Cursor's agent swarm suggests cheaper models can handle most coding when frontier models plan the work

Introduction

In this tutorial, you'll learn how to build a simple AI coding agent system that mimics the concept described in the article about Cursor's agent swarm. We'll create a basic system where one 'planner' AI coordinates multiple 'worker' AIs to complete coding tasks. This is a simplified version of the idea where cheaper models handle most of the work while a more capable model plans the approach.

This tutorial will teach you how to set up a basic AI agent framework using Python and the OpenAI API, giving you hands-on experience with how AI agents can collaborate to solve complex tasks.

Prerequisites

• Basic understanding of Python programming
• Python 3.7 or higher installed on your computer
• An OpenAI API key (you can get one from https://platform.openai.com/)
• Basic knowledge of how to install Python packages using pip

Why these prerequisites? Python is the language we'll use to build our AI agents. The OpenAI API key is necessary to access the AI models that will do the actual work. Understanding basic Python will help you follow along with the code examples.

Step-by-Step Instructions

Step 1: Set Up Your Python Environment

First, create a new directory for our project and set up a virtual environment to keep our dependencies isolated.

mkdir ai-coding-agents
 cd ai-coding-agents
 python -m venv venv
 source venv/bin/activate  # On Windows use: venv\Scripts\activate

Why? Using a virtual environment ensures that the packages we install don't interfere with other Python projects on your computer.

Step 2: Install Required Packages

Now install the necessary Python packages:

pip install openai python-dotenv

Why? The openai package lets us communicate with OpenAI's API, and python-dotenv helps us manage our API key securely.

Step 3: Create Your API Key File

Create a file called .env in your project directory:

OPENAI_API_KEY=your_actual_api_key_here

Why? Storing your API key in a separate file keeps it secure and prevents accidentally sharing it in public code repositories.

Step 4: Create the Main Agent System

Create a file called agent_system.py with the following code:

import os
from openai import OpenAI
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize OpenAI client
client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))

# Define our agent roles
class Planner:
    def __init__(self, client):
        self.client = client
        
    def plan_task(self, task_description):
        prompt = f"Plan how to approach this coding task: {task_description}. Provide a step-by-step plan."
        response = self.client.chat.completions.create(
            model="gpt-4-turbo",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.5
        )
        return response.choices[0].message.content


class Worker:
    def __init__(self, client, worker_type):
        self.client = client
        self.worker_type = worker_type
        
    def execute_task(self, plan, task):
        prompt = f"As a {self.worker_type}, implement the following task: {task}. Follow this plan: {plan}."
        response = self.client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.3
        )
        return response.choices[0].message.content


def run_agent_system(task):
    planner = Planner(client)
    worker1 = Worker(client, "code writer")
    worker2 = Worker(client, "code reviewer")
    
    # Step 1: Planner creates the plan
    plan = planner.plan_task(task)
    print("\n--- Planner's Plan ---")
    print(plan)
    
    # Step 2: Workers execute the plan
    print("\n--- Worker Execution ---")
    code_result = worker1.execute_task(plan, task)
    print("\nCode Result:")
    print(code_result)
    
    # Step 3: Reviewer checks the work
    review_result = worker2.execute_task(plan, code_result)
    print("\n--- Review Result ---")
    print(review_result)
    
    return code_result, review_result

Why? This code sets up the basic structure of our AI agents. The Planner uses a more capable model (GPT-4) to create a plan, while the Workers use a cheaper model (GPT-3.5) to do the actual work. This mimics the concept from the article where cheaper models handle most of the work while a more capable model plans.

Step 5: Create a Test Script

Create a file called main.py to test our agent system:

from agent_system import run_agent_system

if __name__ == "__main__":
    task = "Write a simple Python function that calculates the factorial of a number."
    print("Starting AI agent system...")
    print(f"Task: {task}")
    
    code_result, review_result = run_agent_system(task)
    
    print("\n--- Final Output ---")
    print(code_result)

Why? This script runs our agent system with a simple coding task to demonstrate how it works.

Step 6: Run Your Agent System

Run the test script:

python main.py

Why? This will execute our agent system and show you how the planner creates a plan and how the workers execute it.

Step 7: Experiment with Different Tasks

Try changing the task in main.py to see how the system handles different coding challenges:

task = "Create a basic web scraper in Python that fetches headlines from a news website."
# Or
# task = "Design a simple database schema for a library management system."
# Or
# task = "Write a unit test for a Python function that sorts a list of dictionaries by a specific key."

Why? Experimenting with different tasks helps you understand how the system adapts to various coding challenges.

Summary

In this tutorial, you've built a simplified version of an AI agent system that mimics the concept described in the Cursor article. You've learned how to:

• Set up a Python environment with virtual environments
• Use the OpenAI API to communicate with AI models
• Create different types of AI agents (planners and workers)
• Separate planning from execution in AI systems

This system demonstrates how a more capable AI model can plan complex tasks while cheaper models execute the work. While this is a simplified version, it gives you a foundational understanding of how AI agent swarms work in practice.

Remember, in real-world applications, you'd want to add more sophisticated error handling, logging, and possibly even code execution capabilities to make the agents truly useful for actual software development tasks.