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Cross-Layer Feedback Architecture ​

Comprehensive mapping of how Evaluation Layer (L8) provides feedback and validation to all system layers.

Feedback Flow Overview ​

L8 (Evaluation) ←→ L1 (Noosphere)
                ←→ L2 (Project Library) 
                ←→ L3 (Workshop)
                ←→ L4 (Experience)
                ←→ L5 (Memory)
                ←→ L6 (Orchestration)
                ←→ L7 (Adapters)

Principle: L8 observes all layer interactions and provides both automated corrections and manual recommendations based on quality, performance, and compliance metrics.

L8 ↔ L1 (Noosphere) Feedback ​

Signals from L1 → L8 ​

json
{
  "layer": "L1",
  "signals": {
    "search_quality": {
      "ndcg_at_10": 0.72,
      "recall_at_10": 0.68,
      "precision_at_5": 0.84
    },
    "response_times": {
      "vector_search_ms": 145,
      "graph_traversal_ms": 280,
      "ai_agent_ms": 1200
    },
    "source_quality": {
      "credibility_scores": [0.89, 0.76, 0.92],
      "citation_counts": [245, 12, 387],
      "recency_scores": [0.95, 0.45, 0.88]
    }
  }
}

Automated Actions: L8 → L1 ​

Enabled in v0.1:

  • rerank_weights_adjust: Modify semantic/BM25 blend ratio when precision drops below 0.75
  • source_blacklist: Remove sources with credibility < 0.3 or repeated quality issues (TTL: 24h)

Recommendations Only:

  • Vector embedding refresh for concept drift
  • Knowledge graph pruning for outdated relationships
  • AI agent prompt optimization for specific domains

Implementation Example ​

typescript
// L8 monitoring L1 search quality
class NoosphereFeedback {
  async processSearchResults(searchResults: L1SearchResults): Promise<FeedbackAction[]> {
    const quality = this.computeQualityMetrics(searchResults);
    const actions: FeedbackAction[] = [];
    
    if (quality.precision_at_5 < 0.75) {
      actions.push({
        type: "rerank_weights_adjust",
        target_layer: "L1",
        parameters: {
          semantic_weight: Math.max(0.4, quality.semantic_performance),
          bm25_weight: Math.min(0.6, quality.keyword_performance)
        },
        ttl_hours: 6,
        reason: `Precision@5 degraded to ${quality.precision_at_5}`
      });
    }
    
    return actions;
  }
}

L8 ↔ L2 (Project Library) Feedback ​

Signals from L2 → L8 ​

json
{
  "layer": "L2",
  "signals": {
    "indexing_quality": {
      "coverage_ratio": 0.89,
      "duplicate_detection": 0.95,
      "entity_extraction_accuracy": 0.82
    },
    "retrieval_performance": {
      "project_scope_precision": 0.76,
      "cross_project_contamination": 0.02
    },
    "content_freshness": {
      "avg_staleness_hours": 18,
      "update_lag_ms": 450
    }
  }
}

Automated Actions: L8 → L2 ​

Enabled in v0.1:

  • stale_content_flag: Mark content older than threshold for refresh
  • duplicate_merge: Automatically merge near-duplicate entries (confidence > 0.95)

Recommendations Only:

  • Re-indexing triggers for degraded entity extraction
  • Project boundary adjustments for contamination issues
  • Incremental update optimization

L8 ↔ L3 (Workshop) Feedback ​

Signals from L3 → L8 ​

json
{
  "layer": "L3",
  "signals": {
    "tool_reliability": {
      "success_rates": {"npm": 0.94, "git": 0.98, "docker": 0.87},
      "avg_execution_time": {"npm": 2400, "git": 180, "docker": 8500},
      "error_categories": {"timeout": 0.05, "permission": 0.02, "network": 0.01}
    },
    "validation_effectiveness": {
      "false_positive_rate": 0.03,
      "false_negative_rate": 0.07,
      "coverage": 0.89
    }
  }
}

Automated Actions: L8 → L3 ​

Enabled in v0.1:

  • tool_blacklist: Temporarily disable tools with success rate < 0.80 (TTL: 2h)
  • timeout_adjust: Increase timeouts for tools with high timeout rates

Recommendations Only:

  • Tool replacement suggestions for consistently poor performers
  • Validation rule updates for high false positive/negative rates
  • Tool chain optimization for performance improvements

L8 ↔ L4 (Experience) Feedback ​

Signals from L4 → L8 ​

json
{
  "layer": "L4",
  "signals": {
    "adaptation_effectiveness": {
      "mcp_success_rate": 0.91,
      "http_success_rate": 0.96,
      "websocket_stability": 0.88
    },
    "experience_quality": {
      "task_completion_rate": 0.83,
      "user_satisfaction_proxy": 0.79,
      "hint_relevance": 0.72
    },
    "learning_velocity": {
      "pattern_recognition_improvement": 0.15,
      "adaptation_speed_ms": 850
    }
  }
}

Automated Actions: L8 → L4 ​

Enabled in v0.1:

  • adapter_fallback: Switch to backup adapter when primary fails repeatedly
  • experience_weight_adjust: Modify experience influence based on quality scores

Recommendations Only:

  • Experience pattern consolidation for improved learning
  • Adapter configuration optimization
  • User interaction pattern analysis for UX improvements

L8 ↔ L5 (Memory) Feedback ​

Signals from L5 → L8 ​

json
{
  "layer": "L5",
  "signals": {
    "context_assembly": {
      "relevance_score": 0.81,
      "coherence_score": 0.78,
      "completeness": 0.85
    },
    "memory_integrity": {
      "contradiction_rate": 0.02,
      "staleness_distribution": [0.15, 0.35, 0.40, 0.10],
      "consolidation_effectiveness": 0.87
    },
    "kv_policy_performance": {
      "pin_hit_rate": 0.94,
      "eviction_accuracy": 0.89,
      "compression_ratio": 0.76
    }
  }
}

Automated Actions: L8 → L5 ​

Enabled in v0.1:

  • contradiction_flag: Mark contradictory memory entries for review
  • kv_policy_tune: Adjust pin/evict thresholds based on hit rates

Recommendations Only:

  • Memory consolidation triggers for fragmented knowledge
  • Context assembly strategy optimization
  • Long-term memory retention policy updates

L8 ↔ L6 (Orchestration) Feedback ​

Signals from L6 → L8 ​

json
{
  "layer": "L6",
  "signals": {
    "coordination_efficiency": {
      "acs_decision_quality": 0.86,
      "ceo_strategy_success": 0.79,
      "hcs_streaming_stability": 0.91
    },
    "end_to_end_performance": {
      "p95_latency_ms": 1250,
      "success_rate": 0.94,
      "user_satisfaction": 0.82
    },
    "resource_utilization": {
      "token_efficiency": 0.88,
      "cost_per_request": 0.045,
      "load_balancing": 0.92
    }
  }
}

Automated Actions: L8 → L6 ​

Enabled in v0.1:

  • strategy_downgrade: Switch to simpler strategies when complex ones fail repeatedly
  • timeout_escalation: Increase soft deadlines when system is under stress

Recommendations Only:

  • ACS provider selection algorithm improvements
  • CEO decision tree optimization
  • HCS streaming buffer size adjustments

L8 ↔ L7 (Adapters) Feedback ​

Signals from L7 → L8 ​

json
{
  "layer": "L7",
  "signals": {
    "protocol_performance": {
      "mcp_latency_p95": 230,
      "http_latency_p95": 180,
      "websocket_latency_p95": 95
    },
    "client_satisfaction": {
      "connection_stability": 0.96,
      "error_recovery": 0.91,
      "feature_completeness": 0.88
    },
    "compatibility": {
      "client_version_support": 0.93,
      "backwards_compatibility": 0.87
    }
  }
}

Automated Actions: L8 → L7 ​

Enabled in v0.1:

  • protocol_fallback: Switch protocols when one shows consistent poor performance
  • connection_retry_tune: Adjust retry policies based on failure patterns

Recommendations Only:

  • Client SDK optimization suggestions
  • Protocol version upgrade recommendations
  • Backwards compatibility maintenance priorities

Cross-Layer Correlation Analysis ​

Multi-Layer Issue Detection ​

L8 identifies issues that span multiple layers:

typescript
interface CrossLayerIssue {
  issue_id: string;
  affected_layers: string[];
  correlation_strength: number;
  root_cause_layer: string;
  propagation_path: string[];
  recommended_actions: Action[];
}

// Example: Poor end-to-end performance
const issue: CrossLayerIssue = {
  issue_id: "perf_degradation_001",
  affected_layers: ["L1", "L6", "L7"],
  correlation_strength: 0.87,
  root_cause_layer: "L1",
  propagation_path: ["L1_slow_search", "L6_timeout_escalation", "L7_client_disconnects"],
  recommended_actions: [
    { layer: "L1", action: "vector_index_rebuild", priority: "high" },
    { layer: "L6", action: "timeout_policy_review", priority: "medium" },
    { layer: "L7", action: "client_timeout_config", priority: "low" }
  ]
};

Feedback Loop Safety Mechanisms ​

Circuit Breakers ​

  • Maximum 2 automated actions per layer per hour
  • All actions have mandatory TTL (max 24 hours)
  • Automatic rollback if metrics worsen after action

Audit Trail ​

typescript
interface FeedbackAuditEntry {
  timestamp: string;
  action_id: string;
  source_layer: string;
  target_layer: string;
  action_type: string;
  trigger_metric: string;
  trigger_value: number;
  threshold: number;
  ttl_hours: number;
  outcome: "applied" | "rejected" | "rolled_back";
  impact_metrics: Record<string, number>;
}

Human Oversight ​

  • All automated actions logged to monitoring dashboard
  • Degradation alerts trigger immediate human review
  • Weekly feedback effectiveness reports
  • Quarterly feedback policy review and optimization

Implementation Timeline ​

v0.1 (Current):

  • Basic feedback loops for L1 (Noosphere) and L3 (Workshop)
  • Safety mechanisms and audit trails
  • Manual recommendation system for all layers

v0.2 (Next Quarter):

  • Automated feedback for L2, L4, L5 layers
  • Cross-layer correlation analysis
  • Advanced safety mechanisms

v1.0 (Future):

  • Full automated feedback across all layers
  • Machine learning-based feedback optimization
  • Predictive quality management

Status: Specification ready for implementation Dependencies: Layer monitoring infrastructure, metrics collection, action execution framework