🛠️ How to Build an AI-Powered IDE Like Kiro from Scratch

Tools like Kiro feel almost magical — you describe what you want, and an AI agent reads your files, writes code, runs commands, and iterates until the job is done. But there’s no magic here. It’s a set of well-defined components working together. This guide breaks down exactly how to build one from scratch.

This is a technical deep-dive. You won’t have a production IDE by the end of it, but you will understand every layer of the architecture and have working code for each piece.

🏗️ The Architecture Overview

An AI IDE like Kiro is made up of five core layers:

Code Editor — where the user writes and views code
File System Interface — read, write, create, and delete files
Terminal / Shell — run commands and capture output
AI Agent — the brain that understands instructions and orchestrates everything
Tool Layer — the bridge between the AI and the other components

The AI agent doesn’t write code directly into your editor by magic. It calls tools — functions like read_file, write_file, run_command — and the results feed back into its context so it can decide what to do next. That loop is the entire foundation of how agentic IDEs work.

User Input
    ↓
AI Agent (LLM)
    ↓
Tool Call (read_file / write_file / run_command)
    ↓
Tool Result → back to AI Agent
    ↓
Next Action or Final Response

🖊️ Layer 1: The Code Editor

The fastest path to a working editor is to build on top of Monaco Editor — the same editor engine that powers VS Code. It’s open source and runs in a browser.

<!DOCTYPE html>
<html>
<head>
  <title>My AI IDE</title>
  <style>
    #editor { width: 100%; height: 600px; border: 1px solid #333; }
  </style>
</head>
<body>
  <div id="editor"></div>

  <script src="https://cdn.jsdelivr.net/npm/monaco-editor@0.44.0/min/vs/loader.js"></script>
  <script>
    require.config({ paths: { vs: 'https://cdn.jsdelivr.net/npm/monaco-editor@0.44.0/min/vs' }});
    require(['vs/editor/editor.main'], function () {
      const editor = monaco.editor.create(document.getElementById('editor'), {
        value: '// Start coding here\n',
        language: 'javascript',
        theme: 'vs-dark',
        fontSize: 14,
      });

      // Expose editor globally so other components can read/write content
      window.getEditorContent = () => editor.getValue();
      window.setEditorContent = (code) => editor.setValue(code);
    });
  </script>
</body>
</html>

This gives you a full-featured code editor with syntax highlighting, autocomplete, and theming in under 30 lines.

📁 Layer 2: File System Interface

The file system layer is a simple Python backend (using Flask) that exposes read and write endpoints. The AI agent calls these to interact with your project files.

from flask import Flask, request, jsonify
import os

app = Flask(__name__)
BASE_DIR = "/path/to/your/project"  # Restrict access to project folder

def safe_path(filepath):
    """Prevent directory traversal attacks."""
    full_path = os.path.realpath(os.path.join(BASE_DIR, filepath))
    if not full_path.startswith(os.path.realpath(BASE_DIR)):
        raise ValueError("Access denied: path outside project directory")
    return full_path

@app.route('/files/read', methods=['POST'])
def read_file():
    filepath = request.json.get('path')
    try:
        with open(safe_path(filepath), 'r') as f:
            return jsonify({"content": f.read()})
    except Exception as e:
        return jsonify({"error": str(e)}), 400

@app.route('/files/write', methods=['POST'])
def write_file():
    filepath = request.json.get('path')
    content = request.json.get('content')
    try:
        full_path = safe_path(filepath)
        os.makedirs(os.path.dirname(full_path), exist_ok=True)
        with open(full_path, 'w') as f:
            f.write(content)
        return jsonify({"success": True})
    except Exception as e:
        return jsonify({"error": str(e)}), 400

@app.route('/files/list', methods=['POST'])
def list_files():
    dirpath = request.json.get('path', '.')
    try:
        entries = os.listdir(safe_path(dirpath))
        return jsonify({"files": entries})
    except Exception as e:
        return jsonify({"error": str(e)}), 400

Notice the safe_path function — always restrict file access to the project directory. Without this, an AI agent could theoretically read or write files anywhere on your system.

💻 Layer 3: Terminal / Shell

The terminal layer runs shell commands and returns their output. This is how the AI can run tests, install packages, or execute scripts.

import subprocess

@app.route('/terminal/run', methods=['POST'])
def run_command():
    command = request.json.get('command')
    cwd = request.json.get('cwd', BASE_DIR)

    try:
        result = subprocess.run(
            command,
            shell=True,
            cwd=safe_path(cwd),
            capture_output=True,
            text=True,
            timeout=30  # Prevent runaway commands
        )
        return jsonify({
            "stdout": result.stdout,
            "stderr": result.stderr,
            "returncode": result.returncode
        })
    except subprocess.TimeoutExpired:
        return jsonify({"error": "Command timed out"}), 408
    except Exception as e:
        return jsonify({"error": str(e)}), 400

The timeout=30 is important — without it, a command like while true; do echo loop; done would hang your server forever.

🤖 Layer 4: The AI Agent

This is the core. The agent receives a user message, decides which tools to call, calls them, and loops until it has a final answer. We’ll use OpenAI’s function calling API (or any compatible LLM API) to drive this.

import openai
import json

client = openai.OpenAI(api_key="your-api-key")

# Define the tools the AI can call
tools = [
    {
        "type": "function",
        "function": {
            "name": "read_file",
            "description": "Read the contents of a file in the project",
            "parameters": {
                "type": "object",
                "properties": {
                    "path": {"type": "string", "description": "Relative path to the file"}
                },
                "required": ["path"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "write_file",
            "description": "Write or overwrite a file in the project",
            "parameters": {
                "type": "object",
                "properties": {
                    "path": {"type": "string"},
                    "content": {"type": "string"}
                },
                "required": ["path", "content"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "run_command",
            "description": "Run a shell command in the project directory",
            "parameters": {
                "type": "object",
                "properties": {
                    "command": {"type": "string"}
                },
                "required": ["command"]
            }
        }
    }
]

def call_tool(name, args):
    """Route tool calls to the actual backend functions."""
    import requests
    base = "http://localhost:5000"
    if name == "read_file":
        r = requests.post(f"{base}/files/read", json=args)
    elif name == "write_file":
        r = requests.post(f"{base}/files/write", json=args)
    elif name == "run_command":
        r = requests.post(f"{base}/terminal/run", json=args)
    else:
        return {"error": f"Unknown tool: {name}"}
    return r.json()

def run_agent(user_message):
    messages = [
        {"role": "system", "content": "You are an AI coding assistant. Use the available tools to read files, write code, and run commands to complete the user's request."},
        {"role": "user", "content": user_message}
    ]

    while True:
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=tools,
            tool_choice="auto"
        )

        message = response.choices[0].message

        # If no tool calls, we have a final answer
        if not message.tool_calls:
            return message.content

        # Process each tool call
        messages.append(message)
        for tool_call in message.tool_calls:
            name = tool_call.function.name
            args = json.loads(tool_call.function.arguments)

            print(f"  → Calling tool: {name}({args})")
            result = call_tool(name, args)

            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(result)
            })

🔗 Layer 5: Wiring It All Together

With all layers in place, the user flow looks like this:

# Simple CLI interface to test the agent
if __name__ == "__main__":
    print("AI IDE Agent — type your request\n")
    while True:
        user_input = input("You: ").strip()
        if not user_input:
            continue
        print("\nAgent thinking...\n")
        response = run_agent(user_input)
        print(f"Agent: {response}\n")

Try prompts like:

“Read main.py and explain what it does”
“Create a new file called utils.py with a function that reverses a string”
“Run the tests and tell me if they pass”

The agent will read files, write code, and run commands — all automatically.

🔒 Security Considerations

Building an agent that can read, write, and execute code on your machine is powerful but risky. A few essential safeguards:

Sandboxing: Restrict all file operations to the project directory (done above with safe_path).
Command allowlist: Consider only permitting known-safe commands like python, npm, git rather than arbitrary shell execution.
Human-in-the-loop: For destructive actions (deleting files, running migrations), require explicit user confirmation before the tool executes.
Timeouts: Always set timeouts on shell commands to prevent hangs.

This is exactly how Kiro’s Supervised Mode works — it pauses before applying file changes and lets you accept or reject each one.

🚀 Taking It Further

Once the core loop works, you can extend it step by step:

Spec Mode: Add a planning phase before execution — generate requirements, design, and task list before writing any code.
Streaming responses: Use SSE (Server-Sent Events) to stream the agent’s output to the UI in real time instead of waiting for the full response.
Hooks: Trigger agent actions automatically on file save or after commands complete.
Steering files: Inject persistent context (your stack, coding standards) into every agent call via system prompt additions.
Local LLM: Swap the OpenAI API for a locally running model via Ollama for complete privacy and zero API costs.

🔮 The Bigger Picture

What makes Kiro and tools like it feel powerful isn’t any single component — it’s the agentic loop. The LLM calls a tool, gets a result, decides what to do next, calls another tool, and repeats until the task is done. Every component you’ve built here — the editor, file system, terminal, and tool layer — is just giving that loop more ways to act on your codebase.

Once you understand the loop, building on top of it becomes intuitive. And now you do.

🏗️ The Architecture Overview#

🖊️ Layer 1: The Code Editor#

📁 Layer 2: File System Interface#

💻 Layer 3: Terminal / Shell#

🤖 Layer 4: The AI Agent#

🔗 Layer 5: Wiring It All Together#

🔒 Security Considerations#

🚀 Taking It Further#

🔮 The Bigger Picture#