Are You ‘Agentic’ Enough for the AI Era?

Introduction

In the rapidly evolving world of AI, the ability to effectively delegate tasks to AI agents has become a crucial skill. This tutorial will teach you how to build and manage AI agents that can handle routine coding tasks, allowing you to focus on higher-level strategic work. By the end of this tutorial, you'll have created a simple AI agent system that can automatically generate code based on natural language prompts.

Prerequisites

Before starting this tutorial, you should have:

• Basic understanding of Python programming
• Python 3.8 or higher installed on your system
• Access to an OpenAI API key (you can get one from OpenAI's platform)
• Basic knowledge of REST APIs and HTTP requests

Step-by-Step Instructions

1. Set Up Your Development Environment

First, create a new Python project directory and install the required dependencies. The key library we'll use is openai, which provides easy access to OpenAI's API.

mkdir ai-agent-tutorial
 cd ai-agent-tutorial
 python -m venv venv
 source venv/bin/activate  # On Windows: venv\Scripts\activate
 pip install openai

Why this step? Creating a virtual environment ensures that our project dependencies don't interfere with other Python projects on your system. Installing the openai library gives us access to the tools needed to communicate with OpenAI's API.

2. Configure Your API Key

Create a new file called config.py in your project directory:

import os
from dotenv import load_dotenv

load_dotenv()

OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')

Next, create a .env file in the same directory and add your API key:

OPENAI_API_KEY=your_actual_api_key_here

Why this step? Storing your API key in a separate configuration file keeps it secure and prevents accidental exposure in version control systems.

3. Create the AI Agent Base Class

Create a file called ai_agent.py to define our base AI agent:

import openai
from config import OPENAI_API_KEY

class AIAgent:
    def __init__(self):
        openai.api_key = OPENAI_API_KEY
        self.model = "gpt-3.5-turbo"

    def generate_code(self, prompt):
        response = openai.ChatCompletion.create(
            model=self.model,
            messages=[
                {"role": "system", "content": "You are a helpful coding assistant. Generate clean, well-documented Python code based on user requests."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=500,
            temperature=0.3
        )
        return response.choices[0].message.content

Why this step? This creates a reusable base class that encapsulates the core functionality of our AI agent. The system prompt guides the AI to be helpful and focused on generating quality code.

4. Implement a Task Manager

Create a task_manager.py file to handle different types of coding tasks:

from ai_agent import AIAgent

class TaskManager:
    def __init__(self):
        self.agent = AIAgent()

    def create_function(self, function_name, description):
        prompt = f"Create a Python function named {function_name} that {description}. Include proper documentation and error handling."
        return self.agent.generate_code(prompt)

    def create_class(self, class_name, attributes, methods):
        prompt = f"Create a Python class named {class_name} with attributes {attributes} and methods {methods}. Include proper documentation and error handling."
        return self.agent.generate_code(prompt)

    def debug_code(self, code):
        prompt = f"Debug the following Python code and explain any issues: {code}"
        return self.agent.generate_code(prompt)

Why this step? This task manager organizes different types of coding tasks into specific methods, making it easier to delegate work to our AI agent based on the type of task required.

5. Create a Simple Interface

Create a main.py file to demonstrate how to use your AI agent:

from task_manager import TaskManager

# Initialize the task manager
manager = TaskManager()

# Example 1: Create a function
print("Creating a function to calculate factorial:")
function_code = manager.create_function(
    "factorial", 
    "calculates the factorial of a given number using recursion"
)
print(function_code)

print("\n" + "="*50 + "\n")

# Example 2: Create a class
print("Creating a simple BankAccount class:")
class_code = manager.create_class(
    "BankAccount", 
    ["balance", "account_number"], 
    ["deposit(amount)", "withdraw(amount)", "get_balance()"]
)
print(class_code)

Why this step? This interface demonstrates how to use your AI agent to complete real coding tasks. It shows how you can delegate specific work to the AI while maintaining control over what gets generated.

6. Run Your AI Agent

Execute your program by running:

python main.py

You should see the AI agent generate code for your requested tasks. The output will include properly formatted Python code with documentation.

Why this step? Running the program demonstrates the complete workflow of delegating tasks to an AI agent and receiving generated code, showing how you can leverage AI for routine coding work.

Summary

This tutorial demonstrated how to build a basic AI agent system that can handle routine coding tasks. By following these steps, you've learned how to:

• Set up an AI agent with OpenAI's API
• Create a structured approach to delegating coding tasks
• Generate code based on natural language prompts
• Manage different types of coding tasks through a task manager

The key insight from the Wired article is that in the AI era, your most valuable skill is determining what tasks to delegate to AI agents. This system allows you to focus on strategic decisions while letting AI handle the implementation details.

As you continue to develop your AI agent capabilities, consider adding more sophisticated features like:

• Error handling and validation of generated code
• Integration with version control systems
• Task prioritization and scheduling
• Code review and optimization capabilities