Needed: Projects requiring specific AI models beyond Claude
• Simple Workflows Preferred: Direct coding without extensive planning phases

The Verdict: Promising AI IDE but Not Ready for Prime Time

This AI IDE introduces genuinely innovative concepts that could reshape AI-assisted development. The Spec-Driven Development approach addresses real problems in AI IDE tools, promoting better planning and documentation practices. Agent Hooks represent a compelling vision of automated development workflows that go beyond traditional VS Code extensions.

However, current reliability issues prevent this AI IDE from being a complete VS Code replacement or Cursor alternative for professional development. The frequent unexpected errors and incomplete feature implementations suggest this AI IDE needs more development time before it can compete with mature VS Code alternatives.

Recommendation

Try Kiro if you're interested in exploring the future of structured AI development and can tolerate preview-stage limitations.

Stick with Cursor if you need reliable AI assistance for production work and can't afford workflow interruptions.

Looking Forward

Kiro IDE's core concepts are sound and potentially transformative. If Amazon can resolve the reliability issues and complete the feature implementations, Kiro could become a serious contender in the AI IDE space.

The free preview period provides an excellent opportunity to explore these innovative features without financial commitment. As the tool matures, it may well deliver on its promise of bringing structure and automation to AI-assisted development.

Ready to experience the future of structured AI development? Download Kiro IDE and test its innovative Spec-Driven Development approach for yourself.