Building Agent Momo: My Journey Creating an AI Coding Agent - Mini Cursor CLI Project

Akshay BhushanAkshay Bhushan
7 min read

The Vision

You know that feeling when you've got this awesome app idea buzzing in your head? Imagine telling it to an AI and then just... watching. No copy-pasting code snippets, no manual file creation, no switching between tools. Just pure autonomous development from idea to execution.

That's exactly what I set out to build with Agent Momo - an agentic AI system specialized in web development who handles everything, from that first spark of an idea all the way to launching the finished product.

What Makes Agent Momo Different?

Agent Momo is designed to be truly autonomous. It doesn't just generate code - it:

  • Plans project architecture and structure

  • Creates files and directories automatically

  • Writes production-ready code

  • Executes system commands and handles project setup

  • Debugs issues and optimizes code

  • Opens the final application for testing

The key difference? Agency. Agent Momo makes decisions, takes actions, and manages the entire development workflow without human intervention.

The Architecture: Building for Autonomy

Core Components:

Agent Momo is built on several key components that work together to enable autonomous operation:

# Available tools that Agent Momo can use
available_tools = {
    "run_system_command": run_system_command,
    "write_file": write_file,
    "read_file": read_file,
    "append_to_file": append_to_file,
    "open_file": open_file,
}

Structured JSON Communication:

One of the most critical design decisions was implementing structured JSON responses. Every interaction with Agent Momo follows a predictable format:

{
    "step": "action",
    "function": "write_file",
    "input": {
        "filename": "todo-app/index.html",
        "content": "<!DOCTYPE html>..."
    },
    "content": "Creating the main HTML structure for the TODO app."
}

This structure ensures reliable communication between the AI and the tool execution system, preventing the ambiguity that often plagues AI interactions.

Key Learnings: The Art of System Prompt

1. Specificity is Everything

My biggest learning was how crucial detailed system prompts are for autonomous systems. Generic prompts lead to generic, unreliable behavior. Here's what I discovered works:

❌ Vague Instruction:

"You are a coding assistant. Help users build web apps."

✅ Specific, Actionable Instruction:

"You are an advanced agentic AI, named Agent Momo, specialized in software development who helps users in building Web-Apps. 
Your goal is to autonomously assist with end-to-end coding tasks, including code generation, debugging and optimization."

2. Explicit Tool Usage Guidelines

The AI needed explicit instructions on how and when to use each tool. I learned to include detailed examples in the system prompt:

{
    "step": "action",
    "function": "write_file",
    "input": {"filename": "app.js", "content": "console.log('Hello');"},
    "content": "Creating the main JavaScript file."
}

3. Error Handling Instructions

Perhaps most importantly, I had to teach Agent Momo how to interpret different types of responses and continue working even when encountering expected "errors" (like trying to create a directory that already exists).

The Challenges: What I Learned the Hard Way

Challenge 1: The Mysterious Exit Code 256

The Problem: Agent Momo would stop working whenever it encountered exit code 256 from system commands, treating it as a fatal error.

The Investigation: I discovered that exit code 256 from mkdir commands simply means "directory already exists" - not an actual error that should halt execution.

The Solution: I refined both the tool implementation and system prompt:

def run_system_command(cmd: str):
    try:
        result = os.system(cmd)
        if result == 0:
            return f"✅ Command executed successfully"
        elif "mkdir" in cmd and result == 256:
            return f"ℹ️ Directory already exists - continuing..."
        else:
            return f"⚠️ Command executed with exit code {result}"
    except Exception as e:
        return f"❌ Command execution failed: {str(e)}"

Key Learning: Autonomous systems need nuanced error interpretation, not just binary success/failure logic.

Challenge 2: Inaccurate Model Responses

The Problem: The AI would sometimes provide malformed JSON, incorrect function parameters, or skip essential steps in the development process.

Root Causes I Identified:

  • Ambiguous instructions in the system prompt

  • Inconsistent examples

  • Missing validation for multi-parameter functions

  • Unclear success/failure criteria

The Solutions:

  • Parameter Validation: I implemented robust input handling that could process both single parameters and complex objects:
# Handle different input types gracefully
if isinstance(tool_input, dict):
    result = available_tools[tool](**tool_input)
elif isinstance(tool_input, str):
    result = available_tools[tool](tool_input)

  • Explicit JSON Format Requirements: I added detailed format specifications to the system prompt with multiple examples.

  • Iterative Prompt Refinement: Through extensive testing, I refined the system prompt to be more explicit about expected behaviors.

Key Learning: AI reliability comes from precise instructions, comprehensive examples, and robust error handling - not just hoping the model "figures it out."

Challenge 3: Multi-Parameter Function Calls

The Problem: Functions like write_file(filename, content) were failing because the AI was only passing one parameter.

The Root Cause: The system prompt didn't clearly specify how to handle functions requiring multiple parameters.

The Solution: I explicitly documented the required JSON structure:

IMPORTANT: For multi-parameter functions, use JSON objects:
{"filename": "path/to/file", "content": "your code here"}

The Exact Moment!!

1. The Magic of "Build me a TODO app"

The moment that truly sold me on Agent Momo's potential was incredibly simple. I typed:

"Build me a TODO app using HTML, CSS & JS"

What happened next was nothing short of magical. Within seconds, Agent Momo sprang into action:

🧠 Planning Phase: Agent Momo immediately analyzed the request and responded: "I'll create a TODO list app using HTML, CSS, and JavaScript. It will allow users to add, delete, and mark tasks as complete. Do you have any UI preferences (dark/light theme, minimal layout, animations, etc.)?"

💫 The Interactive Touch: This is where it got interesting. Agent Momo didn't just build a basic app - it asked for my preferences! When I mentioned I'd love some animations, it didn't miss a beat.

⚡ Lightning-Fast Execution: What followed was a blur of autonomous activity:

  • Created the project structure in milliseconds

  • Generated semantic HTML with proper accessibility attributes

  • Wrote beautiful CSS with smooth animations and transitions

  • Implemented JavaScript with add, delete, and complete functionality

  • Added delightful hover effects and task completion animations

  • Opened the finished application automatically

The entire process took less than 30 seconds.

2. Beyond Functional - It Was Beautiful

The TODO app Agent Momo created wasn't just functional - it was genuinely beautiful:

  • Smooth Animations: Tasks would slide in when added, fade out when deleted, and had a satisfying strikethrough animation when completed

  • Modern Design: Clean typography, proper spacing, and a color scheme that felt professional

  • Responsive Layout: Worked perfectly on both desktop and mobile

  • Micro-interactions: Buttons had hover states, inputs had focus animations, and the entire interface felt alive

The revelation: This wasn't just code generation - Agent Momo had design sensibilities.

3. True Conversational Development

The most impressive part wasn't the speed or even the quality - it was the conversation. Agent Momo asked questions like a real developer would:

  • "Do you want animations?" ✨

  • "Should I use a dark or light theme?"☀️/🌙

  • "Any specific features you'd like me to prioritize?" 🎯

When I said yes to animations, it didn't just add basic CSS transitions. It implemented:

  • Smooth task entry animations with easing functions

  • Hover effects that felt responsive and modern

  • Completion animations that provided satisfying user feedback

  • Loading states and micro-interactions throughout

Conclusion: The Dawn of Autonomous Development

Building Agent Momo taught me that we're on the cusp of a fundamental shift in how software gets built. The combination of advanced language models, structured communication protocols, and robust tool integration creates possibilities we're only beginning to explore.

The challenges I faced - from cryptic exit codes to inaccurate model responses - are solvable problems. With careful system design, iterative refinement, and a focus on autonomous decision-making, we can build AI systems that don't just assist with development but actively participate in it.

Key Takeaways:

  • System prompt engineering is an art that requires precision and iteration

  • Autonomous systems need nuanced error interpretation capabilities

  • Structured communication protocols are essential for reliability

  • The future of development is collaborative human-AI teams

Agent Momo is just the beginning. As these systems become more sophisticated, we'll see a new era of software development where ideas can be transformed into applications at the speed of thought!

Demo Video: Agent Momo in Action!

Github Repo:https://github.com/akshaybhushan26/Agent-Momo

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Written by

Akshay Bhushan
Akshay Bhushan