```python
import os
from typing import Any
import pyautogui as pag
from openai import OpenAI, APIError

class PragmaticProgrammerAGIAgent:
    """
    A PragmaticProgrammerAGIAgent operates by strategizing a step-by-step decomposition of problems,
    the creation of solutions, error detection, and performance improvement tasks.
    """

    def __init__(self) -> None:
        self.api_key = os.getenv("OPENAI_API_KEY")
        self.client = OpenAI(api_key=self.api_key)

    def decompose_problem(self, problem: str) -> Any:
        """
        Decompose any given problem into sub-problems that are simpler and easier to handle.
        """

        decomposition_prompt = f"Given problem: {problem}. Break this down into simpler sub-problems."
        api_call = self.client.Completion.create(
            engine="text-davinci-002",
            prompt=decomposition_prompt,
            temperature=0.7,
            max_tokens=300
        )

        return api_call.choices[0].text.strip()

    def generate_solution(self, sub_problem: str) -> Any:
        """
        Generate a solution for the given sub-problem using the OpenAI API.
        """

        generation_prompt = f"Subproblem: {sub_problem}. Write a python function that might solve this."

        api_call = self.client.Completion.create(
            engine="text-davinci-002",
            prompt=generation_prompt,
            temperature=0.7,
            max_tokens=300
        )

        return api_call.choices[0].text.strip()

    def error_detection(self, script: str) -> Any:
        """
        Perform a real-time error detection on the generated script.
        """

        error_detection_prompt = f"Python script:\n{script}\nDetect any potential errors and propose corrections."

        api_call = self.client.Completion.create(
            engine="text-davinci-002",
            prompt=error_detection_prompt,
            temperature=0.5,
            max_tokens=300
        )

        return api_call.choices[0].text.strip()

    def performance_improvement(self, script: str) -> str:
        """
        Perform a performance improvement on the generated script.
        """

        improvement_prompt = f"Python script:\n{script}\nPropose improvements to optimize performance."

        api_call = self.client.Completion.create(
            engine="text-davinci-002",
            prompt=improvement_prompt,
            temperature=0.7,
            max_tokens=300
        )

        return api_call.choices[0].text.strip()

    def __str__(self) -> str:
        return "PragmaticProgrammerAGIAgent"

if __name__ == "__main__":
    pp_agent = PragmaticProgrammerAGIAgent()
    complex_problem = "Implement an object detection method using deep learning."
    decomposed_problem = pp_agent.decompose_problem(complex_problem)
    solution = pp_agent.generate_solution(decomposed_problem)
    error_check = pp_agent.error_detection(solution)
    improved_solution = pp_agent.performance_improvement(solution)

    print(f"Decomposed problem: {decomposed_problem}\n\nGenerated solution: {solution}\n\nErrors detected: {error_check}\n\nImproved solution: {improved_solution}")
```
In the code above, the `PragmaticProgrammerAGIAgent` starts by decomposing complex problem to simpler sub-problems using openai gpt-3. Then, it generates a solution for the sub-problem, performs real-time error detection and optimize the initial solution for better performance. Following this process, we can ensure the delivery of comprehensive and top quality solutions.

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

openai.api_key = 'your-api-key'

@dataclass
class DecompAndSolution:
    sub_problem: str
    solution: str

class PragmaticProgrammerAGIAgent:

    def __init__(self, model: str = 'text-davinci-002'):
        self.model = model

    def process_problem(self, problem: str) -> List[DecompAndSolution]:
        subproblems = self._decompose_problem(problem)
        solutions = []
        for subproblem in subproblems:
            solution = self._generate_solution(subproblem)
            solution = self._optimize_solution(solution)
            solutions.append(DecompAndSolution(subproblem,solution))
        return solutions


    def _decompose_problem(self, problem: str) -> List[str]:
        prompt = f'Problem: {problem}\n\nDecompose this into subproblems:'
        response = openai.Completion.create(engine=self.model, prompt=prompt, max_tokens=500)
        return [sub.strip() for sub in response.choices[0].text.strip().split(',')]
    
    def _generate_solution(self, subproblem: str) -> str:
        prompt = f'Sub-Problem: {subproblem}\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, solution: str) -> str:
        prompt = f"Code:\n{solution}\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()

if __name__ == "__main__":
    agent = PragmaticProgrammerAGIAgent()
    problem = "Create a DDD architecture based application with docker compose, git actions, pytest, etc."
    decompositions_and_solutions = agent.process_problem(problem)
    for sub_problem_and_solution in decompositions_and_solutions:
        with open(sub_problem_and_solution.sub_problem.replace(" ", "_") + ".py", "w") as file:
            file.write(sub_problem_and_solution.solution)
```
The code creates an AGI Agent that decomposes, solves, and optimizes problems. For each sub-problem, the agent generates a Python file containing the optimized solution. The file is named per the sub-problem, for uniqueness and reference.