Beyond Vibe Coding: A Look at Kiro and Spec-Driven Development

A few years ago, using AI for coding mostly meant asking a chatbot a question, copying whatever it produced, and then spending a while making it fit your project. These days, AI coding tools can understand large parts of a codebase, edit several files at once, run tests, and carry a feature through to the end. People call this agentic coding, and I touched on the idea in my earlier article on Agentic AI.

What caught my attention about Kiro is a slightly different question. What happens if we stop asking AI to just write code, and instead give it a proper development process to follow?

A quick note before I go on: I haven't built a full project with Kiro yet. This article comes from reading through the AWS and Kiro documentation and comparing it with my experience of other AI coding tools, so treat it as a first look rather than a review.

Vibe coding has made building software far more approachable. You describe an idea, let the model generate something, run it, tweak it, and repeat. For a quick prototype, that's hard to beat.

It gets shakier as the project grows. The model misreads a requirement. A change in one module quietly breaks another. A decision you made forty messages ago gets buried in the chat history, and at some point you catch yourself asking what exactly you decided, and why.

Kiro's approach is to slow down before the coding starts. Instead of going straight from prompt to code, it walks an idea through requirements, design, and implementation tasks first.

Kiro is an AI-powered development environment built around agentic development, and AWS describes it as an agentic coding service that turns prompts into detailed specs, then into working code, docs, and tests. You can use it as an IDE built on a VS Code foundation, and it also comes with a CLI and a web version, which Kiro's docs currently label as a preview. On top of the familiar development workflow, it adds specs, steering, hooks, MCP support, custom agents, and skills.

Plenty of tools can generate code, so that isn't the part that interests me. What interests me is how Kiro gives the AI context and structure.

Say I want to add authentication to an app. A typical AI workflow goes: prompt, generate code, test, fix, prompt again, and repeat. Kiro's spec workflow puts more steps around that: idea, requirements, design, tasks, implementation, verification.

A spec breaks the feature into user stories with acceptance criteria, writes a technical design, and then splits the work into discrete implementation tasks. The workflow produces three main artifacts: requirements.md, design.md, and tasks.md.

So the AI isn't handed "build authentication" and left to guess. It first works out what the system should do, then how to build it, then what exactly needs implementing. That's much closer to how software teams have always worked. And because the reasoning lives in plain files instead of a chat log, it goes some way toward answering the "what did we decide?" problem from earlier.

More structure doesn't have to mean more prompting. Steering lets you keep persistent project knowledge in Markdown files under .kiro/steering/. That can cover coding conventions, architecture patterns, preferred libraries, testing expectations, and project-specific standards. Kiro can also generate starter steering files for your product, tech stack, and project structure by analyzing your repository.

This targets something that gets tiring with AI coding tools: explaining the same conventions in every new session. Use this architecture. Follow this naming style. Don't add another library for this. With steering, that knowledge becomes part of the project instead of something you retype.

Hooks run actions automatically when something happens in your session, such as a file being saved, a tool being invoked, or a task completing. The action can be a shell command or a prompt for the agent.

That opens up some practical setups. You could run a formatter, linter, or type check every time a file is saved, or have the agent update documentation after a task finishes. It's a small shift, but it changes how working with the agent feels. "Now run the tests," "now check the formatting," and "now update the docs" stop being things you say and become part of the workflow.

Generative AI is good at producing content. Agentic AI goes a step further: it reasons through a task, uses tools, and takes actions toward a goal. Kiro applies that idea to software development. The agent works with your project, carries out tasks, and responds to events. MCP (Model Context Protocol) lets it connect to external tools and data sources.

One way I think about the progression is: an LLM, then a coding assistant, then an agent that understands your codebase, then one that can use tools, and finally one that works inside a structured development process.

I don't think "can AI write the code?" is the real question, because it clearly can. The harder question is who decides what should be built, which constraints matter, and whether the result is actually correct.

Kiro's workflow seems built with that in mind. You review and iterate on the plan first, and only when you're ready do you ask Kiro to implement each step. AI can take on a lot of the implementation, but requirements, architecture, trade-offs, security, and validation still need a human paying attention. The developer's role may shift from writing every line to defining, reviewing, and guiding the system that writes them.

The biggest takeaway for me isn't a single feature. It's that AI-assisted development seems to be moving from code generation toward development workflows.

"Write this function" and "understand this requirement, plan the implementation, follow our conventions, implement the tasks, test the result, and let me review it" are very different requests. The second needs more than a capable model. It needs context, structure, tools, constraints, and feedback loops, which is where specs, steering, hooks, and MCP come in.

I don't think structured development will simply replace vibe coding. Sometimes you just want to open an assistant and experiment, and that's fine. But for larger projects, having requirements, architecture, tasks, conventions, and automated checks written down could make AI-assisted work much easier to reason about. My next step is to run a small feature through the full spec flow and see how it holds up in practice.

If you've used Kiro's specs on a real project, I'd be interested to hear where the process actually helped, and where it got in the way.