When our team at AWS set out to build Kiro more than a year ago, we faced a fundamental question that many developers are grappling with today: How do we harness the power of AI to build real, production-ready software without falling into the traps of quick fixes and technical debt?
The answer, we discovered, lies not in abandoning structure for the sake of speed, but in reimagining how structure and AI can work together through what we call spec-driven development.
What Is Spec-Driven Development?
Spec-driven development is a structured approach to software engineering that enhances agentic AI capabilities by focusing on three core stages:
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Requirements Definition: Establish a comprehensive understanding of functional/non-functional requirements and user needs before coding.
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Design Planning: Create technical designs that address system interaction, scalability, security and maintainability.
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Structured Implementation: Break development into discrete, manageable tasks that build on previous work and maintain overall context.
Unlike vibe coding, this method produces persistent, reviewable artifacts that preserve context and reasoning beyond a single AI chat session, allowing developers to maintain quality while accelerating delivery.
The Problem With Vibe Coding
Don’t get me wrong; vibe coding has its place. The ability to quickly prototype, iterate on small changes or explore new technologies through conversational AI is genuinely transformative. I’ve seen developers build applications in languages they’ve never used before, create cross-platform solutions rapidly and prototype ideas that would have traditionally required significant time and resource investment.
But here’s the challenge: Ask any developer who’s spent hours in a chat window with an AI assistant, and they’ll tell you the same thing. By the end of the session, they’ve lost track of their original intent. The context disappears, the reasoning behind decisions gets lost and what should be a structured development process becomes a series of disconnected interactions.
This works fine for small, isolated tasks. But when you’re building something substantial like a feature that will serve thousands of users, a system that needs to scale or code that your team will maintain for years, you need more than conversational iteration. You need persistent context, clear requirements and a structured approach that AI can follow consistently.
The Three Pillars of Spec-Driven Development
Spec-driven development is a return to software engineering fundamentals, enhanced by agentic AI capabilities. The approach centers on three core stages that mirror what good development teams have done for decades:
Requirements Definition
Before writing a single line of code, we define what we’re building and why. This isn’t a feature description. It’s a comprehensive understanding of functional and non-functional requirements, user needs and system constraints.
Design Planning
With clear requirements, we create a technical design that addresses how the system will work, what technologies we’ll use and how different components will interact. This is where we consider scalability, security, maintainability and all those critical aspects that less experienced developers often overlook.
Structured Implementation
Finally, we break down the implementation into discrete, manageable tasks. Each task builds on previous work, maintains context about the overall goal and can be executed and validated independently.
What makes this approach powerful with AI is that each stage produces artifacts — actual documents that persist beyond any single conversation. When a developer returns to the project days later, or when a teammate needs to understand the system, the context is preserved in structured, reviewable formats.
Real-World Impact: Transforming Development Speed
The effectiveness of this approach became clear when our own team used Kiro to build a Kiro feature. One of our teammates wanted real-time notifications for task completion. This would typically take two weeks of development due to the complexity of supporting multiple operating systems and team bandwidth.
Using spec-driven development, we delivered the entire feature in just two days. Kiro examined our existing codebase, identified the multi-platform challenges, suggested appropriate libraries and provided a working solution that integrated seamlessly with our existing architecture. The initial research of understanding how to handle notifications across different operating systems would typically take one day. With Kiro’s structured approach, this analysis happened in one shot.
Though speed matters, it’s more about maintaining quality while accelerating delivery. The spec approach required us to think through requirements up front, consider edge cases and design for maintainability, all while using AI to execute the complex implementation details.
Balancing Structure With Flexibility
One concern I often hear is that spec-driven development might be too rigid, that it could stifle the creative exploration that makes AI coding tools so appealing. In practice, we’ve found the opposite to be true.
The spec approach works best for substantial development efforts like major features, significant architectural changes or new system components. But once you have that structured foundation, vibe coding becomes powerful for iteration and refinement. You can quickly adjust implementations, experiment with alternatives and make incremental improvements without losing sight of the bigger picture.
We’ve learned that the most productive approach combines both methodologies: use specs to define and implement the major changes, then use vibe coding to iterate and refine. You get both structured thinking for complex problems and rapid iteration for improvements.
The Human Element Remains Critical
Spec-driven development doesn’t replace human judgment. Rather, it amplifies it. AI agents can’t understand the specific nuances of your service, the operational requirements of your application or the business context that drives your decisions. The experience and expertise that seasoned developers have accumulated over time remains essential for effectively directing AI agents.
What changes is how we apply that expertise. Instead of spending days writing boilerplate code or researching implementation details, developers can focus on the high-value activities: understanding user needs, designing robust systems and ensuring that solutions meet both functional and operational requirements.
The Evolution of Development Practices
As AI coding tools become more sophisticated, we’ll see a fundamental shift in how software development teams operate. The teams that succeed will be those that embrace structure while capitalizing on AI’s capabilities, not those that abandon proven practices in favor of quick wins.
This means maintaining design reviews, code reviews and all the collaborative practices that make software development successful. But it also means getting design documents in minutes instead of days, implementing complex features in hours instead of weeks and having more time to focus on building things the right way.
The future of software development isn’t about choosing between human expertise and AI assistance. It’s about combining them in ways that make both more effective. Spec-driven development provides a framework for doing exactly that, ensuring that as we build faster, we also build better.
