Kiro: The Future of IDEs

by Justin Cook

THE BETA IS FINALLY OUT Y'ALL, and I can talk about it! I spent all day on July 15th at AWS HQ in NYC, going through the brand, marketing and functionality with the creators of Kiro, and am ELATED to dicuss what we can up with. Let's walk through this new IDE:

Amazon Kiro is an advanced AI-powered coding platform currently under development by Amazon Web Services (AWS). It is designed to streamline the software development process by leveraging multi-agent AI technology, allowing developers to generate, optimize, and debug code quickly and efficiently. Kiro works by interpreting user inputs and drawing from vast data sets, offering a near real-time experience. The platform is structured to handle various software-related tasks, including code generation, design documentation, performance optimization, and debugging, through AI agents that can work autonomously or collaboratively.

(Important note: This is NOT the Amazon KIRO, short for "Keep It Running Optimally", for Amazon’s internal maintenance and reliability system used primarily in its fulfillment centers. That was a high operational efficiency, minimal downtime, and proactive equipment maintenance where this is an IDE)

One of the standout features of Amazon Kiro is its multi-agent orchestration capability. This means that developers can use multiple AI agents—both first-party and third-party tools—to handle different aspects of the development process. Whether it's generating code snippets, creating technical documentation, or identifying bugs, these agents work together to ensure a seamless workflow. The platform also supports multi-modal interactions, meaning users can provide inputs not only through text but also via visual diagrams and other contextual files. This makes the development process more intuitive and accessible for a wider range of users, including those who may not be deeply technical.

In addition to code generation, Amazon Kiro excels in code optimization and bug detection. It can automatically flag potential issues in code, offer suggestions for performance tuning, and even generate documentation based on the codebase. This comprehensive approach helps improve both the quality and efficiency of the software development process. For developers working with large teams or complex codebases, Kiro provides a powerful tool to automate routine tasks, refactor code, and integrate security checks, all while improving overall code quality.

Amazon Kiro is positioned as a versatile tool for different types of users. Developers, startups, and large enterprises can benefit from its ability to automate various aspects of the coding process, freeing up time for more complex, creative work. Startups and hackathons, in particular, can leverage Kiro for rapid prototyping, converting ideas into working code quickly. Even non-technical users, such as product managers or designers, can interact with Kiro through visual or textual prompts, enabling them to contribute to the coding process without needing deep technical expertise. This makes Kiro a valuable tool for teams across multiple disciplines.

While Amazon has not yet officially launched Kiro, the platform is expected to enter a private beta or public preview by late 2025. Though still in development, Kiro represents a significant leap forward in AI-assisted software development. It competes with other similar tools in the market, such as GitHub Copilot and OpenAI's Codex, but sets itself apart by integrating multiple AI agents and offering more intuitive, multi-modal interaction options.

The launch of Amazon Kiro is expected to have a profound impact on the software development landscape. It could transform the way developers work, allowing them to focus more on high-level problem-solving while letting AI handle routine tasks like code generation and debugging. By automating these tasks, Kiro has the potential to dramatically improve productivity and software quality, democratize the development process, and make coding more accessible to a broader audience. If successful, Kiro could redefine how we approach software creation in the coming years.

 VIBE versus SPEC:

AWS Kiro introduces two distinct workflows: Vibe Coding (yes we all know where this comes from) and Spec‑Driven Coding (also known as “spec” mode). These modes represent two different lens to constructing software, depending on your end game—whether you're quickly prototyping or aiming for a production-ready system.

Vibe Coding is designed for fast, casual, and exploratory development. You can start by simply prompting Kiro with something like “build me a login form,” and it will instantly generate code. This approach is great for rapid iteration and testing ideas, similar to how GitHub Copilot or Cursor works. However, it’s less structured. While this makes it fast and convenient, it can lead to technical debt if you don’t eventually apply more formal processes to clean up the code.

Spec‑Driven Coding, on the other hand, introduces a structured and disciplined workflow.

Before writing any code, Kiro generates three core documents: requirements.md (user stories and acceptance criteria using EARS notation), design.md(architecture diagrams, interfaces, and API outlines), and tasks.md (step-by-step implementation instructions including tests and deployment tasks). This approach ensures that key planning, testing, and documentation are built in from the beginning.

Overview:

Spec mode bridges the gap between a quick prototype and production-quality software. It automates best practices like test generation, security scans, linting, and README updates, helping developers maintain code quality as the project scales. This is especially valuable for teams building long-term systems that require stability, readability, and maintainability.

In terms of when to use each, vibe coding is ideal for experimenting or spinning up prototypes quickly. It trades off structure for speed. Spec‑driven coding, meanwhile, is better suited for building maintainable, robust applications. While it requires upfront planning, it reduces issues down the line and makes collaboration and scaling easier.

AWS emphasizes this distinction with the phrase: “from vibe coding to viable code.” Kiro’s goal is to allow developers to move fast without sacrificing quality. With spec mode, you’re not just generating code—you’re embedding engineering discipline from the start.

Early developer feedback echoes this. Reddit users have described spec mode as “automatically applying SWE best practices to the vibe-coding workflow.” It locks projects into a structured flow of Requirements → Design → Tasks → Testing, and generates unit tests for every feature it writes. This creates a more organized and production-ready environment without sacrificing the creative speed of AI coding. You "Vibe code" is if you are experimenting, prototyping quickly because there is much less documentation, formaitly but unfortuantley you have to clean it up after. Think of it like writing a book: An author doesn't publish an edited draft.

Spec-Driven is a lot different. This is building maintainable codebases where you are required to put in upfront planning but this pays off long-term.

Overall, I would recommend doing BOTH! Let's Get Started:

Here’s a clean paragraph-style breakdown of the Kiro.dev demo:

1. Installation & SetupTo get started with Kiro, download the installer for your operating system—Windows, macOS, or Linux. Once installed, sign in using your preferred account (GitHub, Google, or AWS). On first launch, Kiro allows you to import your existing VS Code settings and select a familiar theme, making the transition seamless and user-friendly.

2. Project InitializationYou can open an existing project by running kiro . in your terminal, or start a new one. Kiro automatically analyzes the project structure and offers to generate “steering files” such as product.md, tech.md, and structure.md. These files encode the context, conventions, and structure of your codebase so Kiro can understand and work within your specific environment.

3. Spec-Driven Feature PlanningWithin the “Code with Spec” pane, you simply describe a feature or goal—such as “Add user authentication.” Kiro then converts this into structured artifacts: requirement documents (requirements.md), design plans (design.md), and task lists (tasks.md). It may even ask clarifying questions to ensure a precise implementation path.

4. Agentic Task ExecutionKiro acts like a junior engineer by executing tasks such as writing new files, adding tests, and refactoring across multiple modules. Each proposed change is displayed as a live diff, allowing you to approve, edit, or roll back updates before they’re finalized.

5. Agent Hooks: Automate Repeated WorkWith agent hooks, you can automate repetitive workflows. For example, you can configure a hook to automatically generate unit tests every time a new file is saved or committed. A common use case would be: “Whenever I add a new React component, generate its corresponding test.” These hooks enhance productivity and consistency across your codebase.

6. Vibe Mode & ChatKiro offers a “Vibe Coding” mode for free-form exploration—perfect for sketching ideas or prototyping. When you're ready to formalize your work, simply switch to “Spec mode.” The chat interface supports multimodal context, so you can drop in logs, screenshots, or even entire documents, and Kiro will intelligently incorporate them into its reasoning and outputs.

7. Model Context Protocol (MCP) & EcosystemKiro integrates seamlessly with external tools through its Model Context Protocol (MCP), allowing access to databases, APIs, design documents, and more. Because it’s built on the open-source version of VS Code, you can continue using your existing extensions and themes via Open VSX.

8. Supervised or Autopilot FlowYou can choose between two development modes: Supervised or Autopilot. In Supervised mode, you review and approve each change before it's applied. In Autopilot mode, Kiro runs the full task sequence end-to-end, generating code, writing tests, and creating documentation—with minimal need for user input.

The Basics Again: In this progressively growing world of Agentic AI, now in our IDEs such as with Kiro, I wanted to spell out the differences that I see across the hyperscalers through the lens of distinct functionalities of Generative AI, AI Agents, and Agentic AI but this is just a reminder:

Generative AI (Gen AI) is utilized for:

- Art and Content Creation

- Virtual Worlds and Gaming

- Marketing and Advertising

- Operational Flow:

- Input idea → GenAI model → Output (e.g., text/image)

- Key Aspects:

- Purpose: Produces creative content such as text and images.

- Functionality: Focuses on generating innovative outputs.

- Examples: GPT-3, DALL-E

AI Agents are utilized for:

- Customer Service

- Automation in Operations

- Smart Assistants

- Workflow:

- Goal → Agents → Tools → Final Output

- Noteworthy Features:

- Purpose: Automates tasks based on predefined rules.

- Functionality: Executes specific tasks and actions.

- Examples: Chatbots, virtual assistants

Agentic AI are deployed in:

- Autonomous Vehicles

- Robotics

- Dynamic Environment Management

- Process Flow:

- Objective → Sub-Agents → Tools & Memory → Results

- Core Characteristics:

- Purpose: Acts autonomously, making intricate decisions.

- Functionality: Interacts dynamically and adapts to varying environments.

- Examples: Autonomous vehicles, intelligent robots

Basically, Generative AI emphasizes content creation with a static approach, AI Agents adhere to rules for task automation, and Agentic AI showcases autonomy, memory, adaptability, and decision-making akin to human-like intelligence in dynamic scenarios.

Conclusion

Kiro is fun and excited. The UI may feel a little with VS Code (see my desktop here).

I promise you the spec functionality to document and the speed of vibe coding is nothing that I have ever played with before. Go download it NOW and get started here: https://kiro.dev

-Justin Cook, Global CTO - AWS Alliance, AWS Ambassador, AWS Golden Jacket, AWS Community Builder, AWS SME Expert