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Kiro: Agentic AI development from prototype to production

Spec-driven agentic dev platform that turns a prompt into requirements, a design doc, and sequenced tasks before any code is written, then implements from the spec. Runs across IDE, CLI, web, and mobile; validates output with property-based tests.

Introduction

Most AI coding tools start typing immediately and leave you reverse-engineering what they meant. Kiro inverts the order: it forces a written spec — requirements, a design doc, and a task list — out of your prompt first, so the planning happens where you can still edit it cheaply, before a single line is generated.

What Sets It Apart
  • The spec is the unit of work, not the chat. You approve user stories, acceptance criteria, and a design before implementation, which keeps large multi-file changes anchored to intent instead of drifting.
  • It validates with property-based tests, not just example unit tests — surfacing edge cases that pass a happy-path check but break in production.
  • Parallel agents and persistent context carry across IDE, CLI, web, and mobile, so a plan started in one surface continues in another.
  • Model choice spans Claude, open-weight models, or an "Auto" mode that picks per task, rather than locking you to one backend.
Who It's For and the Trade-offs

Great fit if you work on real codebases where a wrong assumption costs hours, and you want the AI to commit to a reviewable plan before editing. The spec-first loop pays off most on multi-step features and team work where intent must be explicit. Look elsewhere if you want a quick inline autocomplete or a throwaway prototype — the upfront spec step is overhead when the task is small, and the deepest leverage assumes you're inside the AWS ecosystem.

Information

  • Websitekiro.dev
  • AuthorsAmazon Web Services (AWS)
  • Published date2025/07/15

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