L3 Orchestration Integration Flows

Purpose: This document shows exactly HOW the L3 orchestration components (CEO→ACS→HCS) work together in practice. Instead of theoretical architecture, we trace concrete examples through the complete system.

Quick Navigation

• Simple Query Flow — Start here: basic debugging question
• Complex Query Flow — Advanced: multi-layered research query
• Resource Pressure Flow — How system degrades gracefully
• Error Recovery Flow — What happens when things go wrong

---

Simple Query Flow

Scenario: Developer asks: "How do I handle React authentication timeouts on mobile?"

Phase 1: CEO → Intent Formation

{
  "input_query": "How do I handle React authentication timeouts on mobile?",
  "ceo_analysis": {
    "intent": {
      "type": "implementation_guidance",
      "domain": "react_authentication_mobile",
      "urgency": "medium",
      "complexity": "intermediate"
    },
    "user_context": {
      "current_file": "src/auth/AuthProvider.tsx",
      "project_type": "react-typescript",
      "experience_level": "intermediate_developer"
    },
    "resource_allocation": {
      "tokens_max": 3000,
      "time_ms": 800,
      "quality_target": "production_ready"
    },
    "risk_assessment": {
      "security_sensitive": true,
      "privacy_mode": "allow_technical",
      "code_safety": "review_required"
    }
  }
}

CEO Decision Logic:

function formulateIntent(query: string, context: UserContext): StructuredIntent {
  // 1. Semantic intent classification
  const intent_type = intentClassifier.classify(query);
  // Result: "implementation_guidance" (not "debugging", not "research")
  
  // 2. Domain extraction
  const domain = domainExtractor.extract(query);
  // Result: ["react", "authentication", "mobile", "timeouts"]
  
  // 3. Context enrichment
  const enriched_context = enrichContext(query, context);
  // Adds: current_file, project_structure, recent_errors
  
  // 4. Resource budget estimation
  const budget = budgetEstimator.estimate(intent_type, domain, context);
  // Implementation guidance + intermediate = 3000 tokens, 800ms
  
  return { intent_type, domain, context: enriched_context, budget };
}

Phase 2: ACS → Context Assembly Planning

{
  "acs_plan": {
    "source_strategy": {
      "primary": "L2_project_library",
      "secondary": "L4_experience_trails", 
      "fallback": "L1_global_knowledge"
    },
    "lod_allocation": {
      "macro": 20,  // High-level patterns
      "micro": 60,  // Implementation details  
      "atomic": 20  // Specific code examples
    },
    "parallel_requests": [
      {
        "target": "L2",
        "query": "React auth timeout handling mobile",
        "time_budget": 200,
        "token_budget": 1200,
        "lod": "micro_focused"
      },
      {
        "target": "L4", 
        "query": "authentication timeout resolution patterns",
        "time_budget": 150,
        "token_budget": 600,
        "lod": "pattern_focused"
      },
      {
        "target": "L1",
        "query": "React mobile authentication best practices",
        "time_budget": 300,
        "token_budget": 1200,
        "lod": "comprehensive",
        "trigger": "if_insufficient_coverage"
      }
    ]
  }
}

ACS Planning Algorithm:

function planContextAssembly(intent: StructuredIntent): AssemblyPlan {
  // 1. Source prioritization based on intent type
  const sources = prioritizeSources(intent);
  // Implementation guidance → L2 primary (project-specific solutions)
  
  // 2. LOD allocation based on complexity
  const lod = allocateLevelOfDetail(intent.complexity, intent.budget);
  // Intermediate complexity → 60% micro detail
  
  // 3. Parallel execution plan
  const requests = createParallelRequests(sources, lod, intent.budget);
  
  // 4. Fallback triggers
  const fallbacks = defineFallbackTriggers(intent.quality_target);
  
  return { sources, lod, requests, fallbacks };
}

Phase 3: Parallel Source Execution

L2 Project Library Response (Primary):

{
  "source": "L2_project_library",
  "execution_time": 180,
  "results": [
    {
      "fragment_id": "auth-timeout-patterns-001",
      "relevance_score": 0.94,
      "content": "Mobile auth timeout handling in React: Use exponential backoff with max 3 retries. Handle network state changes.",
      "code_examples": ["useAuthTimeout hook", "NetworkState detector"],
      "metadata": {
        "freshness": "2024-08-15",
        "validation_score": 0.91,
        "usage_frequency": 0.78
      }
    }
  ],
  "coverage_assessment": {
    "implementation_details": 0.85,
    "mobile_specific": 0.90,
    "timeout_handling": 0.95
  }
}

L4 Experience Trails Response (Secondary):

{
  "source": "L4_experience_trails",
  "execution_time": 145,
  "results": [
    {
      "trail_id": "react-auth-debug-session-042",
      "similarity": 0.89,
      "resolution_pattern": "timeout_retry_with_backoff",
      "successful_outcome": true,
      "key_insights": [
        "Mobile Safari requires different timeout values",
        "Network state changes need explicit handling",
        "User feedback during retry attempts improves UX"
      ]
    }
  ],
  "pattern_confidence": 0.87
}

L1 Global Knowledge (Triggered by insufficient mobile coverage):

{
  "source": "L1_global_knowledge", 
  "execution_time": 285,
  "trigger_reason": "mobile_specific_coverage < 0.95",
  "results": [
    {
      "fragment_id": "mobile-auth-comprehensive-001",
      "knowledge_type": "best_practices",
      "content": "Mobile authentication timeout patterns: iOS Safari 30s default, Android Chrome 60s. Network change events fire during timeouts.",
      "authoritative_sources": [
        "React Native Auth Guide v3.2",
        "MDN: Network Information API",
        "iOS Safari Technical Note TN2265"
      ]
    }
  ]
}

Phase 4: ACS → Fragment Selection & Assembly

Selection Algorithm:

function selectAndAssemble(responses: SourceResponse[]): AssembledContext {
  // 1. Score all fragments
  const scored_fragments = responses.flatMap(response => 
    response.results.map(fragment => ({
      ...fragment,
      composite_score: calculateCompositeScore(fragment, intent)
    }))
  );
  
  // 2. Diversity-aware selection  
  const selected = diversitySelect(scored_fragments, {
    max_fragments: 8,
    diversity_threshold: 0.15,
    budget_tokens: 3000
  });
  
  // 3. Optimal ordering
  const ordered = optimizeOrdering(selected, intent.lod_allocation);
  
  return { fragments: ordered, assembly_metadata };
}

function calculateCompositeScore(fragment: Fragment, intent: Intent): number {
  return (
    fragment.relevance_score * 0.4 +           // How relevant to query
    fragment.validation_score * 0.3 +          // Historical quality
    fragment.freshness_score * 0.2 +           // How recent/current
    fragment.implementation_completeness * 0.1  // How actionable
  );
}

Final Assembly:

{
  "assembled_context": {
    "total_tokens": 2847,
    "assembly_time_ms": 756,
    "fragments": [
      {
        "priority": 1,
        "source": "L2",
        "type": "implementation_pattern",
        "content": "React mobile auth timeout handling with exponential backoff...",
        "confidence": 0.94
      },
      {
        "priority": 2, 
        "source": "L4",
        "type": "experience_insight",
        "content": "Previous successful resolution: Safari requires different timeout values...",
        "confidence": 0.89
      },
      {
        "priority": 3,
        "source": "L1", 
        "type": "authoritative_reference",
        "content": "iOS Safari 30s default timeout behavior...",
        "confidence": 0.92
      }
    ]
  },
  "quality_metrics": {
    "coverage_completeness": 0.91,
    "implementation_actionability": 0.88,
    "source_diversity": 0.73
  }
}

---

Complex Query Flow

Scenario: Research query: "What are the trade-offs between different state management approaches for large React applications with real-time features?"

Phase 1: CEO → Complex Intent Analysis

{
  "complex_intent": {
    "type": "comparative_research",
    "scope": "architectural_decision", 
    "domains": ["react", "state_management", "real_time", "scalability"],
    "comparison_dimensions": ["performance", "developer_experience", "maintainability"],
    "depth_requirement": "comprehensive_analysis",
    "deliverable": "decision_framework"
  },
  "enhanced_budgets": {
    "tokens_max": 8000,  // Higher for research
    "time_ms": 2000,     // More time for multi-source analysis
    "quality_target": "expert_level"
  }
}

Phase 2: ACS → Multi-Layer Orchestration

{
  "complex_plan": {
    "orchestration_strategy": "comprehensive_synthesis",
    "source_allocation": {
      "L1_global": 40,  // Authoritative knowledge about patterns
      "L2_project": 30, // Project-specific experiences
      "L4_experience": 30 // Historical decision outcomes
    },
    "parallel_execution": [
      {
        "target": "L1",
        "sub_queries": [
          "Redux vs Zustand performance comparison",
          "Real-time state management patterns",
          "Large application architecture patterns"
        ],
        "synthesis_required": true
      },
      {
        "target": "L2", 
        "focus": "existing_implementations",
        "analysis_type": "pattern_extraction"
      },
      {
        "target": "L4",
        "focus": "decision_outcomes",
        "success_criteria": ["maintainability_scores", "team_satisfaction"]
      }
    ]
  }
}

Phase 3: Cross-Layer Synthesis

L1 Authoritative Analysis:

{
  "synthesis_result": {
    "knowledge_graph_traversal": {
      "starting_nodes": ["react_state_management", "real_time_systems"],
      "traversal_depth": 3,
      "discovered_patterns": [
        "redux_rtk_query_pattern",
        "zustand_real_time_integration", 
        "jotai_atomic_updates"
      ]
    },
    "comparative_framework": {
      "dimensions": ["bundle_size", "learning_curve", "real_time_support"],
      "solutions": ["redux_toolkit", "zustand", "jotai", "recoil"],
      "evaluation_matrix": "...detailed comparison..."
    }
  }
}

Cross-Source Integration:

function synthesizeComplexQuery(responses: MultiSourceResponse[]): SynthesizedContext {
  // 1. Extract key themes across sources
  const themes = extractCommonThemes(responses);
  
  // 2. Build comparison matrix
  const comparison = buildComparisonMatrix(themes, responses);
  
  // 3. Identify gaps and conflicts
  const analysis = analyzeGapsAndConflicts(comparison);
  
  // 4. Generate decision framework
  const framework = generateDecisionFramework(analysis);
  
  return { themes, comparison, framework, confidence_scores };
}

---

Resource Pressure Flow

Scenario: High-load situation with limited resources - budget_tokens: 1500, time_ms: 400

ACS Graceful Degradation

{
  "degradation_strategy": {
    "triggered_by": "resource_pressure",
    "adjustments": [
      {
        "type": "lod_reduction",
        "from": {"macro": 20, "micro": 60, "atomic": 20},
        "to": {"macro": 40, "micro": 50, "atomic": 10}
      },
      {
        "type": "source_prioritization", 
        "strategy": "L2_only_unless_critical_gap"
      },
      {
        "type": "fragment_limit",
        "max_fragments": 5,  // Reduced from 8
        "quality_threshold": 0.8  // Increased from 0.7
      }
    ]
  }
}

Degradation Decision Tree:

function gracefulDegradation(budget: ResourceBudget, intent: Intent): DegradationPlan {
  if (budget.tokens_max < 2000) {
    // Severe constraint
    return {
      sources: ["L2_primary"],
      lod: { macro: 60, micro: 40, atomic: 0 },
      max_fragments: 3
    };
  } else if (budget.time_ms < 500) {
    // Time pressure
    return {
      sources: ["L2_primary", "L4_if_cached"],
      parallel_limit: 2,
      timeout_aggressive: true
    };
  }
  
  return standardPlan(budget, intent);
}

---

Error Recovery Flow

Scenario: L2 Project Library temporarily unavailable

HCS Circuit Breaker & Recovery

{
  "error_scenario": {
    "failed_component": "L2_project_library",
    "error_type": "service_unavailable",
    "circuit_breaker_state": "open"
  },
  "recovery_plan": {
    "immediate_fallback": {
      "sources": ["L4_experience", "L1_global"],
      "budget_reallocation": {
        "L4": 40,  // Increased from 30
        "L1": 60   // Increased from 40
      },
      "quality_adjustment": "inform_user_of_degradation"
    },
    "circuit_breaker_recovery": {
      "test_interval": "30s",
      "success_threshold": 3,
      "fallback_timeout": "5m"
    }
  }
}

Recovery Logic:

class OrchestrationErrorRecovery {
  async handleSourceFailure(failed_source: string, intent: Intent): Promise<RecoveryPlan> {
    const circuit_state = this.circuit_breaker.getState(failed_source);
    
    if (circuit_state === 'open') {
      // Immediate fallback
      const fallback_plan = this.generateFallbackPlan(failed_source, intent);
      
      // Schedule recovery attempt
      this.scheduleRecoveryAttempt(failed_source);
      
      return fallback_plan;
    }
    
    // Progressive retry with backoff
    return this.retryWithBackoff(failed_source, intent);
  }
}

---

Production Implementation Framework

Integration Testing Strategy

describe('L3 Orchestration Integration Tests', () => {
  describe('End-to-End Flow Tests', () => {
    test('simple query complete flow', async () => {
      const query = "How do I handle React auth timeouts on mobile?";
      const context = { current_file: "src/auth/AuthProvider.tsx" };
      
      // CEO Intent Formation
      const intent = await ceo.formulateIntent(query, context);
      expect(intent.type).toBe('implementation_guidance');
      expect(intent.budget.tokens_max).toBeGreaterThan(2000);
      
      // ACS Planning
      const plan = await acs.planContextAssembly(intent);
      expect(plan.source_strategy.primary).toBe('L2_project_library');
      
      // Parallel Execution
      const responses = await acs.executeParallel(plan);
      expect(responses.length).toBeGreaterThan(0);
      expect(responses[0].source).toBe('L2_project_library');
      
      // Assembly
      const context = await acs.assembleContext(responses, intent);
      expect(context.total_tokens).toBeLessThanOrEqual(intent.budget.tokens_max);
      expect(context.quality_metrics.coverage_completeness).toBeGreaterThan(0.8);
    });
  });
  
  describe('Resource Pressure Tests', () => {
    test('graceful degradation under token pressure', async () => {
      const constrained_intent = {
        ...standard_intent,
        budget: { tokens_max: 1000, time_ms: 300 }
      };
      
      const plan = await acs.planContextAssembly(constrained_intent);
      expect(plan.lod_allocation.macro).toBeGreaterThan(0.4); // More macro focus
      expect(plan.max_fragments).toBeLessThanOrEqual(4);
    });
  });
});

Performance Monitoring

orchestration_monitoring:
  metrics:
    # Flow performance
    - name: l3_intent_formulation_duration_ms
      type: histogram
      labels: [intent_type, complexity]
      
    - name: l3_context_assembly_duration_ms  
      type: histogram
      labels: [source_count, lod_profile]
      
    # Quality metrics
    - name: l3_context_quality_score
      type: histogram
      labels: [query_type, resource_level]
      
    - name: l3_user_satisfaction_rating
      type: gauge
      labels: [flow_type]
      
  alerts:
    - name: L3OrchestrationLatencyHigh
      condition: l3_context_assembly_duration_ms{quantile="0.95"} > 1000
      severity: warning
      
    - name: L3QualityScoreLow
      condition: l3_context_quality_score{quantile="0.50"} < 0.7
      severity: warning

---

Success Criteria & Validation

Integration Success Metrics

const orchestration_success_criteria = {
  performance: {
    simple_query_latency_p95: 800,  // ms
    complex_query_latency_p95: 2000, // ms
    resource_efficiency: 0.85        // utilization ratio
  },
  quality: {
    user_satisfaction_avg: 4.2,      // out of 5.0
    coverage_completeness: 0.8,      // minimum threshold
    implementation_success_rate: 0.75 // user-reported success
  },
  reliability: {
    error_recovery_success_rate: 0.95,
    graceful_degradation_quality: 0.7, // vs. normal operation
    circuit_breaker_effectiveness: 0.9
  }
};

Validation Framework

describe('Orchestration Validation Suite', () => {
  test('validates complete integration flows', async () => {
    const test_scenarios = loadTestScenarios('./test-data/integration-scenarios.json');
    
    for (const scenario of test_scenarios) {
      const result = await orchestrator.processQuery(scenario.query, scenario.context);
      
      // Performance validation
      expect(result.total_latency_ms).toBeLessThan(scenario.max_latency);
      
      // Quality validation  
      expect(result.quality_score).toBeGreaterThan(scenario.min_quality);
      
      // Coverage validation
      expect(result.coverage_analysis.completeness).toBeGreaterThan(0.8);
    }
  });
});

---

Next Steps & Extensions

Phase 1 Implementation Priorities

1. CEO Intent Classification - Implement robust intent detection
2. ACS Basic Planning - Core planning algorithms with simple heuristics
3. Source Integration - Connect to L1/L2/L4 with proper error handling
4. Fragment Assembly - Quality-aware context assembly
5. Monitoring Integration - Basic metrics and observability

Phase 2 Enhancements

1. Learning from Outcomes - Feedback loops for continuous improvement
2. Advanced LOD Algorithms - ML-based level-of-detail optimization
3. Predictive Resource Management - Anticipatory budget allocation
4. Cross-Session Learning - User pattern recognition and adaptation

---

Status: Implementation-Ready | Complexity: High | Priority: Critical Path

This integration guide provides the concrete implementation roadmap for L3 Orchestration, showing exactly how the components work together to deliver intelligent context assembly under real-world constraints.