Kiro is an AI coding tool from AWS that AWS itself describes as built "from the ground up" for spec-driven development. In AWS's words, it helps developers "turn prompts into executable specs, validate code correctness to find bugs unit tests miss, and build across large codebases with parallel agents." A few things are worth pinning down, because they shape everything else: - It's an agentic environment, not just autocomplete. Kiro ships as an IDE and a CLI. Alongside inline suggestions and chat, it runs agents that plan and implement changes across a codebase, and it supports MCP (Model Context Protocol) for connecting to external tools and data. - It replaces Amazon Q Developer. AWS has stated Kiro is the successor to Amazon Q Developer. Per AWS's own end-of-support announcement, new Q Developer signups are blocked as of May 15, 2026, and Q Developer IDE plugins and paid subscriptions reach end of support on April 30, 2027 — a roughly 12-month transition window. Kiro launched in preview in 2025 and rolled out internationally in 2026. - It routes across multiple models. Kiro isn't tied to a single model. Its documentation lists several — Claude Opus, Sonnet, and Haiku, plus open-weight options — and an "Auto" mode that "picks the best model for the task based on complexity, factoring in model quality, latency, and cost." As an AWS product it draws on Amazon Bedrock as the model layer, which in principle lets teams route to Bedrock-available models for cost, capability, or compliance reasons. The consistent through-line: Kiro is trying to put structure around agentic coding rather than let an agent free-associate its way through a codebase.

In practice, a Kiro workflow looks roughly like this: - Prompt to spec. You give Kiro a natural-language description of what you want. It generates the requirements/design/tasks spec — expanding your prompt into structured detail. (One frequently cited user reaction: Kiro wrote out user stories "like a product manager and capture[d] so many details that I didn't even need to mention.") - Review and edit the spec. You correct the requirements and design before code exists. This is the leverage point — mistakes caught here are cheap. - Agents implement the tasks. Kiro works through the task list, generating code across the codebase, with support for parallel agents on large projects. - Correctness checking. Beyond example-based unit tests, Kiro emphasizes property-based testing — asserting "rules that must hold across all inputs instead of checking a few examples" — to catch edge cases that a handful of hand-written tests miss. Two other pieces round it out: - Agent Hooks are event-driven automations that fire on triggers like file save or commit — for example, regenerating tests or updating docs automatically when a file changes. This is what lets some of the workflow run in the background rather than on every manual prompt. - Steering files give Kiro persistent, project-level context — your architecture, conventions, and constraints — so the agent's output stays consistent with how your codebase actually works, across sessions. Together, these are Kiro's answer to the two failure modes of naive agentic coding: drift from architecture (steering files + specs) and silent gaps in correctness (property-based tests + hooks).

Kiro & Spec-Driven Development: AWS's Agentic IDE, Explained

Q: When does spec-driven development help — and when is it overkill?

It helps when: - The change is large or multi-step — a new feature, a refactor across modules, anything where getting the architecture right matters more than typing speed. - Multiple people need shared intent. A spec is a reviewable artifact a team can agree on before code is written, which beats reconstructing intent from a diff. - Edge cases and correctness are load-bearing — regulated domains, data integrity, systems that break expensively in production. - You're working in a large or unfamiliar codebase, where drift from existing conventions is a real risk. It's overkill when: - The task is small and local — a one-line fix, a quick script, a tweak to one function. Writing a spec for that is ceremony. - You're exploring or prototyping, where the whole point is to poke at ideas fast and throw most of them away. - The requirements are genuinely unknown and can only be discovered by building. Sometimes the fastest way to learn what you want is to write throwaway code, not a spec. The mature stance is that spec-first and code-first are modes, not tribes. Good engineers already switch between them — a napkin sketch for a big feature, straight-to-code for a hotfix. Kiro's contribution is making the spec-first mode a first-class, tool-supported path rather than a discipline you have to impose by hand.

In short: Kiro is AWS's agentic development environment (an IDE and CLI) built for spec-driven development — a workflow where you first turn a prompt into a structured, human-readable specification (requirements, design, tasks), and the agent writes code against that spec. The spec becomes the source of truth; the code is a build artifact. That inverts the prompt-to-code model of tools like Cursor, GitHub Copilot, and the Amazon Q Developer that Kiro replaces. It's a genuinely useful idea with real tradeoffs — worth understanding on its merits, not the hype.

This is an explainer, not a pitch. We'll define the terms, describe how Kiro works, compare it fairly to the alternatives, and say plainly where it helps and where it's overkill. At the end, a short honest note on how NewGenApps works — because we build in this spec-first, agentic paradigm ourselves, with our own toolchain.

Quick disambiguation. This page is published by NewGenApps (New Generation Applications Pvt Ltd, newgenapps.com), a senior AI delivery firm. We are not affiliated with Newgen Software Technologies Ltd (newgensoft.com, the NewgenONE low-code/ECM platform) or with newgenapps.io. And we are not affiliated with AWS — this is an independent explainer.

What is Kiro?

Kiro is an AI coding tool from AWS that AWS itself describes as built "from the ground up" for spec-driven development. In AWS's words, it helps developers "turn prompts into executable specs, validate code correctness to find bugs unit tests miss, and build across large codebases with parallel agents."

A few things are worth pinning down, because they shape everything else:

It's an agentic environment, not just autocomplete. Kiro ships as an IDE and a CLI. Alongside inline suggestions and chat, it runs agents that plan and implement changes across a codebase, and it supports MCP (Model Context Protocol) for connecting to external tools and data.
It replaces Amazon Q Developer. AWS has stated Kiro is the successor to Amazon Q Developer. Per AWS's own end-of-support announcement, new Q Developer signups are blocked as of May 15, 2026, and Q Developer IDE plugins and paid subscriptions reach end of support on April 30, 2027 — a roughly 12-month transition window. Kiro launched in preview in 2025 and rolled out internationally in 2026.
It routes across multiple models. Kiro isn't tied to a single model. Its documentation lists several — Claude Opus, Sonnet, and Haiku, plus open-weight options — and an "Auto" mode that "picks the best model for the task based on complexity, factoring in model quality, latency, and cost." As an AWS product it draws on Amazon Bedrock as the model layer, which in principle lets teams route to Bedrock-available models for cost, capability, or compliance reasons.

The consistent through-line: Kiro is trying to put structure around agentic coding rather than let an agent free-associate its way through a codebase.

What is spec-driven development?

Spec-driven development is a workflow where a written specification — not a prompt, and not the code itself — is the primary artifact you edit and version. You describe what the system should do; an agent proposes how; and only after that agreement does code get generated. If you want a change, you change the spec and regenerate, rather than nudging the code directly.

In Kiro, that spec is typically split into three linked parts:

Requirements — what the feature must do, written as structured, testable statements rather than a loose paragraph. Kiro uses EARS (Easy Approach to Requirements Syntax), a notation originally developed for safety-critical and aerospace systems, to keep each requirement unambiguous and checkable. That precision is the point: the agent codes against a specification, not an open-ended request.
Design — the system design and architectural decisions that satisfy those requirements. This is where the reasoning lives, documented explicitly so a teammate (or the agent, later) can see why something was built a certain way.
Tasks — a sequenced list of implementation steps derived from the design, which agents then work through.

The underlying claim is straightforward: agentic coding without formal structure tends to drift. An agent given only a prompt can wander from the intended architecture, quietly miss edge cases, and produce code that demos well but buckles under production load. A spec is an attempt to separate thinking from doing — to force the hard decisions to the surface before thousands of lines get written on a shaky foundation.

This isn't a new insight. It's the old wisdom that "the requirements are the hard part" applied to a world where writing the code is suddenly cheap. When code is expensive, you skimp on specs to save effort. When an agent can generate code in minutes, the spec is the only place your judgment still compounds.

How does Kiro work?

In practice, a Kiro workflow looks roughly like this:

Prompt to spec. You give Kiro a natural-language description of what you want. It generates the requirements/design/tasks spec — expanding your prompt into structured detail. (One frequently cited user reaction: Kiro wrote out user stories "like a product manager and capture[d] so many details that I didn't even need to mention.")
Review and edit the spec. You correct the requirements and design before code exists. This is the leverage point — mistakes caught here are cheap.
Agents implement the tasks. Kiro works through the task list, generating code across the codebase, with support for parallel agents on large projects.
Correctness checking. Beyond example-based unit tests, Kiro emphasizes property-based testing — asserting "rules that must hold across all inputs instead of checking a few examples" — to catch edge cases that a handful of hand-written tests miss.

Two other pieces round it out:

Agent Hooks are event-driven automations that fire on triggers like file save or commit — for example, regenerating tests or updating docs automatically when a file changes. This is what lets some of the workflow run in the background rather than on every manual prompt.
Steering files give Kiro persistent, project-level context — your architecture, conventions, and constraints — so the agent's output stays consistent with how your codebase actually works, across sessions.

Together, these are Kiro's answer to the two failure modes of naive agentic coding: drift from architecture (steering files + specs) and silent gaps in correctness (property-based tests + hooks).

How is Kiro different from Cursor, Copilot, or Amazon Q?

The cleanest way to see the difference is the direction of the workflow. Most popular AI coding tools are code-first: you prompt, the tool edits code, you iterate on the code. Kiro is spec-first: you prompt, the tool produces a spec, you iterate on the spec, and code is generated from it.

That's a real philosophical split, and neither direction is universally "better" — they optimize for different things.

Dimension	Spec-driven (Kiro)	Prompt-to-code (Cursor, Copilot, Q Developer)
Primary artifact	The spec (requirements/design/tasks)	The code, edited directly
Where you catch mistakes	Before code exists, in review of the spec	After code exists, by reading diffs
Best fit	Larger features, multi-step changes, teams needing shared intent	Quick edits, exploration, "make this function do X"
Overhead	Higher up front — you write and review a spec	Lower up front — you just prompt
Risk it targets	Architectural drift, missed edge cases	Slow typing, boilerplate, context switching
Team coordination	Spec is a shared, reviewable source of truth	Coordination happens in PRs and conversation

A fair reading: Cursor and Copilot are superb at velocity in the small — inline completion, fast local edits, staying in flow. Kiro is betting on discipline in the large — that for substantial features, the time spent nailing a spec pays back by preventing rework. Amazon Q Developer sat closer to the Cursor/Copilot end; Kiro is AWS deliberately moving toward the spec-first end.

None of these is a lock-in-free utopia, and none is strictly superior. Cursor and Copilot also support agentic, multi-file workflows, and both are widely used and mature. Other credible tools in this space include Claude Code, Windsurf, Aider, and Zed's agent features. The honest takeaway is that spec-driven development is a methodology — one you can practice, to a degree, in several of these tools — and Kiro is the most opinionated productization of it so far.

When does spec-driven development help — and when is it overkill?

It helps when:

The change is large or multi-step — a new feature, a refactor across modules, anything where getting the architecture right matters more than typing speed.
Multiple people need shared intent. A spec is a reviewable artifact a team can agree on before code is written, which beats reconstructing intent from a diff.
Edge cases and correctness are load-bearing — regulated domains, data integrity, systems that break expensively in production.
You're working in a large or unfamiliar codebase, where drift from existing conventions is a real risk.

It's overkill when:

The task is small and local — a one-line fix, a quick script, a tweak to one function. Writing a spec for that is ceremony.
You're exploring or prototyping, where the whole point is to poke at ideas fast and throw most of them away.
The requirements are genuinely unknown and can only be discovered by building. Sometimes the fastest way to learn what you want is to write throwaway code, not a spec.

The mature stance is that spec-first and code-first are modes, not tribes. Good engineers already switch between them — a napkin sketch for a big feature, straight-to-code for a hotfix. Kiro's contribution is making the spec-first mode a first-class, tool-supported path rather than a discipline you have to impose by hand.

Where we land

At NewGenApps, we build in this spec-first, agentic paradigm — because we think the core idea is right: for anything that has to survive production, the judgment belongs in the specification, and the code is a build artifact downstream of it. That conviction predates Kiro; the tool is a well-executed expression of a direction the field has been moving for a while. (For how this shows up in our delivery, see How we work.)

Our own toolchain is Claude Code plus a library of internal skills — that's our delivery practice, and it's where our spec-first workflow actually runs. (More on that in How we build.) But the deliberate point is model- and vendor-flexibility: nothing we build locks a client into one model or one cloud. Where a client already runs on AWS, Amazon Bedrock's routing across models is a reasonable place to land; where they don't, we don't push them there. We reach for the tools that fit the problem, and we're careful that none of them becomes a trapdoor.

We're not an AWS partner, we hold no badge we haven't earned, and we don't sell any vendor's roadmap. What we sell is judgment and verified delivery — production AI, proven. Whatever generates the code, the part that matters is the same part Kiro is built around: getting the specification and the correctness right. That's what we check, with the same rigor we bring to any agentic system — see our AI evaluation harness for how we prove a system actually works before it ships.

If you're weighing spec-driven development for real work, the useful question isn't "Kiro or Cursor?" It's "where in my workflow does structure pay for itself, and where is it friction?" Answer that honestly and the tool choice mostly follows.

Book an AI working session