A 100–400 level guide to Kiro IDE, Kiro CLI, Kiro Agent, Specs, Steering, Hooks, and why this workflow matters for DevOps builders.
I’ve been exploring AI-assisted development through hands-on community sessions and experiments for a while now.
I started with Claude Code, then experimented with other tools like Amazon Q and the growing list of AI coding assistants that seem to appear every few months. I also had the chance to try Kiro in community settings, including an AWS User Group event and AWS Community Summit in 2025. The market is noisy. Cursor, Claude Code, Copilot, Windsurf, Amazon Q, Kiro — everyone is trying to answer the same question:
How do we build software faster with AI?
But as a DevOps engineer, that is not the only question I care about.
I do not just care whether AI can generate code.
I care whether it can help people think clearly, design properly, document decisions, test assumptions, and ship something maintainable.
That is why Kiro caught my attention.
Ten minutes into my first serious session, I realised Kiro was not just helping me write code. It was making me slow down in the right places before I wrote a single line.
That might sound strange in a world where most AI coding tools are selling speed. But the more I used Kiro, the more I felt that its real value was not only generation.
Its real value was structure.
Most AI coding tools help you write code faster.
Kiro helps you decide what should be built before the code exists.
That is the difference this article is about.
The Thesis
Kiro is the bridge between vibe coding and production engineering because it moves AI development from prompts to specs, context, automation, and agents.
Vibe coding is great for exploration.
It is useful when you want to test an idea, build a prototype, create a quick demo, or help non-developers experience what AI-assisted building feels like.
But production engineering needs more than a good prompt.
It needs requirements.
It needs architecture.
It needs repeatable standards.
It needs review loops.
It needs some kind of audit trail showing what was asked, what was designed, what was implemented, and why.
That is where Kiro becomes interesting.
It does not remove engineering discipline.
It tries to put discipline back into the AI coding workflow.
100 Level: Kiro as an AI IDE
At the 100 level, Kiro is easiest to understand as an agentic IDE.
It looks and feels familiar because it is built from the VS Code world. If you already use VS Code, the transition is not intimidating. Your mental model still works: files on the left, editor in the middle, terminal at the bottom, extensions, themes, keybindings, and project folders.
That matters more than people realise.
One of the biggest adoption barriers with new developer tools is not the feature list. It is the friction.
If a tool asks developers to abandon their environment, relearn their editor, reconfigure their extensions, and rebuild their workflow from scratch, many teams will never get past the first hour.
Kiro avoids a lot of that by feeling familiar.
You install it, sign in, import your existing VS Code-style setup, open a project folder, and start working.
But calling Kiro “just another AI-powered VS Code” misses the point.
The IDE is only the surface.
The deeper idea is that Kiro is designed around agentic development. The AI is not only there to autocomplete a line or answer a question in chat. It can reason about a task, plan the work, modify files, run commands, inspect results, and continue through a workflow.
That puts Kiro in the same broad category as other AI coding tools, but its personality is different.
Claude Code is great when I want a terminal-first coding partner.
Cursor is great when I want fast editor-based iteration.
Copilot is great when I want AI assistance inside an existing developer workflow.
But Kiro feels different because it starts before the code.
It asks:
- What are we building?
- What are the requirements?
- What is the design?
- What tasks need to happen before implementation begins?
Most tools optimise for generation.
Kiro optimises for structured delivery.
That is the mental shift.
200 Level: Specs Before Code
The most important Kiro concept is Specs.
This is the part that changes how you think about AI-assisted development.
Most AI coding workflows look like this:
- You type a prompt.
- The AI generates code.
- You review it.
- You fix whatever does not work.
- You prompt again.
That workflow is fast, but it is also unpredictable.
It works well for small tasks. It gets messy when the feature becomes more complex, when multiple files are involved, when architectural decisions matter, or when someone else needs to understand what happened later.
Kiro’s Spec workflow changes the sequence.
Instead of jumping straight to implementation, Kiro helps you work through three structured phases:
Phase 1: Requirements
You describe what you want to build.
Kiro turns that into a requirements.md file with user stories and acceptance criteria.
This is important because vague prompts create vague systems.
A requirement like “add a contact form” is not enough for serious development.
A better requirement asks:
- Who is using the form?
- What fields are required?
- What validation rules apply?
- What happens after submission?
- What error states should be handled?
- What does success look like?
Kiro makes those questions visible before implementation starts.
Phase 2: Design
Once the requirements are reviewed, Kiro generates a design.md file.
This is where the workflow starts to feel more like engineering and less like prompting.
The design can cover:
- component structure,
- API routes,
- data flow,
- validation logic,
- storage choices,
- sequence diagrams,
- error handling,
- security considerations.
For a simple resume-site contact form, this might include a frontend form component, client-side validation, a backend endpoint, an email notification flow, and spam protection.
For a larger application, this becomes even more valuable.
The design document gives both the developer and the AI a shared map.
Phase 3: Tasks
After the design is approved, Kiro generates a tasks.md file.
This breaks the work into discrete implementation steps.
A simple contact form Spec might produce tasks like:
- create the contact form component,
- add validation for name, email, and message,
- create the API endpoint,
- connect the frontend form to the API,
- add loading and error states,
- add tests,
- update documentation.
That matters because the AI is no longer improvising from one large prompt.
It is working from a task list tied back to reviewed requirements and design decisions.
The unit of work becomes the Spec, not the prompt.
That is the big idea.
A Simple Example: Adding a Contact Form to a Resume Site
Let’s make this practical.
Imagine the project is a simple resume website for a beginner-friendly hands-on session.
A normal vibe coding prompt might be:
Add a contact form to this resume website.
That might work.
But it leaves too much undefined.
With Kiro, I would start with something more intentional:
I want to add a contact form to my resume website.
The form should collect name, email, and message.
It should validate required fields, show success and error states,
and send the message to me by email.
Make it suitable for a beginner-friendly hands-on project.
Kiro can then turn that into a Spec structure like this:
.kiro/specs/contact-form/
├── requirements.md
├── design.md
└── tasks.md
That is a small example, but it shows the value clearly.
The feature is no longer just “AI generated code.”
It becomes a documented workflow:
- what the feature should do,
- how it should be designed,
- how it should be implemented,
- how it should be verified.
For students, community builders, and hands-on learners, that is powerful.
It teaches them not only how to ask AI for code, but how to think about software before building it.
For DevOps builders, it is also powerful for a different reason.
It creates traceability.
You can look back and see what the AI was asked to build, what design choices were made, and which tasks were executed.
That is much healthier than “I prompted it and it worked.”
300 Level: Repeatable Workflows with CLI, Steering, and Hooks
Once you understand Specs, the next level is making Kiro repeatable.
This is where Steering files, Hooks, and Kiro CLI become important.
Steering Files: Teaching Kiro Your Standards
Every team has preferences.
Use TypeScript strict mode.
Follow this folder structure.
Use this logging pattern.
Do not create broad IAM policies.
Validate inputs at the API boundary.
Use conventional commits.
Document public functions.
Without persistent context, you have to repeat those standards in every prompt.
That gets tiring quickly.
Steering files solve this by giving Kiro long-lived project context.
They are Markdown files that describe your product, your stack, your architecture, and your standards.
A project might include steering files like:
.kiro/steering/
├── product.md
├── tech.md
├── structure.md
├── security.md
└── api-standards.md
A product.md file might explain who the product is for and what problem it solves.
A tech.md file might define the stack: Next.js, TypeScript, AWS CDK, Lambda, DynamoDB, Bedrock, or whatever you are using.
A structure.md file might explain folder conventions and naming patterns.
A security.md file might define validation, authentication, authorization, secrets handling, and logging expectations.
This is where Kiro starts to feel less like a chatbot and more like a team member who has read the project handbook.
From a DevOps and cybersecurity perspective, this is a big deal.
The quality of AI-generated code depends heavily on context.
If the context is only whatever you typed in the last prompt, the results will vary.
If the context includes project standards, architecture rules, and security expectations, the output has a much better chance of matching the way your team actually builds.
Hooks: Automating the Repetitive Stuff
Hooks are Kiro’s automation layer.
They let you trigger agent actions based on workspace events.
For example:
- when a file is saved,
- when a Spec is updated,
- when a task is completed,
- before a commit,
- after tests run.
This opens up interesting workflows.
You could use hooks to:
- run the linter on save,
- generate or update tests when components change,
- check for hardcoded secrets before commit,
- update documentation after implementation,
- remind students to fix failing tests,
- surface security issues early.
A simple hook idea for a hands-on learning session might be:
When a React component is saved:
1. Check whether a matching test file exists.
2. If not, suggest a basic test.
3. Run the relevant test file.
4. Report failures clearly.
That creates a feedback loop.
Students do not have to remember every engineering habit manually. The environment nudges them toward better behaviour.
For teams, this is even more interesting.
You can move standards from “please remember this during code review” to “the environment checks this every time.”
That is a very DevOps way of thinking.
Do not rely only on heroics.
Build the workflow so the right thing happens by default.
Kiro CLI: Bringing the Agent to the Terminal
Kiro is not only an IDE story.
Kiro CLI matters because many real engineering workflows still happen in the terminal.
DevOps engineers live in terminals.
We SSH into environments, inspect logs, run scripts, check Git status, work inside containers, test deployments, manage infrastructure, and automate repetitive tasks.
An IDE-based assistant is useful, but a terminal-based agent fits naturally into how many builders actually work.
This is also where the Amazon Q connection matters.
If you used Amazon Q Developer CLI before, you will recognise the direction. AWS has been moving its terminal-based AI developer workflow toward Kiro CLI.
That matters because Kiro is not just an IDE experiment.
It is becoming part of a broader developer workflow: IDE, CLI, Specs, Steering, Hooks, and Agents.
For a DevOps builder, Kiro CLI becomes useful when you want to:
- inspect a repository from the terminal,
- understand unfamiliar code,
- generate or update scripts,
- work in remote environments,
- automate repetitive development tasks,
- connect AI assistance to real operational workflows.
The IDE is where the workflow is easiest to see.
The CLI is where it starts to feel operational.
400 Level: From Assisted Coding to Autonomous Delivery
At the 400 level, the question changes again.
It is no longer only:
Can AI help me write this code?
The better question is:
Can AI help move structured work through a delivery process?
That is where Kiro Agent becomes interesting.
The idea of an autonomous coding agent is not just that it writes files while you watch.
The more important idea is that it can take a structured task, use project context, follow standards, implement changes, run checks, and prepare work for review.
In a mature workflow, that could look like this:
- A feature starts as a Spec.
- Requirements are reviewed.
- Design is reviewed.
- Tasks are generated.
- The agent implements tasks.
- Hooks run validation, tests, and security checks.
- The agent prepares a pull request.
- Humans review the outcome.
That last point matters.
Autonomous does not mean unreviewed.
At least, it should not.
The future of AI-assisted development is not just better prompts.
It is better specs, better context, better workflows, and better review loops.
That is why Kiro’s Spec-first approach matters.
If an agent is going to make changes on your behalf, you need something stronger than a chat history.
You need artifacts.
You need a requirements document.
You need a design document.
You need a task list.
You need tests.
You need logs.
You need human review.
For regulated environments, enterprise teams, cybersecurity teams, and production engineering groups, this is where the conversation becomes serious.
AI-generated code is not automatically a problem.
Untraceable AI-generated code is the problem.
Kiro does not magically solve governance, but it gives you better building blocks:
- Specs for intent,
- Steering files for standards,
- Hooks for checks,
- CLI for operational workflows,
- Agent workflows for structured execution.
That is a much better foundation than prompt-and-pray.
Kiro vs Other AI Coding Tools
I do not think Kiro replaces every AI coding tool.
That is not the right framing.
Claude Code is still excellent for terminal-first workflows and deep codebase work.
Cursor is still excellent for fast editor-based iteration.
Copilot is still useful because it is already embedded in many developer environments.
Windsurf and other tools have their own strengths.
Kiro is different because it is trying to solve a slightly different problem.
It is not only asking:
How do we generate code faster?
It is asking:
How do we build from a reviewed specification?
That is why I see Kiro as especially interesting for DevOps builders, platform engineers, educators, and product builders.
It sits in the gap between playful AI coding and production-minded software delivery.
If you are prototyping something quickly on a weekend, the Spec workflow may feel like overhead.
But if you are building a feature that needs to be maintained, reviewed, tested, explained, or handed to someone else, that overhead starts to look like discipline.
And discipline is exactly what many AI coding workflows are missing.
Why This Matters for Hands-On Learning
One of the reasons I like Kiro is that it gives community builders and learners a better mental model.
The hard part is no longer getting AI to generate code.
That part is easy now.
The harder part is learning how to think clearly before generating code.
Beginners often jump straight into prompts like:
Build me an app.
Or:
Create a dashboard.
Or:
Make this website better.
Those prompts are not wrong, but they skip the thinking.
Kiro’s Specs create a natural pause.
Before the AI builds, the learner has to review what they are asking for.
They can see requirements.
They can challenge the design.
They can understand the implementation plan.
That is a much healthier way to teach AI-assisted building.
It also helps non-developers understand that software is not only code.
Software is decisions.
Software is trade-offs.
Software is structure.
Software is communication.
Kiro makes more of that visible.
Getting Started Checklist
If you are trying Kiro for the first time, I would keep the first session simple.
Do not start with your most complex production system.
Start with a small feature in a familiar project.
For example:
- add a contact form to a resume site,
- add a testimonials section,
- create a basic admin page,
- add search to a small app,
- generate tests for an existing component.
Then try this workflow:
- Install Kiro and sign in.
- Import your existing VS Code-style settings if available.
- Open an existing project folder.
- Generate basic Steering files.
- Create one Feature Spec.
- Review
requirements.md. - Review
design.md. - Review
tasks.md. - Let Kiro implement one task at a time.
- Check the result like an engineer, not like a passenger.
That last point is important.
Do not treat AI output as magic.
Treat it like a junior teammate who works incredibly fast, but still needs direction, standards, and review.
The DevOps Takeaway
For me, the interesting part of Kiro is not that it can write code.
Many tools can write code now.
The interesting part is that it changes the shape of the workflow.
It moves you from:
prompt → code → fix → prompt again
To something closer to:
idea → requirements → design → tasks → implementation → validation → review
That is much closer to how serious engineering already works.
It is also why I think Kiro belongs in the DevOps conversation.
DevOps was never only about tools.
It was about feedback loops, automation, collaboration, and shipping better software more reliably.
Kiro’s approach fits that mindset.
Specs give you clarity.
Steering files give you standards.
Hooks give you automation.
CLI gives you operational reach.
Agents give you a path toward more autonomous delivery.
The tool is still evolving, but the direction is clear.
AI coding is moving beyond autocomplete and chat.
It is moving toward structured, agentic workflows.
Where I’m Taking This Next
I started by using Kiro at an AWS User Group event and later at AWS Community Summit in 2025. That was enough for me to understand the workflow.
At first, I tested it on community-friendly ideas: resume sites, simple features, and guided build exercises that made the Spec workflow easy to explain.
But the real question was not whether Kiro could help me build a demo.
The real question was whether the same workflow could help me build something more serious.
That is what led me to Interview-Ops: an end-to-end AI interview analysis application using AWS, transcription, Bedrock analysis, scoring, structured feedback reports, and automated follow-up emails.
If you are building with AI tools, do not just prompt for code.
Start with specs.
The more serious your application becomes, the more important it is to know what you are building, why you are building it, how it should work, and how it will be reviewed later.
That is where Kiro starts to feel different.
In the next post, I will break down how I used Kiro to build Interview-Ops end to end — from requirements and architecture to AWS implementation, transcription, Bedrock analysis, scoring, feedback reports, and automated follow-up emails.
Resources
kiro.dev — download is free, no AWS account required
Introducing Kiro — the original launch post, good context on the design philosophy behind specs and hooks
Introducing Powers — explains why dynamic MCP loading matters and how Powers solves context rot
Teaching Kiro new tricks with steering and MCP — practical deep dive on using steering + MCP to handle custom libraries and DSLs
Specs documentation — full reference including the Design-First and Bugfix spec workflows
Kiro Powers marketplace — browse Figma, Stripe, Supabase, Datadog, Terraform and more
github.com/kirodotdev/Kiro — issue tracker and feedback repo
