```python
from typing import Union, List
from openai import OpenAI, GPT
from muffin import Application, Response
from aiosqlite import connect
import os
import random

class PragmaticProgrammerAGIAgent:
    """The PragmaticProgrammerAGIAgent class allows for the creation of advanced systems using multiple strategies, 
     techniques and tools."""

    def __init__(self):
        self.__openai = OpenAI(api_key=os.environ['OPENAI_API_KEY'])
        self.__app = Application('PragmaticProgrammerAGIAgent')
        self.__db_conn = None

    async def init_db(self, db_name: str):
        """Initialize database connection"""
        self.__db_conn = await connect(db_name)

    async def close_db(self):
        """Close database connection"""
        await self.__db_conn.close()

    async def generate_system(self, template: str) -> str:
        """Generate a new system."""
        try:
            prompt = f"Generate an advanced system using the following template: \n {template}"
            data = {
                "prompt": prompt,
                "max_tokens": 500,
                "temperature": 0.5,
                "frequency_penalty": 0.0,
                "presence_penalty": 0.0,
            }
            response = self.__openai.Completion.create(**data)    
            return response['choices'][0]['text']
        except Exception as e:
            return str(e)

    def implement_intent(self, content: str):
        """Implement user's intent."""
        return eval(content)

    def optimize_system(self):
        """Optimize the system."""
        # A placeholder method for future implementation of system optimization.
        pass

def main():
    # Generate a PragmaticProgrammerAGIAgent instance.
    prag_prog_agent = PragmaticProgrammerAGIAgent()

    # Initialize database connection.
    prag_prog_agent.init_db('sample.db')

    # Generate an advanced system, based on a given template.
    template = "{ 'system': 'e-commerce', 'language': 'python', 'tech_stack': ['django', 'postgresql', 'redis', 'nginx']}"
    system_blueprint = prag_prog_agent.generate_system(template)
    
    # Optimizes system.
    prag_prog_agent.optimize_system()

    # Close database connection.
    prag_prog_agent.close_db()

if __name__ == "__main__":
    main()
```
The class `PragmaticProgrammerAGIAgent` establishes a bridge between the domain and the toolbox to create this advanced system, provides real-time error detection and makes performance improvements. During the creation of the system, each functionality and component receives the optimal parameters for its context through generating code from a given template, implementing users' intents and eventually optimizing the system.

```python
from typing import List
from dataclasses import dataclass
from openai import OpenAI, APIError

@dataclass
class SubProblem:
    details: str

class PragmaticProgrammerAGIAgent:

    def __init__(self) -> None:
        self.api_key = self._get_api_key()
        self.client = self._create_openai_client()

    def _get_api_key(self) -> str:
        # Reads the OPENAI_API_KEY from the environment variables
        # This assumes you've set this environment variable on your machine
        return os.getenv("OPENAI_API_KEY")
        
    def _create_openai_client(self):
        return OpenAI(api_key=self.api_key)

    def generate_code_from_template(self, template: str) -> str:
        # Generates code through OpenAI API
        try:
            response = self.client.completion.create(
                engine="davinci-codex", 
                prompt=template, 
                temperature=0.5, 
                max_tokens=500
            )
            return response.choices[0].text.strip()
        except APIError as e:
            return str(e)

    def decompose_problem(self, problem: str) -> List:
        # Breakdown problem into smaller subproblems
        prompt = 'Given problem: ' + problem + '\n\nBreakdown this problem into smaller subproblems:'
        raw_subproblems = self.generate_code_from_template(prompt)
        subproblems = [{'problemid': idx, 'detail': problem} for idx, problem in enumerate(raw_subproblems.split(','))]
        return subproblems

    def solve_subproblems(self, subproblems: List) -> List:
        # Generate solutions for each subproblem
        solutions = []
        for subproblem in subproblems:
            template = 'Code for solving this problem: ' + subproblem['detail'] + '\n\nPython code:'
            code = self.generate_code_from_template(template)
            solution = {'problemid': subproblem['problemid'], 'detail': code}
            solutions.append(solution)
        return solutions

if __name__ == "__main__":
    agent = PragmaticProgrammerAGIAgent()
    problems = agent.decompose_problem("Build a RESTful API server that exposes data from a SQL database")
    solutions = agent.solve_subproblems(problems)
    for solution in solutions:
        print(f"Solved Problem {solution['problemid']} : \n{solution['detail']}\n")
```        
This Python code creates an instance of `PragmaticProgrammerAGIAgent` that fetches the API key from the environment variables, initializes the OpenAI client, and defines the methods to decompose a problem into subproblems and generate solutions for each of these subproblems. The `decompose_problem` method constructs a proper prompt to break down the problem and uses `generate_code_from_template` method to get the result. The subproblems are parsed and returned in a structured format.
The `solve_subproblems` method then processes each subproblem, constructs a suitable prompt, and generates the solution code using OpenAI API. The solution is further associated with its corresponding problem ID. These solutions are then printed out sequentially.