Creating an application with just a few prompts is one thing, but getting it ready for actual production deployment requires way more work than you might think. Any developer who's tried this knows the feeling. Sure, it's fun to chat with an AI agent and quickly whip up a prototype, but when you try to turn it into a real service, countless issues start surfacing. You can't tell what assumptions the model made, the requirements aren't clear enough to know if it's actually what you wanted, and it's hard to figure out how the system is even designed.
This is exactly the problemKiroaims to solve. Going beyond simple AI coding tools, it positions itself as an AI IDE that can systematically manage the entire development process from concept to production.
Why We Need Spec-Based Development
The current AI coding approach could be called "vibe coding." You throw out prompts based on rough feelings, look at what comes out, and gradually tweak things. This works great for rapid prototyping, but it has clear limitations in actual production environments.
Kiro's spec-based development is designed to solve these problems. It's an approach where you clearly define requirements, systematically establish technical design, and then proceed with implementation based on that foundation. You could see it as combining traditional software development methodologies with the advantages of AI.
Kiro's Core Features: Specs and Hooks
Specs Functionality
Kiro's specs feature consists of three stages:
Stage 1: Deriving Requirements from a Single Prompt
When you input a simple prompt like "Add a review system to the product," Kiro converts this into concrete user stories. It breaks it down into review viewing, creation, filtering, rating features, and generates acceptance criteria using EARS (Easy Approach to Requirements Syntax) notation for each.
What's important in this process is that it explicitly reveals the assumptions the AI made. You can clearly see what features the AI included when the developer said "review system" and what edge cases it considered.
Stage 2: Technical Design Based on Requirements
Based on approved requirements, Kiro analyzes the existing codebase and generates technical design documents. This includes data flow diagrams, TypeScript interfaces, database schemas, and API endpoints.
What's interesting about this stage is that it considers consistency with the existing codebase. It designs with advance consideration of how new features will integrate with the existing architecture.
Stage 3: Implementation Task Generation
Based on the design, it generates concrete implementation tasks. Each task is arranged in the correct order according to dependencies and linked to requirements. It includes unit tests, integration tests, loading states, mobile responsiveness, and accessibility requirements.
Hooks Functionality
Hooks are event-based automation features. You can set up agent tasks that automatically execute when files are saved or created. For example:
- Automatically update test files when saving React components
- Update README files when modifying API endpoints
- Check for credential leaks with security scans before commits
This serves to automatically check parts that even experienced developers might miss. It also has the advantage of applying consistent quality standards across the entire team.
Real Development Scenario: E-commerce Review System
Let's look at the actual development process through Kiro's official example. It's a scenario of adding a review system to an e-commerce application that sells handcrafted items.
When a developer inputs "Add a review system for products," Kiro converts this into concrete user stories like:
- Users can view product reviews
- Users can write reviews
- Users can filter reviews
- Users can rate products
Each story includes acceptance criteria written in EARS notation. For example, the "write review" feature includes edge cases like restricting access for non-logged-in users, preventing duplicate reviews, and input validation.
In the technical design stage, Review interfaces, database schemas, and API endpoints are generated. Relationships with existing Product models and integration with user authentication systems are considered.
In the implementation stage, each task is ordered according to dependencies. For example, it proceeds in the order: database migration → API endpoint implementation → frontend components → test writing.
Differences from Existing AI Coding Tools
The current market has various AI coding tools like GitHub Copilot, Cursor, and Replit Agent. Most of these focus on code autocompletion or simple code generation. Kiro's differentiator is that it systematically manages the entire development process.
The spec and codebase synchronization feature is particularly impressive. When code changes during development, specs are automatically updated, and conversely, when specs are modified, related tasks are updated. This is an important feature that solves the problem of documentation and code inconsistency.
It also provides comprehensive features like Model Context Protocol (MCP) support, steering rules that guide AI behavior across projects, and agentic chat supporting various context providers.
Limitations and Concerns
Of course, Kiro isn't perfect either. There are several concerns:
Increased Complexity: Spec-based development is systematic, but it can be overhead for simple feature development. It's questionable whether this process is necessary in all situations.
AI Dependency: The structure heavily depends on AI from spec generation to implementation. There's risk when AI makes wrong assumptions or misses important requirements.
Learning Curve: Existing developers will need time to adapt to the new workflow. It could be burdensome especially for developers unfamiliar with spec writing.
Vendor Lock-in: Becoming dependent on Kiro's specialized features could make it difficult to migrate to other tools.
Impact on Development Productivity
Kiro's impact on development productivity is two-sided.
In the short term, it might take more time in the spec writing and design stages. But in the long term, clear requirements and design can reduce confusion and rework during the development process.
It's expected to have even greater effects in team development environments. All team members can share the same specs and maintain consistent code quality through hooks.
However, this process might be excessive for individual developers or small projects. It would be advisable to use it selectively based on project scale and complexity.
Future Prospects and Implications
The vision Kiro presents is intriguing. It aims to solve problems like ensuring design consistency across teams, resolving conflicting requirements, eliminating technical debt, ensuring code review rigor, and preserving knowledge when senior engineers leave.
This can be seen as an attempt to innovate the entire software development process beyond just coding tools. The effort to systematize AI-human collaboration methods is particularly noteworthy.
However, several conditions are needed for this vision to be realized:
Standardization: Standardization of spec writing and development processes is needed. Currently it's Kiro's own approach, but whether it can develop into an industry-wide standard is key.
Tool Maturity: Due to AI technology limitations, perfect spec generation or code implementation might be difficult. The technology needs to mature more to be practically usable.
Development Culture Change: Developers need to become familiar with spec-based development and accept it as a natural workflow.
Practical Application Possibilities
Currently, Kiro is offered free during its preview stage. It supports Mac, Windows, and Linux and claims to support most programming languages. It can use VS Code settings and Open VSX compatible plugins as-is, reducing transition costs from existing development environments.
Scenarios worth trying in practice include:
Medium-scale or Larger Projects: It would be effective for projects with complex requirements where team collaboration is important.
Legacy System Improvements: When adding new features to existing codebases, it would help maintain consistency with existing architecture.
New Developer Training: It could be a good tool for learning systematic development processes.
However, it's not suitable for all situations. It could actually hinder rapid prototyping or simple script writing.
In conclusion, Kiro is an interesting attempt that presents a new direction for AI coding tools. The approach of systematizing the entire development process beyond simple code generation certainly has meaning. Features like spec-code synchronization and event-based automation will help solve real development field problems.
However, for such tools to succeed, not only technical completeness but also changes in development culture are needed. The key is whether developers will recognize the value of spec-based development and accept it as a natural workflow. Since it's currently in preview stage, we'll need to watch its performance and stability in actual production environments. Nevertheless, Kiro's attempt to systematize AI-human collaboration methods can be evaluated as an important case study for glimpsing the future of software development.