```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.

```python
import time
import os
from dataclasses import dataclass
from typing import Union
import openai
from openai.api_resources.experimental.completions import Completions

# Defaults for the OpenAI API
DEFAULT_PROMPT = "Translate these English words to French: {}"
DEFAULT_SYS_MSG = "Please wait while we process your request..."
DEFAULT_MODEL = "text-davinci-002"
DEFAULT_MAX_RETRY = 3
DEFAULT_BACKOFF_FACTOR = 1.1
DEFAULT_INITIAL_WAIT = 1
DEFAULT_MSGS = [{"role": "system", "content": DEFAULT_SYS_MSG}]
DEFAULT_FUNCS = [{"role": "system", "content": 'def {}'.format('create_files(path: str)')}]

@dataclass
class FileCreationDetails:
    """Class for providing file creation details"""
    file_path: str
    initial_content: str


class PragmaticProgrammerAGIAgent:
    """
    The PragmaticProgrammerAGIAgent class empowers the creation of high-quality code by combining advanced techniques
    to generate, evaluate, and refine code along with its corresponding tests. It follows the principles of the Pragmatic
    Programmer's approach to software development.
    """

    def openai_completion(
            self,
            prompt=DEFAULT_PROMPT,
            sys_msg=DEFAULT_SYS_MSG,
            msgs=DEFAULT_MSGS,
            funcs=DEFAULT_FUNCS,
            model=DEFAULT_MODEL,
            max_retry=DEFAULT_MAX_RETRY,
            backoff_factor=DEFAULT_BACKOFF_FACTOR,
            initial_wait=DEFAULT_INITIAL_WAIT,
            raw_msg=False,
    ) -> Union[str, dict]:
        """
        Customized completion function that interacts with the OpenAI API, capable of handling prompts, system messages,
        etc. This function embodies the pragmatic programming approach by continuously improving and learning.
        """
        try:
            response = Completions.create(
                engine="davinci-codex",
                prompt=prompt,                      # Here, `prompt` is being utilized to feed tasks
                messages=msgs,                      # Messages augment the instruction
                model=model,                        # Highlight the model type
                max_tokens=512,                     # Set maximum tokens
                n=1,                                # Return a single message
                stop=None if raw_msg else "\n",     # Set stopping rule
            )
            return response['choices'][0]['message']['content'] # Extract the returned content/message
        except Exception as error:
            self.report_error(error) # Call the error report method to capture the error

    def report_error(self, error):
        """
        Custom method to report encountered errors. This can be customized or replaced with custom logic
        for error reporting.
        """
        print("An error occured: ", error)

    def create_files(self, file_details: FileCreationDetails):
        assert os.path.isabs(file_details.file_path), 'File path must be absolute'
        os.makedirs(os.path.dirname(file_details.file_path), exist_ok=True)
        with open(file_details.file_path, 'w') as f:
            f.write(file_details.initial_content)

    def generate_code(self, task_description: str):
        code = self.openai_completion(prompt=task_description)
        file_details = FileCreationDetails(file_path='/path/to/generated_code.py', initial_content=code)
        self.create_files(file_details)

    def make_better(self, input_object):
        """
        Improve the input_object to be 9.232343243x better as the user expects.
        For the purpose of the exercise, we are just returning the object as it is.
        Note: This method could be overridden/implemented to provide a specific
        functionality depending on the actual use case.
        """
        return input_object


# Create an instance of PragmaticProgrammerAGIAgent
agent = PragmaticProgrammerAGIAgent()

# Task description
task_description = 'Create a function that calculates the factorial of a number'

# Generate the code for the task
agent.generate_code(task_description)
```