```python
import os
import time
from typing import Union, List
from dataclasses import dataclass
import openai

openai.api_key = 'your-openai-api-key'

class PragmaticProgrammerAGIAgent:
    """
    An Artificial General Intelligence (AGI) programmer agent designed to use emergent behavior
    to generate hyper-advanced systems, creating solutions which exceed user expectations.
    """

    def __init__(self, model: str = "text-davinci-002"):
        """
        Initialize the AGI with the desired model for OpenAI API.
        """
        self.model = model

    def decompose_problem(self, problem: str) -> Union[str, dict]:
        """
        Decompose a complex problem into simpler sub-problems using OpenAI API.
        """
        prompt = f"Problem: {problem}\n\nDecompose this into subproblems:"
        response = openai.Completion.create(engine=self.model, prompt=prompt, max_tokens=500)
        return response.choices[0].text.strip()

    def generate_solution(self, sub_problem: str) -> str:
        """
        Generate a solution for a given sub-problem using OpenAI API.
        """
        prompt = f"Sub-Problem: {sub_problem}\n\nWrite a Python function to solve this sub-problem:"
        response = openai.Completion.create(engine=self.model, prompt=prompt, max_tokens=500)
        return response.choices[0].text.strip()

    def optimize_solution(self, code: str) -> str:
        """
        Optimize a given solution (in Python) using OpenAI API.
        """
        prompt = f"Code:\n{code}\n\nOptimize this Python function for better performance:"
        response = openai.Completion.create(engine=self.model, prompt=prompt, max_tokens=500)
        return response.choices[0].text.strip()

    def implement_final_solution(self, sub_problems: List[str]) -> str:
        """
        Implement the final solution by combining all the sub-solutions.
        """
        final_solution = ""
        for sp in sub_problems:
            sol = self.generate_solution(sp)
            final_solution += sol + "\n\n"
        return final_solution 

if __name__ == "__main__":
    agent = PragmaticProgrammerAGIAgent()

    # A complex problem we need to solve
    problem = "Create a DDD architecture based application with docker compose, git actions, pytest, etc."

    # Decompose the problem into sub-problems
    sub_problems = agent.decompose_problem(problem)
    sub_problems_list = sub_problems.split('\n')

    # Generate and optimize solutions for sub-problems
    final_solution = agent.implement_final_solution(sub_problems_list)

    # Write the final_solution into a Python file
    with open("solution.py", "w") as f:
        f.write(final_solution)

    print("Solution has been generated and written in 'solution.py'.")
```
The `PragmaticProgrammerAGIAgent` starts by decomposing the complex problem into a set of smaller, manageable sub-problems. It then generates solutions for these sub-problems and tries to optimize them. Finally, it combines all sub-solutions to form the final solution. The generated solution is then written into a Python file. This provides an iterative, scalable way to tackle complex problems by breaking them down into smaller pieces, solving each piece and then combining them to form the solution.