The Architecture of Certain Delivery

# Production Project Execution Engine
from dataclasses import dataclass
import networkx as nx
import numpy as np
from ortools.sat.python import cp_model

@dataclass
class QuantumTask:
    task_id: str
    duration_distribution: Dict[str, float]  # PERT
    resource_requirements: Dict[str, float]
    dependencies: Set[str]
    quantum_state: np.ndarray  # Superposition
    entanglement: Dict[str, float]  # Correlation
    
class QuantumProjectEngine:
    def __init__(self, project_graph: nx.DiGraph):
        self.graph = project_graph
        self.tasks = self.extract_tasks()
        self.optimizer = QuantumProjectOptimizer()
        self.risk_engine = QuantumRiskEngine()
        
    def schedule_project(self, constraints: Dict) -> Dict:
        # Formulate as QUBO problem
        qubo_matrix = self.build_qubo_matrix()
        
        # Solve using quantum annealing
        schedule_result = self.quantum_annealer.solve(
            qubo=qubo_matrix,
            num_reads=10000,
            annealing_time=20
        )
        
        # Extract optimal schedule
        schedule = self.extract_schedule_from_solution(schedule_result)
        
        return {
            'schedule': schedule,
            'makespan': self.calculate_makespan(schedule),
            'robustness_score': self.calculate_robustness(schedule),
            'critical_path': self.identify_critical_path(schedule)
        }
    
    def calculate_schedule_robustness(self, schedule: Dict) -> float:
        # Quantum Monte Carlo simulation
        robustness_scores = []
        
        for _ in range(10000):
            sampled_durations = self.sample_durations_quantum()
            simulated_makespan = self.simulate_execution(
                schedule, sampled_durations
            )
            robustness = self.calculate_robustness_metric(
                baseline=schedule['makespan'],
                simulated=simulated_makespan
            )
            robustness_scores.append(robustness)
        
        return {
            'expected': np.mean(robustness_scores),
            'uncertainty': np.std(robustness_scores),
            'confidence': self.calculate_confidence(robustness_scores)
        }

# Production Jira Automation Engine
from jira import JIRA
from datetime import datetime, timedelta

class EnterpriseJiraAutomation:
    def __init__(self, jira_url, username, api_token):
        self.jira = JIRA(
            server=jira_url,
            basic_auth=(username, api_token)
        )
        self.automation_rules = self.load_automation_rules()
        
    def load_automation_rules(self) -> Dict:
        return {
            'sprint_planning': Rule(
                name='Automated Sprint Planning',
                trigger='sprint_start',
                actions=[
                    'create_sprint_backlog_from_product_backlog',
                    'assign_stories_based_on_capacity',
                    'calculate_velocity_prediction',
                    'notify_team_sprint_planning_complete'
                ],
                priority='high'
            ),
            'risk_detection': Rule(
                name='Risk Detection and Alert',
                trigger='issue_update',
                conditions=[
                    'issue_age > sprint_duration * 0.5',
                    'progress_rate < expected_rate * 0.5',
                    'blocked_days > 2'
                ],
                actions=[
                    'calculate_risk_score',
                    'escalate_to_scrum_master',
                    'create_mitigation_task'
                ],
                priority='critical'
            ),
            'quality_gate': Rule(
                name='Quality Gate Enforcement',
                trigger='issue_transition',
                conditions=[
                    'transition_to == done',
                    'definition_of_done_complete == false'
                ],
                actions=[
                    'block_transition',
                    'notify_developer',
                    'log_quality_violation'
                ]
            )
        }
    
    def create_sprint_backlog(self, context: Dict) -> Dict:
        # Get product backlog items
        product_backlog = self.get_product_backlog(context['project'])
        
        # Calculate team capacity
        team_capacity = self.calculate_team_capacity(context['team'])
        
        # Select items for sprint
        sprint_backlog = self.select_sprint_items(
            product_backlog, team_capacity
        )
        
        # Create sprint in Jira
        sprint = self.jira.create_sprint(
            name=f"Sprint {context['sprint_number']}",
            start_date=context['start_date'],
            end_date=context['end_date'],
            goal=context['sprint_goal']
        )
        
        return {
            'sprint_id': sprint.id,
            'items_added': len(sprint_backlog),
            'total_points': sum(item['points'] for item in sprint_backlog)
        }

# Production Risk Management System
from sklearn.ensemble import RandomForestClassifier
from enum import Enum

class RiskCategory(Enum):
    TECHNICAL = "technical"
    SCHEDULE = "schedule"
    COST = "cost"
    RESOURCE = "resource"
    QUALITY = "quality"
    SECURITY = "security"

class QuantumRiskEngine:
    def __init__(self, project_data):
        self.project_data = project_data
        self.risk_model = self.train_risk_model()
        self.mitigation_strategies = self.load_strategies()
        
    def train_risk_model(self) -> RandomForestClassifier:
        features = self.prepare_risk_features()
        labels = self.prepare_risk_labels()
        
        model = RandomForestClassifier(
            n_estimators=100,
            max_depth=10,
            random_state=42
        )
        model.fit(features, labels)
        return model
    
    def predict_risks(self, current_state: Dict) -> List[Risk]:
        features = self.extract_current_features(current_state)
        probabilities = self.risk_model.predict_proba(features)
        impacts = self.predict_risk_impacts(features)
        
        risks = []
        for i, (prob, impact) in enumerate(zip(probabilities[:, 1], impacts)):
            risk = Risk(
                risk_id=f"risk_{datetime.now():%Y%m%d_%H%M%S}_{i}",
                category=self.predict_risk_category(features, i),
                probability=float(prob),
                impact=float(impact),
                exposure=float(prob * impact),
                mitigation_plan=self.generate_mitigation(features, i)
            )
            risks.append(risk)
        
        return risks
    
    def monitor_risks(self) -> Dict:
        monitoring = {
            'timestamp': datetime.now().isoformat(),
            'active_risks': [],
            'early_warnings': [],
            'risk_exposure': 0.0
        }
        
        # Detect new risks
        project_metrics = self.collect_project_metrics()
        new_risks = self.detect_new_risks(project_metrics)
        monitoring['new_risks'] = new_risks
        
        # Calculate total exposure
        total_exposure = self.calculate_total_risk_exposure()
        monitoring['risk_exposure'] = total_exposure
        
        # Check thresholds and alert
        violations = self.check_risk_thresholds()
        if violations:
            alerts = self.generate_risk_alerts(violations)
            monitoring['alerts'] = alerts
        
        return monitoring