Kiro: Beyond AI Coding — Building Software with an Agent

AI coding has changed how quickly we can turn an idea into working software.

A few prompts can generate a UI, create an API, write database models, and even fix bugs.

But there is a problem we don't talk about enough.

Writing code is only one part of building software.

A real project also requires understanding requirements, making architectural decisions, maintaining coding standards, testing changes, working with external tools, documenting decisions, and keeping everything consistent as the project grows.

This is where I started looking at Kiro differently.

Kiro isn't just about asking an AI to write code. Its approach is centered around giving an AI agent more of the context, structure, tools, and workflow required to participate in software development.

Kiro describes itself as an AI-powered development environment for building software from prototype to production. Today, its agent capabilities extend across the IDE, CLI, Web, and Mobile through a unified agent harness. (Kiro)

And that leads to a more interesting question:

What happens when an AI agent is given an engineering process, rather than just a prompt?

The Problem With "Just Build This"

Imagine telling an AI:

"Build a scholarship management platform for students."

The model can start generating code almost immediately.

But what exactly should it build?

Should students be able to edit an application after submitting it?

Who verifies the information?

What happens when a student isn't eligible?

What should teachers see?

How should authentication work?

What happens to uploaded documents?

What database structure should be used?

What testing strategy should the project follow?

These decisions exist whether we document them or not.

When they're not documented, they often end up scattered across conversations, code, and the developer's memory.

That's where the difference between code generation and software engineering with agents becomes important.

From Prompt-Driven Development to Spec-Driven Development

One of Kiro's core ideas is spec-driven development.

Instead of immediately jumping from:

Idea → Code

Kiro introduces a more structured path:

Idea
  ↓
Requirements
  ↓
Design
  ↓
Tasks
  ↓
Implementation
  ↓
Verification

A specification becomes an artifact that captures what you're actually trying to build.

Kiro's current spec workflow can generate requirements, a technical design, and implementation tasks, which the agent can then use to carry out the work. (Kiro)

This changes the role of the prompt.

The prompt is no longer just:

"Write the feature."

It can become:

"Understand the feature, clarify the requirements, design the solution, break it into tasks, and then implement it."

That extra structure matters especially when a feature is large enough that the first implementation isn't necessarily the right implementation.

But an Agent Needs Context

Even a powerful model doesn't automatically know how your project works.

Suppose your team has decided:

• all APIs use a particular error format
• React components follow a specific structure
• tests use a certain framework
• database access follows a particular pattern
• pull requests follow a specific template

You could explain all of this every time you start a conversation.

Or you could make that knowledge part of the project.

That's what Kiro's Steering capability is designed for.

Steering uses Markdown files to give Kiro persistent knowledge about the project's conventions, architecture, technologies, testing practices, and other standards. These can live under .kiro/steering/. (Kiro)

For example:

.kiro/
└── steering/
    ├── product.md
    ├── architecture.md
    ├── coding-standards.md
    └── testing.md

Now the repository doesn't only contain source code.

It also contains instructions about how that source code should be developed.

That's a powerful idea.

The Repository Starts Carrying Engineering Knowledge

Think about a normal project.

src/
tests/
package.json
README.md
...

Now imagine:

project/
│
├── src/
├── tests/
├── README.md
│
└── .kiro/
    ├── steering/
    ├── specs/
    ├── hooks/
    └── agents/

The .kiro directory can become a place where important parts of the development workflow live alongside the project.

Kiro's documentation describes project-level configuration in .kiro/ as shared across its different surfaces, allowing the same steering, specs, agents, hooks, and MCP configuration to travel with the repository. (Kiro)

So the project doesn't just contain:

What the software is.

It can also contain:

How the agent should work with the software.

Then Comes Automation

Now imagine the agent modifies a file.

Normally, you might manually:

1. run a formatter
2. run tests
3. check types
4. inspect the change
5. update related files

What if some of those actions could happen automatically?

That's where Kiro Hooks come in.

Hooks can trigger shell commands or agent prompts when defined events occur. Depending on the environment, events can include file changes, tool execution, prompt submission, and spec-task execution. (Kiro)

For example:

Agent modifies component
        ↓
     File saved
        ↓
      Hook
        ↓
Run validation
        ↓
   Test results

Or:

New component created
        ↓
      Hook
        ↓
Generate/update test

The important concept isn't simply automation.

It's turning development rules into executable workflows.

Instead of repeatedly telling the agent:

"Remember to run the tests."

you can create a workflow where the appropriate action happens automatically.

An Agent Doesn't Have to Work Alone

Modern software development rarely happens inside one editor.

You may need:

• GitHub
• databases
• APIs
• documentation
• cloud services
• monitoring tools
• internal systems

This is where MCP — Model Context Protocol — becomes important.

Kiro supports MCP servers to connect agents with external tools and data sources. (Kiro)

Conceptually:

                  Kiro Agent
                      │
        ┌─────────────┼─────────────┐
        ↓             ↓             ↓
     Project         MCP         External
      Files        Servers        Tools
        │             │             │
        └─────────────┼─────────────┘
                      ↓
               Development
                 Workflow

The agent therefore doesn't have to be limited to:

Read code → generate code.

It can become part of a larger tool-using workflow.

Skills, Custom Agents and Sub-Agents

As projects become more complex, one general-purpose agent may not be enough.

Kiro also provides mechanisms such as Skills, Custom Agents, and Sub-agents.

Skills can package reusable instructions and knowledge.

Custom agents can be configured for specialized workflows.

Sub-agents can handle focused work in parallel.

Kiro's current documentation lists these alongside MCP, permissions, hooks, steering, and other agent capabilities. (Kiro)

This starts to resemble a small engineering team:

                    Main Agent
                        │
          ┌─────────────┼─────────────┐
          ↓             ↓             ↓
       Agent A       Agent B       Agent C
      Frontend        Tests        Backend

The important part is not the analogy itself.

The important part is delegation.

Instead of one conversation becoming responsible for everything, focused pieces of work can be separated.

One Agent, Multiple Surfaces

Another part of Kiro that I find particularly interesting is that the agent isn't tied to one interface.

Kiro currently provides an IDE, CLI, Web, and Mobile experience around a unified agent harness. (Kiro)

That means a workflow can look something like:

          Kiro Agent
               │
     ┌─────────┼─────────┐
     ↓         ↓         ↓
    IDE       CLI       Web
     │                   │
     └─────────┬─────────┘
               ↓
            Mobile

You might start designing a feature in the IDE.

Continue implementation through the CLI.

Run a longer task in Kiro Web.

Then check its progress from your phone.

The important idea is that these aren't necessarily separate agents with completely different project knowledge.

Kiro's unified harness is designed so capabilities and configuration can work across these surfaces. (Kiro)

Let's Put It All Together

Suppose we're building a Student Opportunity Platform.

Students can:

• discover scholarships
• check eligibility
• save opportunities
• submit applications
• track application status

Teachers can:

• create student profiles
• verify information
• manage applications

Instead of:

"Build the application."

we could approach it like this:

1. Define the product

What problem are we solving?

2. Create the specification

What exactly should the feature do?

Requirements
     ↓
Design
     ↓
Tasks

3. Add project knowledge

Tell Kiro:

Technology stack
Architecture
Coding standards
Testing strategy
Security rules

through steering.

4. Implement

Let the agent work through the defined tasks.

5. Connect tools

Use MCP when the agent needs external systems.

6. Automate repetitive work

Use hooks for validation, testing, documentation, or other recurring workflows.

7. Review

The developer remains responsible for understanding and approving the result.

The complete workflow becomes:

                 PRODUCT IDEA
                       │
                       ▼
                 SPECIFICATION
                       │
                       ▼
                  REQUIREMENTS
                       │
                       ▼
                     DESIGN
                       │
                       ▼
                    TASKS
                       │
                       ▼
                ┌──────────────┐
                │ KIRO AGENT   │
                └──────────────┘
                  │     │     │
                  ↓     ↓     ↓
              Steering MCP  Skills
                  │     │     │
                  └─────┼─────┘
                        ↓
                  IMPLEMENTATION
                        │
                        ↓
                      Hooks
                        │
                        ↓
                   VALIDATION
                        │
                        ↓
                     REVIEW

Now we're no longer talking about an AI that simply generates code.

We're talking about an AI agent operating inside a software development system.

The Developer's Role Is Changing

This doesn't mean developers become unnecessary.

Quite the opposite.

The developer increasingly becomes responsible for the things that require understanding the bigger picture:

What are we building?

Why are we building it?

What constraints matter?

What architecture should we use?

What should the agent be allowed to do?

How do we know the result is correct?

The shift looks something like this:

Traditional workflow

Developer
   ↓
Write
   ↓
Debug
   ↓
Test
   ↓
Repeat

versus an increasingly agentic workflow:

Developer
   ↓
Define intent
   ↓
Define constraints
   ↓
Plan
   ↓
Delegate
   ↓
Review
   ↓
Verify

The developer isn't disappearing from the loop.

The developer is moving higher in the loop.

The Bigger Picture

When Kiro launched, its central idea was to bring more structure to AI-assisted development through concepts such as specs and hooks, rather than stopping at rapid prototype generation. (Kiro)

Since then, the platform has expanded considerably.

The current Kiro ecosystem includes:

• Spec-driven development
• Steering
• Hooks
• MCP
• Skills
• Custom agents
• Sub-agents
• Permissions
• CLI workflows
• Web-based autonomous development
• Multiple agent surfaces (Kiro)

Looking at these individually can make them feel like a collection of features.

But together, they point toward something bigger:

The development environment itself is becoming part of the agent.

That's the interesting shift.

From AI That Writes Code to AI That Understands the Workflow

The first wave of AI coding focused on a simple promise:

Write code faster.

The next wave is asking a broader question:

Can an AI agent participate in the entire software development workflow?

That requires much more than code generation.

It requires:

Context → What does this project look like?

Intent → What are we actually trying to build?

Planning → What needs to happen?

Tools → What systems can the agent interact with?

Execution → Can it actually perform the work?

Automation → Can repetitive workflows happen automatically?

Verification → How do we know the result is correct?

Kiro's approach brings many of these pieces into one development environment.

And perhaps that's the most interesting way to look at Kiro:

It's not just an AI that helps you write software. It's an attempt to make the development environment itself agentic.

The future of AI-assisted development may therefore look less like:

Prompt → Code

and more like:

Intent → Context → Plan → Agent → Tools → Execution → Verification → Software.

And that's a much bigger change than simply generating code faster.