Environment Awareness Architecture

class OrganizationStructure:
    def __init__(self):
        self.hierarchy = self.build_hierarchy()
        self.roles = self.load_role_definitions()
        self.policies = self.load_policy_framework()
        
    def build_hierarchy(self) -> OrganizationGraph:
        return OrganizationGraph(
            nodes=[
                OrgNode(id="ceo", title="Chief Executive Officer", level=0),
                OrgNode(id="cto", title="Chief Technology Officer", level=1),
                OrgNode(id="eng-mgr", title="Engineering Manager", level=2),
                # ... more nodes
            ],
            relationships=[
                Relationship(from_="ceo", to="cto", type="reports_to"),
                Relationship(from_="cto", to="eng-mgr", type="manages"),
                # ... more relationships
            ]
        )

policy_framework:
  data_governance:
    classification_levels:
      - public
      - internal
      - confidential
      - restricted
    retention_policies:
      financial_records: "7_years"
      operational_logs: "2_years"
      temporary_data: "30_days"
      
  approval_workflows:
    budget_requests:
      threshold_1k: ["direct_manager"]
      threshold_10k: ["direct_manager", "department_head"]
      threshold_100k: ["direct_manager", "department_head", "finance_director"]
      
  security_protocols:
    access_control: "rbac"
    authentication: "mfa_required"
    encryption: "aes_256"

class AuthorityMapper:
    def get_decision_authority(self, decision_type: str, amount: float = None) -> List[str]:
        authority_matrix = {
            "budget_approval": self.get_budget_authority(amount),
            "system_changes": ["system_admin", "cto"],
            "data_access": self.get_data_authority(decision_type),
            "process_changes": ["process_owner", "department_head"]
        }
        return authority_matrix.get(decision_type, ["escalation_required"])
        
    def check_authorization(self, agent_id: str, action: str, context: dict) -> bool:
        required_authorities = self.get_decision_authority(action, context.get("amount"))
        agent_authorities = self.get_agent_authorities(agent_id)
        return any(auth in agent_authorities for auth in required_authorities)

class BusinessCycleDetector:
    def detect_current_cycle(self) -> BusinessCycle:
        indicators = self.collect_indicators()
        
        cycle_signals = {
            "quarterly_end": self.is_quarter_end(indicators["date"]),
            "budget_cycle": self.detect_budget_cycle(indicators["financial"]),
            "seasonal_pattern": self.detect_seasonality(indicators["sales"]),
            "market_conditions": self.analyze_market(indicators["external"])
        }
        
        return BusinessCycle(
            primary_cycle=self.determine_primary_cycle(cycle_signals),
            intensity=self.calculate_intensity(cycle_signals),
            duration_remaining=self.estimate_duration(cycle_signals),
            impact_areas=self.identify_impact_areas(cycle_signals)
        )

financial_monitoring:
  budget_tracking:
    frequency: "daily"
    thresholds:
      warning: 0.8  # 80% of budget
      critical: 0.95  # 95% of budget
    categories:
      - operational_expenses
      - capital_expenditure
      - project_budgets
      
  cost_optimization:
    triggers:
      budget_pressure: "> 85% utilization"
      revenue_decline: "> 10% quarter_over_quarter"
      cost_spike: "> 20% increase"
    actions:
      - defer_non_critical_projects
      - optimize_resource_allocation
      - renegotiate_vendor_contracts

class StrategyTracker:
    def get_current_priorities(self) -> List[Priority]:
        return [
            Priority(
                id="digital_transformation",
                weight=0.4,
                timeline="2024_q1_q4",
                success_metrics=["automation_rate", "cost_reduction"],
                constraints=["budget_limit", "skill_availability"]
            ),
            Priority(
                id="customer_experience",
                weight=0.3,
                timeline="ongoing",
                success_metrics=["satisfaction_score", "response_time"],
                constraints=["regulatory_compliance"]
            )
        ]

class ResourceMonitor:
    def get_resource_status(self) -> ResourceStatus:
        return ResourceStatus(
            compute=self.monitor_compute_resources(),
            storage=self.monitor_storage_resources(),
            network=self.monitor_network_resources(),
            human=self.monitor_human_resources(),
            external_services=self.monitor_external_services()
        )
    
    def monitor_compute_resources(self) -> ComputeStatus:
        metrics = self.collect_metrics(['cpu', 'memory', 'gpu'])
        return ComputeStatus(
            cpu_utilization=metrics['cpu'],
            memory_utilization=metrics['memory'],
            gpu_utilization=metrics['gpu'],
            available_instances=self.count_available_instances(),
            scaling_capacity=self.get_scaling_capacity()
        )

capacity_planning:
  thresholds:
    cpu: 
      warning: 70%
      critical: 85%
      scaling_trigger: 80%
    memory:
      warning: 75%
      critical: 90%
      scaling_trigger: 85%
    storage:
      warning: 80%
      critical: 95%
      provisioning_trigger: 85%
      
  scaling_policies:
    scale_out:
      cpu_threshold: 80%
      duration: "5_minutes"
      cooldown: "10_minutes"
      max_instances: 20
    scale_in:
      cpu_threshold: 30%
      duration: "15_minutes"
      cooldown: "30_minutes"
      min_instances: 2

class ConstraintManager:
    def check_operational_constraints(self, action: Action) -> ConstraintResult:
        constraints = []
        
        # Check resource constraints
        if self.would_exceed_resource_limits(action):
            constraints.append(ResourceConstraint(
                type="insufficient_resources",
                severity="blocking",
                recommendation="wait_for_resources"
            ))
        
        # Check maintenance windows
        if self.is_maintenance_window():
            constraints.append(MaintenanceConstraint(
                type="maintenance_window",
                severity="warning",
                recommendation="defer_until_after_maintenance"
            ))
        
        # Check SLA constraints
        sla_impact = self.assess_sla_impact(action)
        if sla_impact.risk_level > 0.7:
            constraints.append(SLAConstraint(
                type="sla_risk",
                severity="high",
                recommendation="use_canary_deployment"
            ))
            
        return ConstraintResult(
            constraints=constraints,
            overall_feasibility=self.calculate_feasibility(constraints),
            recommendations=self.generate_recommendations(constraints)
        )

class MarketIntelligence:
    def gather_market_data(self) -> MarketData:
        return MarketData(
            economic_indicators=self.get_economic_indicators(),
            industry_trends=self.analyze_industry_trends(),
            competitive_landscape=self.monitor_competitors(),
            regulatory_updates=self.track_regulatory_changes(),
            technology_trends=self.analyze_tech_trends()
        )
    
    def assess_market_impact(self, action: Action) -> MarketImpact:
        market_data = self.gather_market_data()
        
        return MarketImpact(
            timing_assessment=self.assess_timing(action, market_data),
            competitive_advantage=self.assess_advantage(action, market_data),
            regulatory_compliance=self.check_compliance(action, market_data),
            risk_factors=self.identify_risks(action, market_data)
        )

regulatory_monitoring:
  frameworks:
    gdpr:
      status: "active"
      last_updated: "2024-01-01"
      key_requirements:
        - data_minimization
        - consent_management
        - breach_notification
        - data_portability
        
    sox:
      status: "active"
      last_updated: "2023-12-15"
      key_requirements:
        - financial_controls
        - audit_trails
        - segregation_of_duties
        
  compliance_checks:
    frequency: "daily"
    automated_scanning: true
    alert_thresholds:
      high_risk: "immediate"
      medium_risk: "within_24h"
      low_risk: "weekly_report"

class DataCollector:
    def __init__(self):
        self.collectors = {
            "organizational": OrganizationalDataCollector(),
            "business": BusinessDataCollector(),
            "operational": OperationalDataCollector(),
            "external": ExternalDataCollector()
        }
        
    async def collect_all(self) -> EnvironmentSnapshot:
        tasks = [
            collector.collect() 
            for collector in self.collectors.values()
        ]
        
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        return EnvironmentSnapshot(
            timestamp=datetime.utcnow(),
            organizational_data=results[0],
            business_data=results[1],
            operational_data=results[2],
            external_data=results[3],
            collection_metadata=self.generate_metadata(results)
        )

stream_processing:
  kafka_configuration:
    topics:
      - organizational_events
      - business_metrics
      - operational_alerts
      - external_updates
    
    partitions: 10
    replication_factor: 3
    retention: "7_days"
    
  processing_topology:
    organizational_stream:
      - parse_ldap_events
      - detect_structure_changes
      - update_authority_matrix
      
    business_stream:
      - parse_financial_data
      - detect_cycle_changes
      - update_constraints
      
    operational_stream:
      - parse_metrics
      - detect_anomalies
      - trigger_alerts

class FeasibilityAssessor:
    def assess_action_feasibility(self, action: Action, env: Environment) -> FeasibilityAssessment:
        assessments = {
            "organizational": self.assess_organizational_feasibility(action, env.org),
            "business": self.assess_business_feasibility(action, env.business),
            "operational": self.assess_operational_feasibility(action, env.ops),
            "external": self.assess_external_feasibility(action, env.external)
        }
        
        overall_score = self.calculate_overall_feasibility(assessments)
        
        return FeasibilityAssessment(
            overall_score=overall_score,
            dimension_scores=assessments,
            blocking_constraints=self.identify_blockers(assessments),
            recommendations=self.generate_recommendations(assessments),
            optimal_timing=self.suggest_optimal_timing(assessments)
        )
    
    def calculate_overall_feasibility(self, assessments: dict) -> float:
        # Weighted average with blocking constraint handling
        weights = {"organizational": 0.3, "business": 0.25, "operational": 0.25, "external": 0.2}
        
        # Check for blocking constraints
        for dimension, assessment in assessments.items():
            if assessment.has_blocking_constraints:
                return 0.0  # Any blocking constraint makes action infeasible
        
        # Calculate weighted score
        weighted_sum = sum(
            assessment.score * weights[dimension]
            for dimension, assessment in assessments.items()
        )
        
        return min(weighted_sum, 1.0)

class PatternAnalyzer:
    def analyze_environmental_patterns(self, history: EnvironmentHistory) -> PatternAnalysis:
        patterns = {
            "cyclical": self.detect_cyclical_patterns(history),
            "seasonal": self.detect_seasonal_patterns(history),
            "trending": self.detect_trends(history),
            "anomalous": self.detect_anomalies(history)
        }
        
        return PatternAnalysis(
            detected_patterns=patterns,
            confidence_scores=self.calculate_confidence(patterns),
            predictive_insights=self.generate_predictions(patterns),
            actionable_recommendations=self.derive_recommendations(patterns)
        )
    
    def detect_cyclical_patterns(self, history: EnvironmentHistory) -> List[CyclicalPattern]:
        # Business cycle detection
        business_cycles = self.analyze_business_cycles(history.business_data)
        
        # Resource utilization cycles
        resource_cycles = self.analyze_resource_cycles(history.operational_data)
        
        # Organizational change cycles
        org_cycles = self.analyze_org_cycles(history.organizational_data)
        
        return business_cycles + resource_cycles + org_cycles

class RiskAnalyzer:
    def analyze_environmental_risks(self, action: Action, env: Environment) -> RiskAnalysis:
        risks = []
        
        # Organizational risks
        org_risks = self.assess_organizational_risks(action, env.organizational)
        risks.extend(org_risks)
        
        # Business risks
        business_risks = self.assess_business_risks(action, env.business)
        risks.extend(business_risks)
        
        # Operational risks
        operational_risks = self.assess_operational_risks(action, env.operational)
        risks.extend(operational_risks)
        
        # External risks
        external_risks = self.assess_external_risks(action, env.external)
        risks.extend(external_risks)
        
        return RiskAnalysis(
            identified_risks=risks,
            overall_risk_score=self.calculate_overall_risk(risks),
            mitigation_strategies=self.suggest_mitigations(risks),
            monitoring_recommendations=self.suggest_monitoring(risks)
        )

class TimingOptimizer:
    def find_optimal_timing(self, action: Action, constraints: List[Constraint]) -> TimingRecommendation:
        # Generate time windows
        time_windows = self.generate_time_windows(
            start_date=datetime.now(),
            end_date=datetime.now() + timedelta(days=90),
            granularity="hour"
        )
        
        # Score each window
        scored_windows = []
        for window in time_windows:
            env_state = self.predict_environment_state(window)
            feasibility = self.assess_feasibility(action, env_state, constraints)
            risk = self.assess_risk(action, env_state)
            opportunity = self.assess_opportunity(action, env_state)
            
            score = self.calculate_timing_score(feasibility, risk, opportunity)
            scored_windows.append((window, score, feasibility, risk, opportunity))
        
        # Find optimal windows
        optimal_windows = sorted(scored_windows, key=lambda x: x[1], reverse=True)[:5]
        
        return TimingRecommendation(
            primary_recommendation=optimal_windows[0],
            alternative_windows=optimal_windows[1:],
            factors_considered=["feasibility", "risk", "opportunity"],
            confidence_level=self.calculate_confidence(optimal_windows)
        )

class OpportunityDetector:
    def detect_opportunities(self, env: Environment) -> List[Opportunity]:
        opportunities = []
        
        # Resource optimization opportunities
        if env.operational.cpu_utilization < 0.3:
            opportunities.append(Opportunity(
                type="resource_optimization",
                description="Low CPU utilization detected",
                potential_value="cost_reduction",
                confidence=0.9,
                time_sensitivity="low",
                recommended_actions=["scale_down_instances", "consolidate_workloads"]
            ))
        
        # Business cycle opportunities
        if env.business.current_cycle == "budget_planning":
            opportunities.append(Opportunity(
                type="budget_optimization",
                description="Budget planning cycle active",
                potential_value="strategic_positioning",
                confidence=0.8,
                time_sensitivity="high",
                recommended_actions=["submit_automation_proposals", "request_ai_investments"]
            ))
        
        # Market opportunities
        if env.external.market_conditions.volatility < 0.2:
            opportunities.append(Opportunity(
                type="market_expansion",
                description="Stable market conditions",
                potential_value="growth_acceleration",
                confidence=0.7,
                time_sensitivity="medium",
                recommended_actions=["expand_services", "enter_new_markets"]
            ))
        
        return self.rank_opportunities(opportunities)

environment_metrics:
  organizational:
    - policy_change_frequency
    - authority_matrix_updates
    - role_definition_changes
    - compliance_violations
    
  business:
    - budget_utilization_rate
    - revenue_trend
    - cost_optimization_savings
    - strategic_goal_progress
    
  operational:
    - resource_utilization_efficiency
    - constraint_violation_frequency
    - performance_degradation_incidents
    - capacity_planning_accuracy
    
  external:
    - market_volatility_index
    - regulatory_change_frequency
    - competitive_threat_level
    - technology_disruption_risk

class EnvironmentAlerting:
    def setup_alerts(self):
        alert_rules = [
            AlertRule(
                name="critical_constraint_detected",
                condition="constraint.severity == 'critical'",
                action="immediate_notification",
                recipients=["agent_operators", "system_administrators"]
            ),
            AlertRule(
                name="high_risk_environment",
                condition="overall_risk_score > 0.8",
                action="escalation_workflow",
                recipients=["risk_management", "business_owners"]
            ),
            AlertRule(
                name="opportunity_window_opening",
                condition="opportunity.time_sensitivity == 'high'",
                action="opportunity_notification",
                recipients=["strategic_planners", "agent_managers"]
            )
        ]
        
        for rule in alert_rules:
            self.alert_engine.register_rule(rule)

class EnvironmentMemoryIntegration:
    def store_environment_context(self, env_state: EnvironmentState, agent_id: str):
        # Store in episodic memory
        episode = Episode(
            timestamp=env_state.timestamp,
            environment_snapshot=env_state,
            agent_id=agent_id,
            context_type="environment_awareness"
        )
        self.memory_system.store_episode(episode)
        
        # Update semantic memory with learned patterns
        patterns = self.pattern_analyzer.extract_patterns(env_state)
        for pattern in patterns:
            self.memory_system.update_semantic_knowledge(pattern)
            
    def enhance_decisions_with_history(self, current_env: EnvironmentState) -> EnhancedEnvironment:
        # Retrieve similar historical environments
        similar_envs = self.memory_system.find_similar_environments(
            current_env,
            similarity_threshold=0.8,
            max_results=10
        )
        
        # Learn from historical outcomes
        historical_insights = self.analyze_historical_outcomes(similar_envs)
        
        return EnhancedEnvironment(
            current_state=current_env,
            historical_insights=historical_insights,
            learned_patterns=self.extract_patterns(similar_envs)
        )

class EnvironmentIdentityIntegration:
    def apply_identity_based_constraints(self, env: Environment, agent_id: str) -> FilteredEnvironment:
        agent_identity = self.identity_service.get_agent_identity(agent_id)
        
        # Filter environment based on agent permissions
        filtered_env = env.copy()
        
        # Apply role-based filtering
        if not agent_identity.has_permission("access_financial_data"):
            filtered_env.business.financial_data = None
            
        # Apply security clearance filtering
        if agent_identity.security_clearance < SecurityLevel.CONFIDENTIAL:
            filtered_env.external.sensitive_market_data = None
            
        # Apply organizational scope filtering
        filtered_env.organizational = self.filter_by_scope(
            filtered_env.organizational,
            agent_identity.organizational_scope
        )
        
        return filtered_env

class EnvironmentContextIntegration:
    def enrich_context_with_environment(self, context: Context, env: Environment) -> EnrichedContext:
        enriched = context.copy()
        
        # Add environmental constraints to context
        enriched.constraints.extend(env.get_active_constraints())
        
        # Adjust context relevance based on environment
        for item in enriched.items:
            env_relevance = self.calculate_environmental_relevance(item, env)
            item.relevance_score *= env_relevance
            
        # Add environmental metadata
        enriched.metadata.environment_state = env.get_summary()
        enriched.metadata.last_environment_update = env.last_update
        
        return enriched

# Check environment awareness health
sindhan-cli env health --all-layers
 
# Test constraint detection
sindhan-cli env test-constraints --action="proposed_action"
 
# Analyze environment patterns
sindhan-cli env analyze-patterns --period=30d
 
# Simulate environment scenarios
sindhan-cli env simulate --scenario="peak_load"
 
# Export environment state
sindhan-cli env export --format=json --include-history