```python
from typing import List
from openai import api
from aiosqlite import connect
import os

class PragmaticProgrammerAGIAgent:
    def __init__(self, api_key):
        self.api = api.Completion.create(engine="davinci-codex", api_key=api_key)
        self.db = None

    async def init_db(self, db):
        self.db = await connect(db)
    
    async def close_db(self):
        if self.db is not None:
            await self.db.close()

    async def create_system(self, definition):
        response = self.api.create(prompt=f"Create a advanced system following this definition: {definition}")
        code = response.choices[0].text.strip()
        return code

class PragmaticStarterKit:
    def __init__(self, agent: PragmaticProgrammerAGIAgent):
        self.agent = agent

    async def create_pragmatic_starter_kit(self):
        definition = {
            'name': 'Pragmatic Starter Kit',
            'description': 'A pragmatic starter kit incorporating DDD, Docker Compose, GitHub Actions, and pytest',
            'technologies': ['DDD', 'Docker Compose', 'GitHub Actions', 'pytest']
        }
        code = await self.agent.create_system(definition)
        return code

if __name__ == "__main__":
    api_key = os.getenv('OPENAI_API_KEY')
    agent = PragmaticProgrammerAGIAgent(api_key)
    kit = PragmaticStarterKit(agent)
    system_code = kit.create_pragmatic_starter_kit()
    with open('PragmaticStarterKit.py', 'w') as file:
        file.write(system_code)
```
By instantiating the `PragmaticProgrammerAGIAgent` class and passing it the `OPENAI_API_KEY`, you can then instantiate the `PragmaticStarterKit` by passing the agent object as a parameter. The `PragmaticStarterKit` class contains a method that creates the entire structure for the Pragmatic Starter Kit. This kit incorporates DDD, Docker Compose, GitHub Actions, and pytest based on a predefined definition. This method invokes the `create_system` method of the agent, which in turn generates the required code to setup this system. The generated code is then written to a Python file named "PragmaticStarterKit.py". This demonstrates the power and the advanced capabilities of the `PragmaticProgrammerAGIAgent` in creating high standard and advanced systems just from a simple definition.

```python
import asyncio
from typing import List, Dict, Any

class PragmaticProgrammerAGIAgent:

    def decompose_problem(self, problem: str) -> List[str]:
        """Decompose a complex problem into simpler sub-problems."""
        return self._internal_decompose(problem) 

    def generate_solutions(self, problems: List[str]) -> Dict[str, str]:
        """Generate solutions for each sub-problem."""
        return {problem: self._internal_solve(problem) for problem in problems}

    async def solve_problem(self, problem: Any) -> Any:
        """Solve a problem in its entirety by decomposing it, solving each sub-problem and then composing the solutions."""
        decomposed_problems = self.decompose_problem(problem)
        solutions = self.generate_solutions(decomposed_problems)

        return self._internal_compose(solutions)

    def _internal_decompose(self, problem: str) -> List[str]:
        """Internal method to decompose a problem."""
        ...  

    def _internal_solve(self, problem: str) -> str:
        """Internal method to solve a problem."""
        ... 

    def _internal_compose(self, solutions: Dict[str, str]) -> Any:
        """Internal method to compose solutions."""
        ... 

def main() -> None:
    agent = PragmaticProgrammerAGIAgent()
    loop = asyncio.get_event_loop()
    problem = "Build a software to manage a hospital."
    solution = loop.run_until_complete(agent.solve_problem(problem))
     
if __name__ == "__main__":
    main()
```
In this block of code, the `PragmaticProgrammerAGIAgent` class is designed to solve complex problems by decomposing them into simpler sub-problems. First, the `decompose_problem` method breaks down a complex problem into simpler sub-problems. The `generate_solutions` method then generates solutions for each of these simpler sub-problems. The `solve_problem` method ties everything together; it takes in a complex problem, decomposes it, solves each of the simpler problems, and then composes the solutions to solve the original complex problem. All of the complex logic is abstracted away inside the "internal" methods (`_internal_decompose`, `_internal_solve`, and `_internal_compose`) that are prefixed with a single underscore to indicate that they are for internal use only.