The days of AI just autocompleting your code are fading fast. A new generation of tools is emerging that doesn’t just help you write code — it understands what you’re trying to build and drives the entire development process from idea to implementation. Kiro, Amazon’s AI-powered IDE, is one of the most capable tools in this new wave.

If you’ve been curious about Kiro but aren’t sure where to start, this guide walks you through the key features and how to use them to ship faster without sacrificing quality.

🤔 What is Kiro?

Kiro is an AI-powered development environment built on top of VS Code. It integrates a conversational AI agent directly into your IDE workflow. Unlike simpler copilot-style tools that suggest the next line of code, Kiro can handle multi-file tasks, navigate your entire codebase, run terminal commands, and reason through complex problems end-to-end.

Under the hood, it’s backed by Amazon Bedrock and uses powerful frontier models to power its agent. The key distinction: Kiro doesn’t just generate text. It acts.

🗂️ Vibe Mode vs. Spec Mode

Kiro offers two distinct ways to work, and understanding when to use each is the first step to working fast.

  • Vibe Mode (Chat): This is your conversational, exploratory mode. Great for quick questions, one-off fixes, asking about a function, or iterating on a small change. Think of it as pair programming with a knowledgeable colleague.
  • Spec Mode: This is where Kiro shines for larger features. You describe what you want to build, and Kiro works through a structured three-step process — Requirements → Design → Tasks — before writing a single line of code. It forces clarity upfront and breaks the work into trackable steps.

For anything beyond a small bug fix, Spec Mode is the smarter choice. It prevents the AI from going off in the wrong direction and gives you clear control points to review the plan before it touches your code.

✅ Using Spec Mode Step-by-Step

Here’s how a typical Spec Mode workflow looks in practice.

1. Describe the feature in plain language. In the chat input, switch to Spec Mode and describe what you want. You don’t need to be technical. Something like: “Add a dark mode toggle to the settings page that persists across sessions” is enough to get started.

2. Review the Requirements. Kiro will generate a structured list of requirements — what the feature needs to do, edge cases, and acceptance criteria. This is your chance to catch misunderstandings early. Edit, remove, or add requirements before moving on.

3. Approve the Design. Next, Kiro proposes a technical design: which files to create or modify, what data structures to use, and how components will interact. Review this to make sure it fits your architecture. If it suggests a pattern you don’t want, say so. It will adjust.

4. Work Through the Tasks. Finally, Kiro breaks the design into a numbered task list. You can execute tasks one at a time. After each one, Kiro writes the code, and you can review the diff before accepting or rejecting individual changes.

This workflow might feel slower than just asking the AI to “build the whole thing,” but it’s dramatically faster in the long run. You avoid the back-and-forth of fixing AI code that went off-track.

🤖 Autopilot vs. Supervised Mode

Kiro also lets you choose how much autonomy the agent has.

  • Autopilot Mode is the default. Kiro works end-to-end on a task, making file edits and running commands without stopping to ask. You can still review all changes and revert anything you don’t like.
  • Supervised Mode slows Kiro down intentionally. After every turn with file edits, it pauses and shows each code change as an individual diff — accept or reject per hunk. This is invaluable when working on sensitive code like authentication, database migrations, or core business logic.

Use Autopilot for feature scaffolding and boilerplate. Switch to Supervised when you’re touching anything critical.

🪝 Hooks: Automate the Boring Stuff

One of Kiro’s most underrated features is Hooks — automations that trigger agent actions when IDE events happen. A few practical examples:

  • On file save: Automatically run the linter and fix any errors.
  • After a task completes: Run the test suite to catch regressions immediately.
  • On every prompt: Remind the agent to follow your team’s coding standards.

Hooks are defined as simple JSON files. Here’s one that runs npm run lint every time you save a TypeScript file:

{
  "name": "Lint on Save",
  "version": "1.0.0",
  "when": {
    "type": "fileEdited",
    "patterns": ["*.ts", "*.tsx"]
  },
  "then": {
    "type": "runCommand",
    "command": "npm run lint"
  }
}

Set these up once and they run silently in the background — no more forgetting to lint before a commit.

🧭 Steering Files: Give Kiro Long-Term Context

By default, the AI only knows what’s in your conversation and what it reads from files. Steering files let you inject persistent context into every interaction. Store them in .kiro/steering/ as Markdown files.

Good things to put in a steering file:

  • Your tech stack and preferred libraries
  • Naming conventions and folder structure rules
  • Which patterns to use (and which to avoid)
  • Links to your API spec or data model

For example, a stack.md steering file that says “This is a Next.js 14 app using Tailwind CSS and Prisma. Never introduce new dependencies without asking” will shape every suggestion Kiro makes across the entire project.

🔮 The Bigger Picture

Kiro represents a meaningful step beyond the copilot era. Tools like GitHub Copilot changed how we write code line by line. Kiro — and tools like it — are changing how we think about and manage entire features. The developer’s job shifts from typing code to directing an autonomous agent, reviewing its output, and making the high-level calls.

That said, Kiro works best when you stay in the loop. The developers who get the most out of it aren’t the ones who hand everything off blindly — they’re the ones who use Spec Mode to stay sharp, review diffs carefully, and treat the AI as a fast, capable junior developer rather than an oracle.

The bottom line: if you’re still using AI just for autocomplete, you’re leaving a lot of speed on the table. Give Kiro a shot, and you might be surprised how much faster a well-directed AI can move.

The AI Series

  1. What is AI?
  2. 💻 The Rise of AI in Software Development
  3. 🤖 Rise of the AI Agents: The Next Wave of Automation
  4. The Rise of Vibe-Coding: How AI is Reshaping Software Development
  5. ⚡ How to Use Kiro AI for Faster Development (This Post)