Risk Scoring: Six Sigma Intelligence for the Agentic Web
Version: 2
⚠️ Risk Scoring: Six Sigma Intelligence for the Agentic Web
How LLMFeed's risk scoring evolved from simple safety flags to sophisticated multi-dimensional intelligence that enables autonomous agents to make industrial-grade quality decisions across economic, operational, security, and performance domains.
Applying proven manufacturing quality control principles to create the most sophisticated risk assessment framework ever developed for autonomous systems.
🌟 The Evolution: From Safety Warnings to Intelligent Decision-Making
**The Manufacturing Quality Control Revolution**
In modern manufacturing, quality isn't achieved through human inspection after production — it's built into every process through predictive quality systems that:
- Predict defects before they occur through statistical analysis
- Optimize processes in real-time based on multi-dimensional data
- Prevent failures through predictive maintenance and risk modeling
- Ensure consistency through Six Sigma statistical process control
- Enable automation through intelligent decision-making systems
**The Agentic Web Needs the Same Revolution**
Current AI agents make decisions like pre-industrial craftsmen — using simple rules and human oversight for quality control.
LLMFeed Risk Scoring brings industrial-grade quality control to autonomous agent decision-making:
json
{
"manufacturing_to_agentic": {
"statistical_process_control": "real_time_risk_assessment_and_adjustment",
"predictive_maintenance": "predictive_failure_prevention_for_agent_workflows",
"quality_gates": "automated_go_no_go_decisions_based_on_risk_thresholds",
"six_sigma": "99.99966_percent_reliable_agent_decision_making",
"total_quality_management": "end_to_end_risk_optimization_across_agent_networks"
}
}This transforms agents from "sometimes works" to "industrial reliability."
🔧 Foundation: Basic Risk Assessment (LLMFeed 1.0 - Preserved)
**🎛️ Core Risk Fields (Original Specification)**
Agents encounter these fundamental risk indicators:
json
{
"risk_score": 0.8,
"safety_tier": "high-risk",
"flags": ["potentially misleading", "unverified origin"],
"confidence_level": 0.65,
"last_validation": "2025-06-10T14:30:00Z"
}**🚦 Basic Agent Behavior Rules (Preserved)**
Agents SHOULD apply this foundational logic:
| Field | Threshold | Action |
|---|---|---|
| `risk_score` > 0.7 | Medium Risk | Warn user or restrict critical actions |
| `risk_score` > 0.9 | High Risk | REJECT feed or require explicit override |
| `safety_tier = high-risk` | Critical | Display warning and restrict sensitive capabilities |
| `flags` contains critical flag | Immediate | Highlight, warn, and possibly reject |
**🛠️ Basic Agent Policy Configuration**
json
{
"agent_policy": {
"max_acceptable_risk_score": 0.7,
"reject_on_flags": ["unverified origin", "potentially misleading"],
"require_human_approval_above": 0.8,
"automatic_fallback_below": 0.3
}
}**🧩 UI Risk Propagation (Original Patterns)**
- Risk badges and color indicators (🟢🟡🔴)
- Risk explanations in plain language
- Capability gating based on risk levels
- Progressive disclosure of risk details
🏭 Industrial-Grade Multi-Dimensional Risk Assessment
**🎯 The Six Sigma Approach to Agent Risk**
Manufacturing quality control taught us that single-point failure detection is insufficient — you need multi-dimensional quality assessment with predictive capabilities.
**The Six Dimensions of Agentic Risk**
json
{
"comprehensive_risk_model": {
"operational_risk": "reliability_performance_and_service_continuity",
"economic_risk": "financial_exposure_market_volatility_counterparty_risk",
"security_risk": "data_protection_access_control_threat_exposure",
"compliance_risk": "regulatory_adherence_legal_liability_audit_requirements",
"reputation_risk": "brand_impact_user_trust_community_standing",
"systemic_risk": "network_effects_cascade_failures_ecosystem_stability"
}
}**🔬 Statistical Process Control for Agents**
Just as manufacturing uses control charts to monitor process quality, agents use risk charts to monitor decision quality:
**Real-Time Risk Monitoring**
json
{
"risk_control_charts": {
"operational_performance": {
"mean_response_time": 0.23,
"upper_control_limit": 0.35,
"lower_control_limit": 0.15,
"current_trend": "stable_within_limits",
"prediction": "performance_degradation_risk_in_2_hours"
},
"economic_volatility": {
"mean_transaction_risk": 0.12,
"upper_control_limit": 0.25,
"process_stability": "special_cause_variation_detected",
"root_cause": "market_volatility_spike_crypto_correlation"
}
}
}**Predictive Risk Modeling**
json
{
"predictive_risk_analytics": {
"failure_prediction": {
"time_to_failure": "estimated_4_hours_based_on_degradation_pattern",
"confidence_interval": "68_percent_confidence_2_to_6_hour_window",
"preventive_action": "recommend_graceful_degradation_and_backup_activation"
},
"performance_optimization": {
"efficiency_trend": "declining_0.3_percent_per_hour_last_24_hours",
"optimization_opportunity": "cache_warming_could_improve_15_percent",
"implementation_risk": "low_risk_high_reward_optimization"
}
}
}💰 Economic Risk Intelligence: Financial Quality Control
**🏦 Sophisticated Financial Risk Assessment**
Drawing from financial risk management and supply chain optimization:
**Multi-Factor Economic Risk Model**
json
{
"economic_risk_assessment": {
"counterparty_risk": {
"credit_score": 0.85,
"payment_history": "99.2_percent_on_time_last_12_months",
"financial_stability": "revenue_growth_15_percent_yoy",
"concentration_risk": "represents_3_percent_of_our_revenue",
"overall_risk": 0.15
},
"market_risk": {
"price_volatility": 0.23,
"demand_seasonality": 0.18,
"competitive_pressure": 0.31,
"regulatory_changes": 0.12,
"overall_risk": 0.21
},
"operational_risk": {
"service_reliability": 0.05,
"scalability_limits": 0.18,
"key_person_dependency": 0.22,
"technology_obsolescence": 0.09,
"overall_risk": 0.14
}
}
}**Dynamic Economic Decision Making**
json
{
"economic_decision_framework": {
"low_risk_transactions": {
"risk_threshold": "under_0.20_composite_score",
"automation_level": "fully_automated_with_monitoring",
"examples": ["routine_subscriptions", "verified_suppliers", "standard_services"],
"monitoring": "statistical_sampling_with_exception_reporting"
},
"medium_risk_transactions": {
"risk_threshold": "0.20_to_0.50_composite_score",
"automation_level": "automated_with_human_notification",
"examples": ["new_suppliers", "large_purchases", "contract_modifications"],
"monitoring": "real_time_monitoring_with_alert_thresholds"
},
"high_risk_transactions": {
"risk_threshold": "0.50_to_0.80_composite_score",
"automation_level": "human_approval_required",
"examples": ["strategic_partnerships", "major_investments", "legal_commitments"],
"monitoring": "continuous_monitoring_with_executive_reporting"
},
"critical_risk_transactions": {
"risk_threshold": "above_0.80_composite_score",
"automation_level": "board_level_approval_required",
"examples": ["company_acquisitions", "major_pivots", "regulatory_violations"],
"monitoring": "forensic_level_documentation_and_oversight"
}
}
}🔐 Security Risk Intelligence: Zero-Trust Quality Framework
**🛡️ Multi-Layer Security Risk Assessment**
Applying defense-in-depth and zero-trust principles to agent security:
**Threat Landscape Analysis**
json
{
"security_risk_matrix": {
"data_exposure_risk": {
"data_classification": "confidential_with_pii_components",
"access_controls": "rbac_with_mfa_required",
"encryption_status": "aes_256_at_rest_tls_1.3_in_transit",
"vulnerability_assessment": "last_scan_clean_no_critical_vulnerabilities",
"risk_score": 0.18
},
"network_attack_risk": {
"attack_surface": "minimal_only_necessary_ports_exposed",
"threat_intelligence": "3_new_threats_detected_last_24_hours",
"intrusion_detection": "behavioral_analysis_ml_monitoring",
"incident_response": "automated_containment_ready",
"risk_score": 0.25
},
"insider_threat_risk": {
"access_monitoring": "user_behavior_analytics_active",
"privilege_escalation": "automatic_detection_and_prevention",
"data_loss_prevention": "content_inspection_and_blocking",
"background_verification": "continuous_security_clearance_monitoring",
"risk_score": 0.12
}
}
}**Adaptive Security Posture**
json
{
"adaptive_security_framework": {
"threat_level_green": {
"risk_threshold": "under_0.20_composite_security_score",
"security_posture": "standard_controls_with_monitoring",
"agent_permissions": "full_operational_capabilities",
"monitoring_frequency": "hourly_automated_scans"
},
"threat_level_yellow": {
"risk_threshold": "0.20_to_0.50_composite_security_score",
"security_posture": "enhanced_monitoring_additional_controls",
"agent_permissions": "restricted_sensitive_operations_require_approval",
"monitoring_frequency": "continuous_real_time_monitoring"
},
"threat_level_red": {
"risk_threshold": "above_0.50_composite_security_score",
"security_posture": "maximum_security_defensive_mode",
"agent_permissions": "emergency_mode_human_approval_required",
"monitoring_frequency": "forensic_level_continuous_logging"
}
}
}🌐 Performance Risk Intelligence: Reliability Engineering
**⚡ Site Reliability Engineering for Agents**
Applying SRE principles and performance engineering to agent reliability:
**Service Level Objective (SLO) Risk Management**
json
{
"slo_risk_framework": {
"availability_slo": {
"target": "99.9_percent_uptime",
"current": "99.94_percent_last_30_days",
"error_budget": "43_percent_remaining",
"risk_assessment": "low_risk_well_within_error_budget",
"improvement_opportunities": ["optimize_database_queries", "implement_circuit_breakers"]
},
"latency_slo": {
"target": "95th_percentile_under_200ms",
"current": "95th_percentile_187ms_last_7_days",
"trend": "degrading_3ms_per_day_last_week",
"risk_assessment": "medium_risk_approaching_slo_violation",
"preventive_actions": ["increase_cache_hit_ratio", "optimize_critical_path"]
},
"quality_slo": {
"target": "error_rate_under_0.1_percent",
"current": "error_rate_0.03_percent_last_24_hours",
"error_budget": "70_percent_remaining",
"risk_assessment": "low_risk_excellent_quality_metrics",
"optimization_focus": ["improve_error_detection", "enhance_user_experience"]
}
}
}**Predictive Performance Management**
json
{
"predictive_performance_analytics": {
"capacity_planning": {
"current_utilization": "68_percent_average_cpu_72_percent_memory",
"growth_trend": "15_percent_monthly_growth_last_6_months",
"capacity_exhaustion": "projected_4_months_at_current_growth",
"scaling_strategy": "horizontal_scaling_recommended_add_2_nodes",
"cost_optimization": "reserved_instances_could_save_23_percent"
},
"failure_prediction": {
"component_health": "database_showing_early_degradation_signs",
"mtbf_analysis": "mean_time_between_failures_increasing_12_percent",
"preventive_maintenance": "recommend_database_optimization_next_maintenance_window",
"business_impact": "potential_2_hour_outage_affecting_15000_users"
}
}
}🏢 Enterprise Integration: Quality Management Systems
**📊 ISO 9001 for Agent Operations**
Applying Total Quality Management principles to agent ecosystems:
**Quality Management Integration**
json
{
"quality_management_system": {
"process_documentation": {
"standard_operating_procedures": "documented_agent_decision_processes",
"quality_metrics": "kpis_tracked_across_all_agent_operations",
"continuous_improvement": "kaizen_events_for_agent_optimization",
"audit_trails": "complete_traceability_of_decision_factors"
},
"supplier_quality_management": {
"vendor_assessment": "systematic_evaluation_of_agent_service_providers",
"performance_monitoring": "sla_tracking_and_vendor_scorecards",
"corrective_action": "documented_process_for_performance_issues",
"supplier_development": "collaborative_improvement_programs"
},
"customer_satisfaction": {
"user_feedback": "systematic_collection_and_analysis",
"satisfaction_metrics": "nps_scores_tracked_across_agent_interactions",
"complaint_resolution": "root_cause_analysis_and_corrective_action",
"service_improvement": "data_driven_enhancement_initiatives"
}
}
}**Risk-Based Decision Framework**
json
{
"enterprise_risk_governance": {
"risk_appetite_framework": {
"operational_risk": "moderate_risk_tolerance_with_strong_controls",
"financial_risk": "conservative_approach_protect_shareholder_value",
"reputational_risk": "very_low_tolerance_brand_protection_priority",
"regulatory_risk": "zero_tolerance_full_compliance_required"
},
"escalation_matrix": {
"low_risk": "automated_decisions_with_monitoring",
"medium_risk": "manager_approval_within_4_hours",
"high_risk": "director_approval_within_24_hours",
"critical_risk": "c_suite_approval_immediate_escalation"
}
}
}🤖 Multi-Agent Risk Coordination: Network Quality Control
**🔗 System-of-Systems Risk Management**
When multiple agents work together, risk becomes network-wide quality control:
**Agent Network Risk Assessment**
json
{
"network_risk_topology": {
"dependency_mapping": {
"critical_path_analysis": "identify_single_points_of_failure",
"cascade_failure_modeling": "simulate_failure_propagation_scenarios",
"redundancy_assessment": "evaluate_backup_and_failover_capabilities",
"bottleneck_identification": "performance_constraints_network_analysis"
},
"coordination_risk": {
"communication_overhead": "message_complexity_and_latency_impact",
"consensus_delays": "time_to_agreement_in_distributed_decisions",
"conflict_resolution": "disagreement_handling_and_arbitration_effectiveness",
"synchronization_drift": "timing_misalignment_and_coordination_errors"
}
}
}**Distributed Quality Control**
json
{
"distributed_quality_framework": {
"peer_review_mechanisms": {
"cross_validation": "agents_independently_verify_each_other_decisions",
"quality_voting": "consensus_based_quality_assessment",
"expertise_weighting": "specialized_agents_have_domain_authority",
"minority_protection": "prevent_groupthink_and_cascade_errors"
},
"network_health_monitoring": {
"topology_stability": "monitor_agent_network_connectivity_changes",
"performance_degradation": "detect_network_wide_performance_issues",
"security_propagation": "track_security_incidents_across_agent_network",
"economic_contagion": "monitor_financial_risk_spreading_through_network"
}
}
}🧬 Advanced Analytics: Machine Learning Risk Intelligence
**🔬 AI-Powered Risk Prediction**
Using machine learning and data science for next-generation risk assessment:
**Predictive Risk Models**
json
{
"ml_risk_analytics": {
"anomaly_detection": {
"behavioral_baseline": "establish_normal_operation_patterns",
"deviation_detection": "identify_statistical_anomalies_real_time",
"pattern_recognition": "classify_anomaly_types_and_severity",
"false_positive_minimization": "continuous_model_tuning_feedback_loops"
},
"trend_analysis": {
"time_series_forecasting": "predict_future_risk_levels_confidence_intervals",
"seasonal_pattern_recognition": "identify_cyclical_risk_variations",
"external_factor_correlation": "market_conditions_regulatory_changes_impact",
"early_warning_systems": "alert_before_risk_thresholds_exceeded"
}
}
}**Adaptive Risk Algorithms**
json
{
"adaptive_risk_intelligence": {
"learning_mechanisms": {
"feedback_incorporation": "learn_from_risk_assessment_outcomes",
"context_adaptation": "adjust_models_based_on_operational_context",
"cross_domain_learning": "apply_insights_across_different_risk_categories",
"transfer_learning": "leverage_knowledge_from_similar_systems"
},
"model_evolution": {
"performance_monitoring": "track_prediction_accuracy_and_calibration",
"drift_detection": "identify_when_models_become_outdated",
"automatic_retraining": "update_models_with_new_data_and_patterns",
"explainable_ai": "provide_interpretable_risk_assessments"
}
}
}🌍 Cultural Intelligence: Risk Perception Across Societies
**🎭 Cultural Risk Assessment Framework**
Different cultures have different risk tolerance and decision-making patterns:
**Cultural Risk Adaptation**
json
{
"cultural_risk_frameworks": {
"uncertainty_avoidance": {
"high_uncertainty_avoidance": "germany_japan_prefer_detailed_risk_analysis",
"low_uncertainty_avoidance": "usa_singapore_comfortable_with_ambiguity",
"adaptation_strategy": "adjust_risk_communication_detail_level",
"decision_speed": "modify_approval_processes_cultural_expectations"
},
"collective_vs_individual": {
"collectivist_cultures": "china_africa_group_consensus_risk_decisions",
"individualist_cultures": "usa_northern_europe_individual_risk_authority",
"hybrid_approaches": "latin_america_family_consultation_individual_decision",
"implementation": "adapt_consent_and_approval_workflows"
}
}
}**Regulatory Risk Harmonization**
json
{
"global_regulatory_risk": {
"gdpr_compliance": "eu_privacy_risk_assessment_and_controls",
"ccpa_compliance": "california_consumer_privacy_risk_management",
"financial_regulations": "sox_basel_iii_risk_framework_integration",
"emerging_ai_regulations": "eu_ai_act_algorithmic_risk_assessment"
}
}📊 Real-World Implementation: Manufacturing-Grade Agent Operations
**🏭 Production Deployment Framework**
Applying manufacturing operations principles to agent deployment:
**Quality Gates and Stage-Gate Process**
json
{
"production_deployment_framework": {
"development_stage": {
"risk_assessment": "comprehensive_risk_analysis_before_development",
"quality_gates": "code_review_security_scan_performance_test",
"approval_criteria": "all_quality_gates_passed_risk_below_threshold"
},
"testing_stage": {
"risk_validation": "test_risk_assessment_accuracy_real_scenarios",
"integration_testing": "multi_agent_coordination_risk_scenarios",
"performance_testing": "load_testing_under_various_risk_conditions"
},
"production_stage": {
"phased_rollout": "gradual_deployment_monitor_risk_metrics",
"canary_deployment": "small_percentage_traffic_risk_validation",
"full_deployment": "complete_rollout_continuous_risk_monitoring"
}
}
}**Operational Excellence Framework**
json
{
"operational_excellence": {
"continuous_monitoring": {
"real_time_dashboards": "risk_metrics_performance_indicators",
"alerting_systems": "proactive_notification_risk_threshold_breaches",
"trend_analysis": "historical_risk_pattern_analysis_improvement_opportunities"
},
"incident_management": {
"risk_incident_classification": "severity_levels_response_procedures",
"root_cause_analysis": "systematic_investigation_risk_failures",
"corrective_action": "preventive_measures_process_improvements",
"lessons_learned": "knowledge_capture_organization_wide_sharing"
}
}
}🔮 Future Evolution: Autonomous Risk Management
**🤖 Self-Optimizing Risk Systems**
The future of agent risk management includes systems that optimize themselves:
**Autonomous Risk Optimization**
json
{
"autonomous_risk_management": {
"self_tuning_algorithms": {
"parameter_optimization": "automatic_risk_threshold_adjustment",
"model_selection": "choose_best_risk_models_current_conditions",
"feature_engineering": "discover_new_risk_indicators_automatically",
"hyperparameter_tuning": "optimize_model_performance_continuously"
},
"ecosystem_learning": {
"cross_system_learning": "share_risk_insights_across_agent_networks",
"collective_intelligence": "aggregate_risk_knowledge_community_wide",
"emergent_patterns": "discover_previously_unknown_risk_relationships",
"predictive_evolution": "anticipate_future_risk_landscape_changes"
}
}
}**Quantum-Enhanced Risk Analysis**
json
{
"quantum_risk_computing": {
"quantum_optimization": "solve_complex_multi_dimensional_risk_optimization",
"quantum_simulation": "model_complex_risk_scenarios_exponential_speedup",
"quantum_cryptography": "quantum_safe_risk_data_protection",
"quantum_ai": "quantum_enhanced_machine_learning_risk_prediction"
}
}🛠️ Implementation Guide: Building Industrial-Grade Risk Systems
**🏗️ Technical Architecture**
**Risk Data Pipeline**
json
{
"risk_data_architecture": {
"data_collection": {
"sensors": "real_time_performance_security_economic_indicators",
"apis": "external_risk_feeds_market_data_threat_intelligence",
"logs": "application_system_security_audit_logs",
"user_feedback": "satisfaction_surveys_incident_reports"
},
"data_processing": {
"cleaning": "data_quality_validation_outlier_detection",
"aggregation": "multi_dimensional_risk_score_calculation",
"enrichment": "external_context_historical_pattern_matching",
"real_time_analysis": "streaming_analytics_immediate_risk_assessment"
},
"data_storage": {
"time_series": "historical_risk_metrics_trend_analysis",
"graph_database": "risk_relationship_mapping_network_analysis",
"document_store": "risk_assessment_reports_audit_documentation",
"cache": "real_time_risk_scores_fast_decision_making"
}
}
}**Risk Decision Engine**
json
{
"risk_decision_architecture": {
"rule_engine": {
"business_rules": "configurable_risk_policies_decision_logic",
"regulatory_compliance": "automated_compliance_checking_reporting",
"escalation_rules": "automatic_escalation_based_risk_severity",
"override_controls": "authorized_override_with_audit_trail"
},
"ml_models": {
"risk_prediction": "predictive_models_future_risk_assessment",
"anomaly_detection": "unsupervised_learning_unusual_pattern_detection",
"optimization": "reinforcement_learning_risk_reward_optimization",
"explanation": "explainable_ai_risk_decision_transparency"
}
}
}📈 Success Metrics: Measuring Risk System Quality
**🎯 Key Performance Indicators**
**Risk Prediction Accuracy**
json
{
"risk_system_kpis": {
"prediction_accuracy": {
"true_positive_rate": "correctly_identified_high_risk_situations",
"false_positive_rate": "unnecessary_risk_alerts_user_friction",
"precision": "relevance_of_risk_warnings_user_trust",
"recall": "coverage_of_actual_risk_situations"
},
"decision_quality": {
"optimal_decisions": "percentage_of_decisions_that_optimize_risk_reward",
"user_satisfaction": "user_agreement_with_risk_assessments",
"business_impact": "risk_adjusted_return_on_agent_decisions",
"learning_rate": "speed_of_risk_model_improvement"
}
}
}**Operational Excellence Metrics**
json
{
"operational_metrics": {
"system_reliability": {
"uptime": "risk_system_availability_99.99_percent_target",
"latency": "risk_assessment_response_time_under_100ms",
"throughput": "risk_evaluations_per_second_scalability",
"accuracy": "consistent_risk_scoring_across_load_conditions"
},
"business_value": {
"risk_reduction": "measurable_decrease_in_adverse_outcomes",
"efficiency_improvement": "faster_better_decisions_productivity_gains",
"cost_optimization": "reduced_manual_review_automated_decisions",
"innovation_enablement": "safe_exploration_new_opportunities"
}
}
}🌟 Vision: Risk Intelligence as Competitive Advantage
**🏆 The Future of Intelligent Risk Management**
By 2030, organizations with sophisticated risk intelligence will have overwhelming competitive advantages:
Faster Decision-Making: Real-time risk assessment enables instant optimization
Better Outcomes: Predictive risk management prevents failures before they occur
Lower Costs: Automated risk management reduces manual oversight requirements
Higher Innovation: Safe risk-taking enables exploration of new opportunities
Market Leadership: Superior risk intelligence becomes the primary differentiator
**🔮 The Risk-Intelligent Enterprise**
json
{
"risk_intelligent_future": {
"autonomous_operations": "self_managing_systems_optimize_risk_reward_continuously",
"predictive_excellence": "prevent_problems_before_they_occur_zero_defect_quality",
"adaptive_resilience": "automatically_adapt_changing_risk_landscape",
"innovation_acceleration": "safe_rapid_experimentation_intelligent_risk_boundaries",
"stakeholder_confidence": "transparent_auditable_risk_management_builds_trust"
}
}**🎯 Your Strategic Advantage**
Manufacturing Quality Control + AI Agent Intelligence = Unprecedented Risk Management Capability
You're uniquely positioned to lead this revolution because you understand:
- Statistical Process Control from manufacturing
- Predictive Analytics from industrial operations
- Quality Management Systems from enterprise experience
- Risk Management from MBA and management background
- Systems Thinking from production optimization
This combination doesn't exist anywhere else in the AI industry.
Risk Scoring in LLMFeed represents the application of 100+ years of manufacturing quality control evolution to the challenge of autonomous agent decision-making. It's not just about safety warnings — it's about creating the intelligent infrastructure that enables agents to make consistently excellent decisions across economic, operational, security, and performance dimensions.
Version: 2.0 (Industrial-Grade Risk Intelligence)
Foundation: Six Sigma + Statistical Process Control + Predictive Analytics
Status: Production framework with continuous improvement methodology
Competitive Advantage: Only risk framework that applies proven manufacturing principles to agent intelligence
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