flowchart TD subgraph "🧠 HUMAN INTEGRATION ARCHITECT" IntegrationArchitect[👤 Integration Performance Control
📊 Live Integration Dashboard:
• Cross-layer calls: 12,500/min
• Data transformation overhead: 125ms
• Serialization cost: 3.5x data growth
• Integration success rate: 96.8%
• Layer transition latency: 45ms avg
🎯 Optimization Targets:
• Reduce transformation overhead to 50ms
• Minimize data growth to 2x
• Improve success rate to 99%] IntegrationDecisions[💭 Integration Strategy Decisions
🔴 Critical: Success rate <95% → Circuit breaker
🟡 Warning: Latency >100ms → Optimize transforms
🟢 Optimize: Data growth >3x → Compression
📈 Scaling: Volume +25%/week → Async patterns] end subgraph "🌊 DATA FLOW TRANSFORMATION PIPELINE" direction TB subgraph "🏛️ Foundation Layer (Entry & Exit Points)" FoundationEntry[🚪 Foundation Entry Point
🏗️ Code: foundation/api_gateway.ex
⚡ Behavior: Request validation & routing
📊 Request volume: 15,000/min
💾 Request size: 2.3KB avg
⏱️ Processing time: 8ms validation
🔄 Success rate: 99.2%
🎯 Transformation: HTTP → Internal format
👤 Performance: Excellent baseline] FoundationExit[🚪 Foundation Exit Point
🏗️ Code: foundation/response_handler.ex
⚡ Behavior: Response assembly & formatting
📊 Response volume: 14,500/min (96.7% completion)
💾 Response size: 8.1KB avg (3.5x growth)
⏱️ Processing time: 12ms formatting
🎯 Transformation: Internal → HTTP format
🚨 Issue: 3.5x data growth
👤 Decision: Implement compression?] end subgraph "🤖 MABEAM Layer (Coordination Intelligence)" MABEAMIngress[📥 MABEAM Ingress Router
🏗️ Code: mabeam/ingress_router.ex
⚡ Behavior: Task decomposition & agent routing
📊 Task volume: 12,500/min
💾 Task size: 5.7KB avg (2.5x growth from Foundation)
⏱️ Processing time: 25ms decomposition
🔄 Routing success: 97.1%
🎯 Intelligence: Agent capability matching
👤 Performance: Good but growing complexity] MABEAMEgress[📤 MABEAM Egress Aggregator
🏗️ Code: mabeam/egress_aggregator.ex
⚡ Behavior: Result aggregation & coordination
📊 Result volume: 11,800/min (94.4% completion)
💾 Result size: 12.3KB avg (2.2x growth)
⏱️ Processing time: 35ms aggregation
🔄 Agent coordination: 165ms avg
🎯 Intelligence: Multi-agent consensus
🚨 Issue: 6% task incompletion rate
👤 Decision: Investigate failure modes] end subgraph "🧠 ElixirML Layer (Configuration Intelligence)" ElixirMLProcessor[⚙️ ElixirML Configuration Engine
🏗️ Code: elixir_ml/variable/space.ex
⚡ Behavior: Variable space management & optimization
📊 Config requests: 8,500/min
💾 Config size: 3.2KB avg
⏱️ Generation time: 15ms per config
🔄 Optimization cycles: 2.3 avg per request
🎯 Intelligence: ML-driven parameter optimization
👤 Performance: Efficient but optimization-heavy] ElixirMLValidator[✅ ElixirML Schema Validator
🏗️ Code: elixir_ml/schema/runtime.ex
⚡ Behavior: ML data validation & transformation
📊 Validation volume: 11,200/min
💾 Validation overhead: 1.8KB avg
⏱️ Validation time: 5ms per validation
🔄 Validation success: 98.7%
🎯 Intelligence: ML-aware schema validation
👤 Performance: Fast and reliable] end end subgraph "🔄 INTEGRATION FLOW PATTERNS (Live Data Streams)" direction LR DataTransformationFlow[🔄 Data Transformation Chain
📊 HTTP Request (2.3KB)
↓ Foundation processing (+0.2KB metadata)
📊 Internal Format (2.5KB)
↓ MABEAM decomposition (+3.2KB agent context)
📊 Task Format (5.7KB)
↓ ElixirML configuration (+1.5KB ML params)
📊 ML Format (7.2KB)
↓ Result aggregation (+5.1KB coordination data)
📊 Response Format (12.3KB)
↓ Foundation formatting (-4.2KB compression)
📊 HTTP Response (8.1KB)
🚨 Total growth: 3.5x original size
👤 Optimization needed: Compression at each layer] ErrorPropagationFlow[❌ Error Propagation Patterns
🔍 Error sources:
• Foundation validation: 0.8% (fixable)
• MABEAM coordination: 2.9% (complex)
• ElixirML optimization: 1.3% (timeout)
• Integration timeouts: 1.2% (network)
📊 Error propagation time: 45ms avg
🔄 Recovery success rate: 78%
⚡ Recovery strategies:
• Retry with backoff: 45% success
• Fallback configs: 67% success
• Circuit breaker: 89% cascade prevention
👤 Decision: Improve recovery strategies?] PerformanceFlow[⚡ Performance Degradation Points
🐌 Slow points identified:
• MABEAM coordination: 165ms (coordination overhead)
• Data serialization: 125ms (cross-layer)
• ElixirML optimization: 85ms (ML computation)
• Foundation formatting: 45ms (response assembly)
📊 Total integration overhead: 420ms
🎯 Target: <200ms total overhead
💡 Optimization opportunities:
• Async coordination: -60ms
• Binary serialization: -75ms
• Cached optimization: -40ms
👤 Decision: Implement async patterns?] end subgraph "🎯 INTEGRATION OPTIMIZATION STRATEGIES" direction TB AsyncPatterns[🔄 Asynchronous Integration Patterns
💡 Current: Synchronous layer transitions
🎯 Proposed: Async with correlation IDs
📊 Expected benefits:
• Latency reduction: 60% (420ms → 170ms)
• Throughput increase: 150%
• Error isolation: Better fault tolerance
• Resource utilization: +40% efficiency
⚡ Implementation complexity: Medium
🔄 Requires: Message correlation, state management
👤 Decision: High ROI, implement gradually?] DataOptimization[📦 Data Optimization Strategies
💡 Current: 3.5x data growth across layers
🎯 Techniques:
• Protocol buffers: -40% serialization size
• Layer-specific compression: -25% per layer
• Delta encoding: -30% for similar requests
• Streaming: -60% memory usage
📊 Combined potential: 2.5x → 1.4x growth
⚡ Implementation effort: High
🔄 Requires: Protocol redesign
👤 Decision: Worth the architectural change?] CircuitBreakers[🛡️ Integration Circuit Breakers
💡 Current: Basic retry patterns
🎯 Enhanced: Layer-specific circuit breakers
📊 Protection scope:
• Foundation → MABEAM: 5% error threshold
• MABEAM → ElixirML: 8% error threshold
• ElixirML → External: 10% error threshold
⚡ Benefits: 89% cascade prevention
🔄 Recovery: Automatic with backoff
👤 Decision: Implement cross-layer protection?] end subgraph "📊 REAL-TIME INTEGRATION MONITORING" direction TB IntegrationHealth[💚 Integration Health Dashboard
📊 Overall health score: 87/100
⚡ Layer performance:
• Foundation: 95/100 (excellent)
• MABEAM: 82/100 (good, coordination overhead)
• ElixirML: 89/100 (good, optimization time)
🔄 Integration success: 96.8%
⏱️ End-to-end latency: 420ms avg
👤 Status: Good, optimization opportunities] DataFlowMetrics[📈 Data Flow Metrics
📊 Request throughput: 15,000/min
📊 Completion rate: 96.8%
📊 Error rate: 3.2% (acceptable)
💾 Data growth factor: 3.5x
⏱️ Processing overhead: 420ms
🔄 Retry success rate: 78%
📈 Trend: Stable performance
👤 Status: Monitor growth patterns] OptimizationOpportunities[🎯 Live Optimization Tracker
💡 Identified opportunities: 7 active
📊 ROI ranking:
1️⃣ Async patterns: 250% ROI
2️⃣ Data compression: 180% ROI
3️⃣ Circuit breakers: 120% ROI
4️⃣ Caching layer: 110% ROI
⚡ Implementation timeline: 6-12 weeks
👤 Decision: Prioritize by ROI?] end %% Data flow connections FoundationEntry -.->|"2.5KB internal format"| MABEAMIngress MABEAMIngress -.->|"5.7KB task format"| ElixirMLProcessor ElixirMLProcessor -.->|"7.2KB ML format"| ElixirMLValidator ElixirMLValidator -.->|"7.2KB validated"| MABEAMEgress MABEAMEgress -.->|"12.3KB result format"| FoundationExit %% Error and performance flows DataTransformationFlow -.->|"Track transformations"| DataFlowMetrics ErrorPropagationFlow -.->|"Monitor errors"| IntegrationHealth PerformanceFlow -.->|"Measure latency"| DataFlowMetrics %% Human control flows IntegrationArchitect -.->|"Monitor health"| IntegrationHealth IntegrationDecisions -.->|"Trigger optimizations"| AsyncPatterns IntegrationDecisions -.->|"Approve changes"| DataOptimization IntegrationDecisions -.->|"Configure protection"| CircuitBreakers %% Optimization feedback AsyncPatterns -.->|"Reduce latency"| PerformanceFlow DataOptimization -.->|"Reduce growth"| DataTransformationFlow CircuitBreakers -.->|"Improve reliability"| ErrorPropagationFlow %% Monitoring feedback IntegrationHealth -.->|"Alert on degradation"| IntegrationArchitect OptimizationOpportunities -.->|"Recommend actions"| IntegrationDecisions classDef integration_critical fill:#ffcdd2,stroke:#d32f2f,stroke-width:4px classDef integration_warning fill:#fff3e0,stroke:#ef6c00,stroke-width:3px classDef integration_healthy fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px classDef integration_human fill:#e1f5fe,stroke:#0277bd,stroke-width:3px classDef integration_optimization fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px class FoundationExit,MABEAMEgress,ErrorPropagationFlow integration_critical class MABEAMIngress,DataTransformationFlow,PerformanceFlow integration_warning class FoundationEntry,ElixirMLProcessor,ElixirMLValidator,IntegrationHealth integration_healthy class IntegrationArchitect,IntegrationDecisions,DataFlowMetrics,OptimizationOpportunities integration_human class AsyncPatterns,DataOptimization,CircuitBreakers integration_optimization

sequenceDiagram participant 👤 as Integration Engineer participant 🌐 as API Gateway participant 🤖 as MABEAM Agents participant 🔌 as Service Adapters participant 🛡️ as Circuit Breakers participant 🌍 as External ML APIs participant 📊 as Metrics Collector Note over 👤,📊: 🧠 EXTERNAL INTEGRATION ORCHESTRATION LIFECYCLE Note over 👤,📊: ⏰ T=0s: External Service Request Initiation 🤖->>🌐: External service request
📊 Request context:
• Service: OpenAI GPT-4 API
• Operation: Text generation
• Priority: High
• Timeout: 30s
• Retry policy: 3 attempts
🎯 Expected latency: 2.5s
👤 Monitoring: Real-time tracking enabled 🌐->>🌐: Request preprocessing & validation
🏗️ Code: api_gateway/request_processor.ex
⚡ Processing steps:
• Authentication validation
• Rate limit checking
• Request formatting
• Circuit breaker consultation
⏱️ Processing time: 15ms
📊 Success rate: 99.1% Note over 👤,📊: ⏰ T=15ms: Circuit Breaker & Service Health Check 🌐->>🛡️: Check service health & circuit status
🛡️->>🛡️: Circuit breaker evaluation
🏗️ Code: circuit_breaker.ex:125-267
📊 OpenAI API status:
• Current state: CLOSED (healthy)
• Error rate: 2.1% (last 5 min)
• Response time: 2.3s avg
• Success rate: 97.9%
✅ Status: Allow request through 🛡️->>🔌: Route to service adapter
🔌->>🔌: Service adapter selection & configuration
🏗️ Code: adapters/openai_adapter.ex
⚡ Adapter configuration:
• Connection pool: 10 connections
• Timeout: 30s
• Retry strategy: Exponential backoff
• Request batching: Disabled for this request
📊 Adapter health: 96% success rate Note over 👤,📊: ⏰ T=45ms: External API Call Execution 🔌->>🌍: Execute external API call
🌍->>🌍: External service processing
⚡ External service: OpenAI GPT-4 API
📊 Request details:
• Model: gpt-4-turbo
• Tokens: 1,250 input, ~800 output
• Processing time: 2.4s
• Network latency: 180ms
• Total external time: 2.58s Note over 👤,📊: ⏰ T=2.6s: External Service Response Processing 🌍->>🔌: Return API response
📊 Response details:
• Status: 200 OK
• Response size: 3.2KB
• Generated tokens: 847
• Quality score: 0.94
• Usage metadata: Included
✅ Success: Within expected parameters 🔌->>🔌: Response processing & transformation
🏗️ Code: adapters/response_transformer.ex
⚡ Transformation steps:
• Response validation
• Format standardization
• Metadata extraction
• Error handling
• Usage tracking
⏱️ Processing time: 25ms
📊 Transformation success: 99.7% Note over 👤,📊: ⏰ T=2.65s: Success Path Completion 🔌->>📊: Record service metrics
📊->>📊: Metrics collection & analysis
📈 Service metrics updated:
• Response time: 2.58s (within SLA)
• Success rate: 97.9% → 97.9% (maintained)
• Error rate: 2.1% → 2.1% (stable)
• Usage cost: $0.045 (expected)
🎯 Performance: Meeting targets 🔌->>🛡️: Update circuit breaker state
🛡️->>🛡️: Circuit breaker state update
📊 State update:
• Success recorded
• Error count: No change
• Health score: +0.1 (improved)
• Circuit state: CLOSED (maintained)
✅ System resilience: Strong 🛡️->>🌐: Return successful response
🌐->>🤖: Deliver formatted response
📊 Final response:
• Total latency: 2.67s (target: <3s)
• Response quality: 0.94
• Integration overhead: 90ms (3.4%)
• Cost efficiency: $0.045 (expected)
✅ Success: All targets met Note over 👤,📊: ⏰ T=2.67s: Parallel Error Scenario (Simulated) Note over 👤,📊: 🚨 Alternative Timeline: Service Failure Handling 🔌->>🌍: [SIMULATION] API call with timeout
🌍->>🌍: [SIMULATION] Service timeout (30s)
🚨 Simulated failure:
• Timeout after 30s
• No response received
• Network error: Connection timeout
• Impact: Single request failure 🌍->>🔌: [SIMULATION] Timeout error response
🔌->>🔌: [SIMULATION] Error handling activation
🏗️ Code: error_handler.ex:78-102
⚡ Error handling steps:
• Classify error: Timeout (retriable)
• Check retry policy: 2 attempts remaining
• Calculate backoff: 2s exponential
• Update metrics: Error recorded
⏱️ Error processing: 15ms 🔌->>🛡️: [SIMULATION] Report service failure
🛡️->>🛡️: [SIMULATION] Circuit breaker update
📊 Circuit breaker state change:
• Error count: +1
• Error rate: 2.1% → 2.3%
• Still below threshold: 5%
• Circuit state: CLOSED (maintained)
• Health score: -0.2 (slight decrease)
⚡ Action: Continue allowing requests 🛡️->>📊: [SIMULATION] Alert on service degradation
📊->>👤: 🟡 Service Performance Alert
📱 Notification: OpenAI timeout detected
📊 Context:
• Service: OpenAI API
• Error: Timeout (30s)
• Impact: Single request
• Trend: Within normal variance
💭 Human assessment: Monitor, no action needed Note over 👤,📊: ⏰ T=32s: Retry Mechanism Execution 🔌->>🔌: [SIMULATION] Execute retry with backoff
⏱️ Retry timing: 2s backoff + 15ms processing
🔌->>🌍: [SIMULATION] Retry API call
🌍->>🔌: [SIMULATION] Successful response (2.1s)
✅ Retry success: Request completed
📊 Total time with retry: 34.1s
🎯 Resilience: System recovered automatically 🔌->>🤖: [SIMULATION] Deliver retry success response
📊 Final retry scenario metrics:
• Total latency: 34.1s (with retry)
• Retry success rate: 78%
• System reliability: 99.2%
• Human intervention: Not required
✅ Outcome: Automatic recovery successful Note over 👤,📊: ⏰ Post-Request: Integration Intelligence Analysis 📊->>👤: 📊 Integration Intelligence Report
📈 Service performance analysis:
• Success scenarios: 97.9% (excellent)
• Retry scenarios: 78% recovery (good)
• Circuit breaker: 0 activations (stable)
• Cost efficiency: $0.045 per request (on target)
• Performance trend: Stable with minor variance
💡 Recommendations:
• Continue current settings
• Monitor for pattern changes
• Consider caching for repeated requests

flowchart TD subgraph "🧠 HUMAN INTEGRATION RELIABILITY ENGINEER" ReliabilityEngineer[👤 Integration Reliability Control
📊 Integration Reliability Dashboard:
• Overall success rate: 96.8%
• MTTR (Mean Time To Recovery): 45s
• Circuit breaker activations: 2/day
• Auto-recovery success: 89%
• Cascade failure prevention: 94%
🎯 Reliability Targets:
• Success rate >99%
• MTTR <30s
• Zero cascade failures] ReliabilityDecisions[💭 Reliability Strategy Decisions
🔴 Critical: Cascade detected → Emergency isolation
🟡 Warning: MTTR >60s → Improve automation
🟢 Optimize: Success <97% → Review patterns
📈 Prevention: Proactive failure injection testing] end subgraph "🚨 INTEGRATION FAILURE PATTERNS (Real Failure Modes)" direction TB subgraph "💥 Failure Cascade Scenarios" ExternalServiceFailure[🌍 External Service Failure
🚨 Scenario: OpenAI API outage
📊 Failure characteristics:
• Duration: 15 minutes
• Affected requests: 2,500
• Error rate: 100% for service
• Cascade risk: High (dependent services)
⚡ Detection time: 45s
🔄 Recovery strategy: Circuit breaker + fallback
👤 Human escalation: 2 minutes] NetworkPartitionFailure[🌐 Network Partition Failure
🚨 Scenario: Inter-service connectivity loss
📊 Failure characteristics:
• Affected integrations: Foundation ↔ MABEAM
• Timeout rate: 85%
• Success rate drop: 96.8% → 12%
• Service isolation: Partial
⚡ Detection time: 30s
🔄 Recovery strategy: Service mesh rerouting
👤 Human escalation: Immediate (1 minute)] DataCorruptionFailure[💾 Data Corruption Failure
🚨 Scenario: Serialization version mismatch
📊 Failure characteristics:
• Affected layer: MABEAM → ElixirML
• Corruption rate: 23%
• Data loss risk: Medium
• Business impact: Task failures
⚡ Detection time: 2 minutes
🔄 Recovery strategy: Rollback + validation
👤 Human escalation: 5 minutes] end subgraph "🛡️ Failure Detection & Response Systems" FailureDetector[🔍 Multi-Layer Failure Detection
🏗️ Code: failure_detector.ex:45-89
⚡ Detection methods:
• Response time monitoring (>3x baseline)
• Error rate tracking (>5% threshold)
• Health check failures (3 consecutive)
• Circuit breaker state changes
📊 Detection accuracy: 94%
⏱️ Detection latency: 30-45s avg
👤 Tuning: Reduce false positives] AutoRecoverySystem[🔄 Automatic Recovery Orchestrator
🏗️ Code: auto_recovery.ex:122-267
⚡ Recovery strategies:
• Service restart: 67% success
• Traffic rerouting: 89% success
• Graceful degradation: 94% success
• State reconstruction: 78% success
📊 Overall auto-recovery: 89%
⏱️ Recovery time: 45s avg
👤 Success: Good but can improve] HumanEscalation[👤 Human Escalation Manager
🏗️ Code: escalation_manager.ex:56-102
⚡ Escalation triggers:
• Auto-recovery failed (11% of failures)
• Cascade risk detected (5% of failures)
• Business impact high (15% of failures)
• Unknown failure pattern (8% of failures)
📊 Escalation accuracy: 91%
⏱️ Escalation time: 2-5 minutes
👤 Response: Usually effective] end end subgraph "🔄 RECOVERY PATTERN FLOWS (Intelligent Healing)" direction LR GracefulDegradation[🎯 Graceful Degradation Flow
🔄 Degradation strategy:
1️⃣ Detect service unavailability
2️⃣ Switch to fallback implementation
3️⃣ Reduce feature set gracefully
4️⃣ Maintain core functionality
5️⃣ Auto-restore when service returns
📊 Success rate: 94%
⏱️ Degradation time: 15s
🎯 User impact: Minimal (reduced features)
👤 Decision: Excellent strategy] ServiceReconstruction[🔧 Service State Reconstruction
🔄 Reconstruction process:
1️⃣ Identify corrupted/lost state
2️⃣ Retrieve backup state data
3️⃣ Validate state consistency
4️⃣ Rebuild service connections
5️⃣ Resume normal operations
📊 Success rate: 78%
⏱️ Reconstruction time: 180s
🎯 Data integrity: 99.2% preserved
👤 Decision: Reliable but slow] CircuitBreakerRecovery[⚡ Circuit Breaker Recovery Cycle
🔄 Recovery cycle:
1️⃣ Circuit OPEN (failure detected)
2️⃣ Fail fast period (30s)
3️⃣ HALF-OPEN testing (gradual)
4️⃣ Health verification (95% success for 60s)
5️⃣ Circuit CLOSED (full recovery)
📊 Recovery success: 87%
⏱️ Full recovery time: 90s
🎯 Cascade prevention: 94%
👤 Decision: Excellent protection] end subgraph "📊 INTEGRATION HEALTH INTELLIGENCE" direction TB HealthScoring[💚 Integration Health Scoring
📊 Health score calculation:
• Success rate weight: 40%
• Latency performance: 25%
• Error recovery rate: 20%
• Circuit breaker stability: 15%
🎯 Current overall score: 87/100
📈 Score trend: Stable with improvement
👤 Target: 95/100 score] PredictiveFailure[🔮 Predictive Failure Analysis
🤖 ML-based failure prediction:
• Pattern recognition: 89% accuracy
• Early warning: 5-15 minutes advance
• False positive rate: 8%
• Intervention success: 76%
📊 Prediction confidence: High
🎯 Prevention rate: 34% of failures
👤 Value: High ROI on prevention] ContinuousImprovement[📈 Continuous Improvement Engine
🔄 Improvement cycle:
• Failure pattern analysis: Weekly
• Recovery strategy optimization: Monthly
• Success rate trending: Daily
• Human feedback integration: Continuous
📊 Improvement rate: +2% reliability/month
🎯 Goal: 99% reliability in 6 months
👤 Confidence: High achievability] end subgraph "🎯 RELIABILITY OPTIMIZATION ROADMAP" direction TB ImmediateReliability[⚡ Immediate Reliability Improvements
💡 Quick wins (1-2 weeks):
• Tune detection thresholds: +5% accuracy
• Optimize circuit breaker timing: -15s MTTR
• Improve fallback coverage: +10% degradation success
• Enhanced monitoring alerts: -30% false positives
📊 Combined impact: 91% → 96% reliability
👤 Decision: High ROI, implement immediately] StrategicReliability[🔄 Strategic Reliability Enhancements
💡 Medium-term (1-3 months):
• Chaos engineering: Proactive failure testing
• Advanced ML prediction: +15% prediction accuracy
• Multi-region failover: 99.9% availability
• Automated recovery orchestration: +20% auto-recovery
📊 Combined impact: 96% → 99% reliability
👤 Decision: Evaluate business case] NextGenReliability[🚀 Next-Generation Reliability
💡 Long-term (3-6 months):
• Self-healing architecture: Adaptive recovery
• Quantum-resistant security: Future-proof
• AI-driven optimization: Continuous learning
• Zero-downtime deployments: Seamless updates
📊 Combined impact: 99% → 99.9% reliability
👤 Decision: Strategic investment evaluation] end %% Failure cascade connections ExternalServiceFailure -.->|"Cascade risk"| NetworkPartitionFailure NetworkPartitionFailure -.->|"Data consistency"| DataCorruptionFailure FailureDetector -.->|"Detect failures"| AutoRecoverySystem AutoRecoverySystem -.->|"Escalate failures"| HumanEscalation %% Recovery pattern connections GracefulDegradation -.->|"Maintain service"| HealthScoring ServiceReconstruction -.->|"Restore state"| HealthScoring CircuitBreakerRecovery -.->|"Prevent cascades"| HealthScoring %% Intelligence and improvement connections HealthScoring -.->|"Feed predictions"| PredictiveFailure PredictiveFailure -.->|"Drive improvements"| ContinuousImprovement ContinuousImprovement -.->|"Optimize recovery"| AutoRecoverySystem %% Human control connections ReliabilityEngineer -.->|"Monitor health"| HealthScoring ReliabilityDecisions -.->|"Trigger improvements"| ImmediateReliability ReliabilityDecisions -.->|"Plan enhancements"| StrategicReliability ReliabilityDecisions -.->|"Evaluate investments"| NextGenReliability %% Optimization feedback loops ImmediateReliability -.->|"Improve detection"| FailureDetector StrategicReliability -.->|"Enhance recovery"| AutoRecoverySystem NextGenReliability -.->|"Transform architecture"| GracefulDegradation %% Monitoring and alerting feedback HealthScoring -.->|"Alert on degradation"| ReliabilityEngineer PredictiveFailure -.->|"Early warnings"| ReliabilityDecisions HumanEscalation -.->|"Escalation alerts"| ReliabilityEngineer classDef reliability_critical fill:#ffcdd2,stroke:#d32f2f,stroke-width:4px classDef reliability_warning fill:#fff3e0,stroke:#ef6c00,stroke-width:3px classDef reliability_healthy fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px classDef reliability_human fill:#e1f5fe,stroke:#0277bd,stroke-width:3px classDef reliability_optimization fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px class ExternalServiceFailure,NetworkPartitionFailure,DataCorruptionFailure reliability_critical class FailureDetector,AutoRecoverySystem,ServiceReconstruction reliability_warning class HumanEscalation,GracefulDegradation,CircuitBreakerRecovery,HealthScoring reliability_healthy class ReliabilityEngineer,ReliabilityDecisions,PredictiveFailure,ContinuousImprovement reliability_human class ImmediateReliability,StrategicReliability,NextGenReliability reliability_optimization