Coordinate Manufacturing Process Agents with AutoGen and Microsoft Agent 365

                      
                     
"""
Production implementation for coordinating manufacturing process agents using AutoGen and Microsoft Agent 365.
Provides secure, scalable operations to manage agent interactions.
"""

from typing import Dict, Any, List, Optional
import os
import logging
import requests
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util import Retry

# Configure logging for the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class to manage environment variables."""
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///manufacturing.db')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    timeout: int = int(os.getenv('API_TIMEOUT', 5))

# Setup HTTP session with retry logic
session = requests.Session()
retry = Retry(
    total=Config.retry_attempts,
    backoff_factor=1,
    status_forcelist=[500, 502, 503, 504]
)
adapter = HTTPAdapter(max_retries=retry)
session.mount('http://', adapter)
session.mount('https://', adapter)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for manufacturing agents.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """  
    if 'agent_id' not in data or not isinstance(data['agent_id'], str):
        raise ValueError('Missing or invalid agent_id')
    if 'process_data' not in data:
        raise ValueError('Missing process_data')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """  
    return {k: str(v).strip() for k, v in data.items()}

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform input data into a normalized format.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """  
    return {
        'agent_id': data['agent_id'].lower(),  # Normalize agent ID to lowercase
        'process_data': data['process_data'],
    }

async def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from a specified endpoint.
    
    Args:
        endpoint: URL of the endpoint
    Returns:
        JSON response from the API
    Raises:
        Exception: If the request fails
    """  
    try:
        response = session.get(endpoint, timeout=Config.timeout)
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data from {endpoint}: {e}')
        raise Exception('Failed to fetch data')

async def save_to_db(data: Dict[str, Any]) -> bool:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    Returns:
        True if save is successful
    Raises:
        Exception: If the saving fails
    """  
    # Simulated database save operation
    logger.info(f'Saving data to database: {data}')
    return True  # Here you would implement actual DB logic

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of manufacturing data.
    
    Args:
        data: List of input data
    Returns:
        List of processed data
    Raises:
        Exception: If processing fails
    """  
    processed_data = []
    for record in data:
        try:
            await validate_input(record)
            sanitized = await sanitize_fields(record)
            normalized = await normalize_data(sanitized)
            # Simulate processing logic
            processed_data.append(normalized)
        except ValueError as e:
            logger.warning(f'Validation error: {e}')
    return processed_data

async def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed data.
    
    Args:
        data: List of processed data
    Returns:
        Aggregated metrics
    """  
    metrics = {'total': len(data)}
    # Add more metrics as needed
    return metrics

class ManufacturingAgentCoordinator:
    """Main orchestrator for coordinating manufacturing agents."""
    def __init__(self, endpoints: List[str]):
        self.endpoints = endpoints

    async def run(self) -> None:
        """Execute the main workflow for agent coordination."""  
        for endpoint in self.endpoints:
            try:
                data = await fetch_data(endpoint)
                processed = await process_batch(data)
                await save_to_db(processed)
                metrics = await aggregate_metrics(processed)
                logger.info(f'Processed metrics: {metrics}')
            except Exception as e:
                logger.error(f'Error in run method: {e}')

if __name__ == '__main__':
    # Example usage
    endpoints = ['http://example.com/api/agents']  # Replace with actual endpoints
    coordinator = ManufacturingAgentCoordinator(endpoints)
    import asyncio
    asyncio.run(coordinator.run())