Enterprise-Scale Intelligent Process Automation: Advanced Patterns and Governance

from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Union
from dataclasses import dataclass
from enum import Enum

class PolicyScope(Enum):
    ENTERPRISE = "enterprise"
    BUSINESS_UNIT = "business_unit"
    DEPARTMENT = "department"
    PROCESS = "process"

@dataclass
class PolicyInheritanceRule:
    scope: PolicyScope
    parent_policy_id: Optional[str]
    override_permissions: List[str]
    inheritance_strategy: str  # "merge", "override", "extend"

class EnterprisePolicyManager:
    """Manages hierarchical policy inheritance and resolution."""

    def __init__(self, policy_store: 'PolicyStore'):
        self.policy_store = policy_store
        self.inheritance_cache = {}

    async def resolve_effective_policy(
        self,
        business_unit: str,
        department: str,
        process_type: str,
        context: Dict[str, Any]
    ) -> 'CompositePolicy':
        """Resolve effective policy by combining inheritance hierarchy."""

        cache_key = f"{business_unit}:{department}:{process_type}"
        if cache_key in self.inheritance_cache:
            return self.inheritance_cache[cache_key]

        # Build policy inheritance chain
        policy_chain = await self._build_policy_chain(
            business_unit, department, process_type
        )

        # Apply inheritance rules and create composite policy
        effective_policy = await self._apply_inheritance_rules(
            policy_chain, context
        )

        # Cache resolved policy with TTL
        self.inheritance_cache[cache_key] = effective_policy
        return effective_policy

    async def _build_policy_chain(
        self, business_unit: str, department: str, process_type: str
    ) -> List['PolicyDocument']:
        """Build ordered chain of applicable policies."""

        policies = []

        # Enterprise-level policies (highest precedence for compliance)
        enterprise_policies = await self.policy_store.get_policies_by_scope(
            PolicyScope.ENTERPRISE
        )
        policies.extend(enterprise_policies)

        # Business unit policies
        bu_policies = await self.policy_store.get_policies_by_business_unit(
            business_unit
        )
        policies.extend(bu_policies)

        # Department policies
        dept_policies = await self.policy_store.get_policies_by_department(
            business_unit, department
        )
        policies.extend(dept_policies)

        # Process-specific policies (lowest precedence)
        process_policies = await self.policy_store.get_policies_by_process(
            business_unit, department, process_type
        )
        policies.extend(process_policies)

        return policies

from typing import Protocol
import asyncio
from datetime import datetime, timezone

class RegionalDecisionNode(Protocol):
    """Protocol for regional AI decision engines."""

    async def make_local_decision(
        self, context: 'DecisionContext', policies: 'PolicyDocument'
    ) -> 'DecisionResult': ...

    async def escalate_to_central(
        self, context: 'DecisionContext', reason: str
    ) -> 'EscalationResult': ...

class CentralDecisionOrchestrator:
    """Orchestrates decisions across regional nodes with fallback capabilities."""

    def __init__(self):
        self.regional_nodes: Dict[str, RegionalDecisionNode] = {}
        self.central_engine: 'CentralAIEngine' = CentralAIEngine()
        self.performance_monitor = PerformanceMonitor()

    async def route_decision(
        self, context: 'DecisionContext'
    ) -> 'OrchestrationResult':
        """Route decision to appropriate regional node with fallback logic."""

        # Determine optimal routing based on multiple factors
        routing_decision = await self._calculate_routing_strategy(context)

        if routing_decision.route_locally:
            return await self._process_regional_decision(
                context, routing_decision.target_region
            )
        else:
            return await self._process_central_decision(context)

    async def _calculate_routing_strategy(
        self, context: 'DecisionContext'
    ) -> 'RoutingDecision':
        """Determine optimal routing based on complexity, latency, and capacity."""

        factors = {
            'complexity_score': await self._assess_decision_complexity(context),
            'regional_capacity': await self._check_regional_capacity(
                context.geography
            ),
            'latency_requirements': context.urgency_level,
            'compliance_requirements': await self._assess_compliance_scope(context),
            'historical_performance': await self._get_historical_performance(
                context.geography, context.request_type
            )
        }

        # Multi-criteria decision analysis for routing
        routing_score = await self._calculate_routing_score(factors)

        return RoutingDecision(
            route_locally=routing_score.local_preference > 0.7,
            target_region=context.geography,
            confidence=routing_score.confidence,
            fallback_strategy=routing_score.fallback_strategy
        )

from typing import Set, Dict, Any
from dataclasses import dataclass
from datetime import datetime

@dataclass
class ComplianceRequirement:
    regulation_id: str
    jurisdiction: str
    requirement_type: str  # "mandatory", "conditional", "advisory"
    applicability_criteria: Dict[str, Any]
    validation_rules: List[str]
    audit_requirements: Dict[str, Any]

class ComplianceEngine:
    """Ensures AI decisions comply with applicable regulations."""

    def __init__(self):
        self.regulations = self._load_regulatory_framework()
        self.compliance_validators = self._initialize_validators()

    async def validate_decision_compliance(
        self,
        decision: 'DecisionResult',
        context: 'DecisionContext'
    ) -> 'ComplianceValidation':
        """Validate decision against all applicable regulations."""

        applicable_regulations = await self._identify_applicable_regulations(
            context
        )

        validation_results = []
        for regulation in applicable_regulations:
            result = await self._validate_against_regulation(
                decision, context, regulation
            )
            validation_results.append(result)

        return ComplianceValidation(
            is_compliant=all(r.compliant for r in validation_results),
            validation_results=validation_results,
            required_actions=self._extract_required_actions(validation_results),
            audit_trail=self._generate_compliance_audit_trail(
                decision, validation_results
            )
        )

    async def _identify_applicable_regulations(
        self, context: 'DecisionContext'
    ) -> List[ComplianceRequirement]:
        """Identify regulations applicable to specific decision context."""

        applicable = []

        for regulation in self.regulations:
            if await self._check_applicability(regulation, context):
                applicable.append(regulation)

        return applicable

    async def _check_applicability(
        self,
        regulation: ComplianceRequirement,
        context: 'DecisionContext'
    ) -> bool:
        """Check if regulation applies to specific context."""

        # Geographic applicability
        if not self._check_geographic_scope(regulation, context):
            return False

        # Transaction type applicability
        if not self._check_transaction_scope(regulation, context):
            return False

        # Value threshold applicability
        if not self._check_value_thresholds(regulation, context):
            return False

        return True

from typing import Any, Dict, List, Optional
from dataclasses import dataclass, asdict
from datetime import datetime
import json
import hashlib

@dataclass
class AuditableDecision:
    """Complete audit record for AI agent decision."""

    decision_id: str
    timestamp: datetime
    context_hash: str  # Cryptographic hash of input context
    policy_version_hash: str  # Hash of policies used
    decision_reasoning: str
    confidence_score: float
    processing_path: List[str]  # Step-by-step decision path
    model_versions: Dict[str, str]  # AI model versions used
    human_reviewers: List[str]  # Any human involvement
    compliance_validations: List[Dict[str, Any]]
    risk_assessments: List[Dict[str, Any]]

class EnterpriseAuditSystem:
    """Enterprise-grade audit system for AI decision tracking."""

    def __init__(self, audit_store: 'AuditStore'):
        self.audit_store = audit_store
        self.encryption_service = EncryptionService()

    async def create_audit_record(
        self,
        decision: 'DecisionResult',
        context: 'DecisionContext',
        processing_metadata: Dict[str, Any]
    ) -> AuditableDecision:
        """Create comprehensive audit record for decision."""

        # Generate cryptographic hashes for integrity
        context_hash = self._generate_context_hash(context)
        policy_hash = self._generate_policy_hash(
            processing_metadata['policies_used']
        )

        # Extract detailed reasoning chain
        reasoning_chain = await self._extract_reasoning_chain(
            processing_metadata['ai_processing_log']
        )

        audit_record = AuditableDecision(
            decision_id=decision.decision_id,
            timestamp=datetime.now(timezone.utc),
            context_hash=context_hash,
            policy_version_hash=policy_hash,
            decision_reasoning=decision.reasoning,
            confidence_score=decision.confidence,
            processing_path=reasoning_chain,
            model_versions=processing_metadata['model_versions'],
            human_reviewers=processing_metadata.get('human_reviewers', []),
            compliance_validations=processing_metadata.get('compliance_checks', []),
            risk_assessments=processing_metadata.get('risk_assessments', [])
        )

        # Encrypt and store audit record
        encrypted_record = await self._encrypt_audit_record(audit_record)
        await self.audit_store.store_audit_record(encrypted_record)

        return audit_record

    async def generate_regulatory_report(
        self,
        start_date: datetime,
        end_date: datetime,
        regulation_framework: str
    ) -> 'RegulatoryReport':
        """Generate comprehensive regulatory compliance report."""

        decisions = await self.audit_store.get_decisions_by_period(
            start_date, end_date
        )

        compliance_analysis = await self._analyse_compliance_patterns(
            decisions, regulation_framework
        )

        risk_analysis = await self._analyse_risk_patterns(decisions)

        return RegulatoryReport(
            period=f"{start_date.isoformat()} to {end_date.isoformat()}",
            total_decisions=len(decisions),
            compliance_summary=compliance_analysis,
            risk_summary=risk_analysis,
            exception_analysis=await self._analyse_exceptions(decisions),
            model_performance_analysis=await self._analyse_model_performance(
                decisions
            )
        )

from typing import Dict, List, Optional
from dataclasses import dataclass
from enum import Enum

class TransformationPhase(Enum):
    ASSESSMENT = "assessment"
    PILOT = "pilot"
    SCALING = "scaling"
    OPTIMIZATION = "optimization"
    MATURATION = "maturation"

@dataclass
class TransformationMetrics:
    process_automation_rate: float
    decision_accuracy_improvement: float
    compliance_score: float
    user_adoption_rate: float
    cost_reduction_percentage: float
    time_to_decision_improvement: float

class EnterpriseTransformationManager:
    """Manages enterprise-wide transformation to AI agent controllers."""

    def __init__(self):
        self.transformation_roadmap = self._create_transformation_roadmap()
        self.change_management_framework = ChangeManagementFramework()

    async def assess_transformation_readiness(
        self, organisation: 'Organisation'
    ) -> 'ReadinessAssessment':
        """Assess organisational readiness for AI agent controller deployment."""

        assessments = {
            'technical_readiness': await self._assess_technical_capabilities(
                organisation
            ),
            'cultural_readiness': await self._assess_cultural_factors(
                organisation
            ),
            'process_maturity': await self._assess_process_maturity(
                organisation
            ),
            'governance_framework': await self._assess_governance_capabilities(
                organisation
            ),
            'change_capacity': await self._assess_change_management_capacity(
                organisation
            )
        }

        overall_readiness = self._calculate_overall_readiness(assessments)

        return ReadinessAssessment(
            overall_score=overall_readiness.score,
            readiness_factors=assessments,
            recommended_approach=overall_readiness.recommended_approach,
            critical_success_factors=overall_readiness.success_factors,
            risk_mitigation_strategies=overall_readiness.risk_strategies
        )

    async def create_transformation_plan(
        self,
        assessment: 'ReadinessAssessment',
        business_objectives: Dict[str, Any]
    ) -> 'TransformationPlan':
        """Create customised transformation plan based on assessment."""

        # Identify optimal transformation sequence
        transformation_sequence = await self._optimise_transformation_sequence(
            assessment, business_objectives
        )

        # Define success metrics and monitoring
        success_metrics = await self._define_success_metrics(
            business_objectives
        )

        # Create risk mitigation strategies
        risk_strategies = await self._create_risk_mitigation_strategies(
            assessment.risk_factors
        )

        return TransformationPlan(
            phases=transformation_sequence,
            success_metrics=success_metrics,
            risk_mitigation=risk_strategies,
            resource_requirements=await self._calculate_resource_requirements(
                transformation_sequence
            ),
            timeline=await self._create_transformation_timeline(
                transformation_sequence
            )
        )

from typing import Any, Dict, List, Optional, Protocol
import asyncio
from dataclasses import dataclass

class EnterpriseEventBus(Protocol):
    """Protocol for enterprise event bus integration."""

    async def publish_event(self, event: 'BusinessEvent') -> None: ...
    async def subscribe_to_events(
        self, event_types: List[str], handler: 'EventHandler'
    ) -> None: ...

@dataclass
class BusinessEvent:
    event_type: str
    event_id: str
    timestamp: datetime
    source_system: str
    payload: Dict[str, Any]
    correlation_id: Optional[str] = None

class AIAgentEventIntegration:
    """Integrates AI agent controllers with enterprise event streams."""

    def __init__(self, event_bus: EnterpriseEventBus):
        self.event_bus = event_bus
        self.decision_processors = {}

    async def initialize_event_subscriptions(self):
        """Set up event subscriptions for AI decision triggering."""

        # Subscribe to business events that trigger AI decisions
        await self.event_bus.subscribe_to_events(
            ['purchase_request_submitted', 'policy_updated', 'budget_allocated'],
            self._handle_business_event
        )

        # Subscribe to system events for monitoring and optimization
        await self.event_bus.subscribe_to_events(
            ['system_performance_alert', 'compliance_violation_detected'],
            self._handle_system_event
        )

    async def _handle_business_event(self, event: BusinessEvent):
        """Process business events that require AI decision making."""

        if event.event_type == 'purchase_request_submitted':
            await self._process_purchase_request_event(event)
        elif event.event_type == 'policy_updated':
            await self._handle_policy_update_event(event)
        elif event.event_type == 'budget_allocated':
            await self._handle_budget_allocation_event(event)

    async def _process_purchase_request_event(self, event: BusinessEvent):
        """Process purchase request through AI decision engine."""

        try:
            # Extract request context from event payload
            context = await self._build_context_from_event(event)

            # Route to appropriate decision engine
            decision_result = await self._route_decision(context)

            # Publish decision result as enterprise event
            decision_event = BusinessEvent(
                event_type='ai_decision_completed',
                event_id=f"decision_{event.event_id}",
                timestamp=datetime.now(timezone.utc),
                source_system='ai_agent_controller',
                payload={
                    'original_request_id': event.event_id,
                    'decision': decision_result.decision,
                    'reasoning': decision_result.reasoning,
                    'confidence': decision_result.confidence,
                    'next_steps': decision_result.next_steps
                },
                correlation_id=event.correlation_id
            )

            await self.event_bus.publish_event(decision_event)

        except Exception as e:
            # Publish error event for monitoring and alerting
            error_event = BusinessEvent(
                event_type='ai_decision_error',
                event_id=f"error_{event.event_id}",
                timestamp=datetime.now(timezone.utc),
                source_system='ai_agent_controller',
                payload={
                    'original_request_id': event.event_id,
                    'error_type': type(e).__name__,
                    'error_message': str(e),
                    'requires_manual_intervention': True
                },
                correlation_id=event.correlation_id
            )

            await self.event_bus.publish_event(error_event)