core/enterprise_simulation.py

"""
Enterprise Feature Simulation - Shows what ARF Enterprise adds on top of OSS
Not real execution, but demonstrates the value proposition
"""
import asyncio
import logging
from typing import Dict, Any, List
from datetime import datetime
import random

logger = logging.getLogger(__name__)

# Trial license for demo
DEMO_TRIAL_LICENSE = "ARF-TRIAL-DEMO-2026"

class EnterpriseFeatureSimulation:
    """
    Simulates Enterprise features that would be available with arf_enterprise package
    
    Shows:
    1. Novel execution protocols
    2. Rollback guarantees
    3. Deterministic confidence
    4. Autonomous healing
    5. Enhanced safety features
    """
    
    def __init__(self):
        self.enterprise_available = False
        self.trial_license = DEMO_TRIAL_LICENSE
        self._check_enterprise()
    
    def _check_enterprise(self):
        """Check if enterprise package is available"""
        try:
            # Try to import real enterprise package
            from arf_enterprise import (
                create_enterprise_server,
                EnterpriseLLMClient,
                RollbackController,
                ExecutionMode,
                DeterministicConfidence,
                NovelExecutionIntent,
                get_novel_execution_capabilities
            )
            self.enterprise_available = True
            logger.info("✅ Real ARF Enterprise package available")
        except ImportError:
            self.enterprise_available = False
            logger.info("⚠️ ARF Enterprise package not available - using simulation")
    
    async def enhance_oss_analysis(self, oss_analysis: Dict[str, Any], scenario_name: str) -> Dict[str, Any]:
        """
        Enhance OSS analysis with Enterprise features
        
        Shows what Enterprise adds:
        - Novel execution protocols
        - Rollback guarantees
        - Deterministic confidence
        - Business impact analysis
        """
        logger.info(f"🏢 Enhancing OSS analysis with Enterprise features for: {scenario_name}")
        
        enhancement_start = datetime.now()
        
        try:
            # Extract data from OSS analysis
            oss_intent = oss_analysis.get("analysis", {}).get("decision", {})
            similar_incidents = oss_analysis.get("analysis", {}).get("recall", [])
            detection = oss_analysis.get("analysis", {}).get("detection", {})
            
            # 1. Apply deterministic confidence system (Enterprise feature)
            deterministic_confidence = self._create_deterministic_confidence(
                detection, similar_incidents, scenario_name
            )
            
            # 2. Apply novel execution protocols (Enterprise feature)
            novel_execution = self._apply_novel_execution_protocols(
                oss_intent, deterministic_confidence, scenario_name
            )
            
            # 3. Prepare rollback guarantees (Enterprise feature)
            rollback_guarantees = await self._prepare_rollback_guarantees(
                oss_intent, scenario_name
            )
            
            # 4. Calculate enhanced business impact (Enterprise feature)
            business_impact = self._calculate_enhanced_business_impact(
                scenario_name, similar_incidents
            )
            
            # 5. Determine execution mode capabilities
            execution_capabilities = self._get_execution_capabilities()
            
            enhancement_time = (datetime.now() - enhancement_start).total_seconds() * 1000
            
            return {
                "enterprise_available": self.enterprise_available,
                "trial_license": self.trial_license if not self.enterprise_available else "Real License",
                "enhancements": {
                    "deterministic_confidence": deterministic_confidence,
                    "novel_execution_protocols": novel_execution,
                    "rollback_guarantees": rollback_guarantees,
                    "business_impact_analysis": business_impact,
                    "execution_capabilities": execution_capabilities
                },
                "value_proposition": [
                    "✅ Autonomous execution with safety guarantees",
                    "✅ Novel execution protocols for unprecedented incidents",
                    "✅ Deterministic confidence scoring (not just ML probabilities)",
                    "✅ Rollback guarantees for zero-downtime deployments",
                    "✅ Business-aware impact analysis",
                    "✅ Audit trail and compliance reporting",
                    f"✅ Execution modes: {', '.join(execution_capabilities['modes'])}"
                ],
                "processing_time_ms": enhancement_time,
                "requires_real_enterprise": not self.enterprise_available,
                "upgrade_cta": "Contact sales@arf.dev for Enterprise trial" if not self.enterprise_available else None
            }
            
        except Exception as e:
            logger.error(f"Enterprise enhancement failed: {e}")
            return {
                "enterprise_available": self.enterprise_available,
                "error": str(e),
                "fallback_message": "OSS analysis complete. Enterprise features require arf_enterprise package."
            }
    
    def _create_deterministic_confidence(self, detection: Dict, similar_incidents: List, scenario_name: str) -> Dict[str, Any]:
        """Simulate deterministic confidence system (Enterprise feature)"""
        detection_confidence = detection.get("confidence", 0.85)
        
        # Calculate pattern confidence from similar incidents
        if similar_incidents:
            pattern_confidence = sum([inc.get("similarity_score", 0.7) for inc in similar_incidents]) / len(similar_incidents)
            success_rate = sum([1 for inc in similar_incidents if inc.get("success", False)]) / len(similar_incidents)
        else:
            pattern_confidence = 0.75
            success_rate = 0.70
        
        # Scenario-specific adjustments
        scenario_factors = {
            "Cache Miss Storm": {"historical_pattern": 0.92, "current_metrics": 0.87, "system_state": 0.95},
            "Database Connection Pool Exhaustion": {"historical_pattern": 0.88, "current_metrics": 0.82, "system_state": 0.90},
            "Kubernetes Memory Leak": {"historical_pattern": 0.90, "current_metrics": 0.85, "system_state": 0.92},
            "API Rate Limit Storm": {"historical_pattern": 0.85, "current_metrics": 0.88, "system_state": 0.87},
            "Network Partition": {"historical_pattern": 0.93, "current_metrics": 0.90, "system_state": 0.96},
            "Storage I/O Saturation": {"historical_pattern": 0.87, "current_metrics": 0.83, "system_state": 0.89}
        }
        
        factors = scenario_factors.get(scenario_name, {"historical_pattern": 0.85, "current_metrics": 0.80, "system_state": 0.85})
        
        # Combine factors deterministically (not just ML probability)
        business_context = 0.88  # Always consider business impact
        safety_margin = 0.95  # Enterprise includes safety margins
        
        components = [
            {"component": "historical_pattern", "value": factors["historical_pattern"], "weight": 0.25},
            {"component": "current_metrics", "value": factors["current_metrics"], "weight": 0.25},
            {"component": "system_state", "value": factors["system_state"], "weight": 0.20},
            {"component": "detection_confidence", "value": detection_confidence, "weight": 0.15},
            {"component": "business_context", "value": business_context, "weight": 0.10},
            {"component": "safety_margin", "value": safety_margin, "weight": 0.05}
        ]
        
        # Calculate weighted score
        weighted_score = sum(c["value"] * c["weight"] for c in components)
        
        return {
            "score": round(weighted_score, 3),
            "components": components,
            "deterministic": True,  # Enterprise feature: deterministic not probabilistic
            "explainable": True,  # Enterprise feature: each component explained
            "safety_margin_included": True
        }
    
    def _apply_novel_execution_protocols(self, oss_intent: Dict, confidence: Dict, scenario_name: str) -> Dict[str, Any]:
        """Apply novel execution protocols (Enterprise feature)"""
        # Determine novelty level based on confidence and scenario
        confidence_score = confidence.get("score", 0.85)
        
        if confidence_score >= 0.95:
            novelty_level = "KNOWN_PATTERN"
            risk_category = "LOW"
            execution_approach = "autonomous_safe"
        elif confidence_score >= 0.85:
            novelty_level = "PARTIAL_MATCH"
            risk_category = "MEDIUM"
            execution_approach = "human_approval_required"
        else:
            novelty_level = "NOVEL_SCENARIO"
            risk_category = "HIGH"
            execution_approach = "enhanced_monitoring_first"
        
        return {
            "novelty_level": novelty_level,
            "risk_category": risk_category,
            "execution_approach": execution_approach,
            "protocols_applied": [
                "deterministic_confidence_validation",
                "blast_radius_containment",
                "business_hour_compliance",
                "rollback_preparation",
                "circuit_breaker_setup"
            ],
            "enterprise_feature": True,
            "requires_license": True
        }
    
    async def _prepare_rollback_guarantees(self, oss_intent: Dict, scenario_name: str) -> Dict[str, Any]:
        """Prepare rollback guarantees (Enterprise feature)"""
        await asyncio.sleep(0.1)  # Simulate rollback preparation
        
        component = oss_intent.get("component", "unknown")
        
        return {
            "rollback_prepared": True,
            "state_id": f"state_{datetime.now().timestamp()}",
            "guarantee": "STRONG",
            "recovery_time_estimate": "45 seconds",
            "snapshot_strategy": "incremental",
            "verification_complete": True,
            "rollback_scenarios": [
                f"Restore {component} to previous state",
                "Rollback configuration changes",
                "Restore database connections",
                "Reset circuit breakers"
            ],
            "enterprise_feature": True,
            "requires_enterprise_server": True
        }
    
    def _calculate_enhanced_business_impact(self, scenario_name: str, similar_incidents: List) -> Dict[str, Any]:
        """Calculate enhanced business impact (Enterprise feature)"""
        # Get average savings from similar incidents
        if similar_incidents:
            avg_savings = sum(inc.get("cost_savings", 5000) for inc in similar_incidents) / len(similar_incidents)
            avg_resolution_time = 15  # minutes (average from similar incidents)
        else:
            avg_savings = 6500
            avg_resolution_time = 20
        
        # Scenario-specific impacts
        scenario_impacts = {
            "Cache Miss Storm": {
                "users_affected": 45000,
                "revenue_risk_per_hour": 8500,
                "recovery_time_manual": 45,
                "recovery_time_arf": 12
            },
            "Database Connection Pool Exhaustion": {
                "users_affected": 25000,
                "revenue_risk_per_hour": 4200,
                "recovery_time_manual": 35,
                "recovery_time_arf": 15
            },
            "Kubernetes Memory Leak": {
                "users_affected": 35000,
                "revenue_risk_per_hour": 5500,
                "recovery_time_manual": 40,
                "recovery_time_arf": 18
            },
            "API Rate Limit Storm": {
                "users_affected": 20000,
                "revenue_risk_per_hour": 3800,
                "recovery_time_manual": 25,
                "recovery_time_arf": 8
            },
            "Network Partition": {
                "users_affected": 75000,
                "revenue_risk_per_hour": 12000,
                "recovery_time_manual": 60,
                "recovery_time_arf": 20
            },
            "Storage I/O Saturation": {
                "users_affected": 30000,
                "revenue_risk_per_hour": 6800,
                "recovery_time_manual": 50,
                "recovery_time_arf": 22
            }
        }
        
        impact = scenario_impacts.get(scenario_name, {
            "users_affected": 30000,
            "revenue_risk_per_hour": 5000,
            "recovery_time_manual": 30,
            "recovery_time_arf": 15
        })
        
        # Calculate ARF benefits
        time_saved = impact["recovery_time_manual"] - impact["recovery_time_arf"]
        cost_saved_per_incident = (impact["revenue_risk_per_hour"] / 60) * time_saved
        
        return {
            "scenario_specific": True,
            "users_protected": impact["users_affected"],
            "revenue_risk_per_hour": f"${impact['revenue_risk_per_hour']:,}",
            "recovery_times": {
                "manual": f"{impact['recovery_time_manual']} minutes",
                "arf": f"{impact['recovery_time_arf']} minutes",
                "time_saved": f"{time_saved} minutes",
                "percent_faster": f"{int((time_saved / impact['recovery_time_manual']) * 100)}%"
            },
            "cost_analysis": {
                "cost_saved_per_incident": f"${int(cost_saved_per_incident):,}",
                "estimated_annual_savings": f"${int(cost_saved_per_incident * 15 * 12):,}",  # 15 incidents/month
                "roi_multiplier": "5.2×",
                "payback_months": "6.0"
            },
            "enterprise_feature": True,
            "business_aware": True
        }
    
    def _get_execution_capabilities(self) -> Dict[str, Any]:
        """Get execution mode capabilities (Enterprise feature)"""
        return {
            "modes": ["advisory", "approval", "autonomous"],
            "current_mode": "autonomous" if self.enterprise_available else "advisory",
            "requires_enterprise": ["approval", "autonomous"],
            "safety_guarantees": {
                "rollback": "guaranteed" if self.enterprise_available else "not_available",
                "circuit_breaker": "enabled" if self.enterprise_available else "disabled",
                "blast_radius": "enforced" if self.enterprise_available else "advisory_only",
                "business_hours": "enforced" if self.enterprise_available else "monitored"
            }
        }
    
    async def simulate_execution(self, scenario_name: str, mode: str = "autonomous") -> Dict[str, Any]:
        """Simulate Enterprise execution"""
        if mode == "advisory":
            return {
                "status": "advisory_only",
                "message": "OSS mode: Execution not allowed. Upgrade to Enterprise for autonomous healing.",
                "requires_enterprise": True,
                "execution_mode": "advisory"
            }
        
        await asyncio.sleep(0.3)
        
        if mode == "approval":
            return {
                "status": "awaiting_approval",
                "message": "Enterprise Approval Mode: Healing intent created, awaiting human approval",
                "requires_human_approval": True,
                "estimated_savings": "$8,500",
                "rollback_prepared": True,
                "execution_mode": "approval"
            }
        else:  # autonomous
            return {
                "status": "executed",
                "message": "Enterprise Autonomous Mode: Healing action executed with safety guarantees",
                "execution_time": "12 minutes",
                "cost_saved": "$8,500",
                "rollback_available": True,
                "rollback_guarantee": "STRONG",
                "novel_execution_used": True,
                "execution_mode": "autonomous",
                "enterprise_features_used": [
                    "deterministic_confidence",
                    "novel_execution_protocols",
                    "rollback_guarantees",
                    "business_aware_execution"
                ]
            }


# Factory function
_enterprise_sim_instance = None

async def get_enterprise_simulation() -> EnterpriseFeatureSimulation:
    """Get singleton EnterpriseFeatureSimulation instance"""
    global _enterprise_sim_instance
    if _enterprise_sim_instance is None:
        _enterprise_sim_instance = EnterpriseFeatureSimulation()
    return _enterprise_sim_instance