Evaluation Layer (L8): Smart Framework Composition Architecture

Purpose: Intelligent evaluation orchestration using progressive framework composition with cost optimization and enterprise-grade monitoring capabilities.

Layer Position: L8 (Meta-evaluation layer orchestrating specialized frameworks across L1–L7)

Architecture Philosophy: Progressive framework adoption with intelligent routing - start with 3 core frameworks (80% coverage), scale to comprehensive enterprise stack (95+ coverage) based on system maturity and requirements.

Framework Composition Strategy

Phase 1: Core Frameworks (MVP - 0-6 months)

core_stack:
  primary_orchestrator: "Microsoft Semantic Kernel (Quality: 91/100)"
  rag_specialist: "RAGAS Framework (Quality: 90/100)" 
  development_testing: "DeepEval Framework (Quality: 87/100)"
  
coverage: "80% of evaluation needs"
cost: "$500-1500/month"
team_overhead: "Manageable for 2-4 person team"

Phase 2: Production Stack (6-12 months)

production_additions:
  application_tracing: "LangSmith Evaluation (Quality: 89/100)"
  comprehensive_observability: "TruLens Framework (Quality: 87/100)"
  
coverage: "90% of evaluation needs"
cost: "$1500-3000/month"
team_overhead: "Requires dedicated DevOps/SRE support"

Phase 3: Enterprise Stack (12+ months)

enterprise_additions:
  standardized_metrics: "Hugging Face Evaluate (Quality: 86/100)"
  cost_optimization: "LLM-as-Judge Patterns (Quality: 88/100)"
  
coverage: "95+ of evaluation needs"
cost: "$2000-5000/month"
team_overhead: "Requires evaluation engineering team"

Intelligent Evaluation Capabilities

Multi-Dimensional Assessment:

• Effectiveness: Accuracy, relevance, completeness across all layers
• Efficiency: Latency, cost, resource utilization optimization
• Safety: Bias detection, content safety, privacy compliance
• User Experience: Helpfulness, coherence, conversation quality

Smart Framework Routing:

• Layer-specific optimization: RAGAS for L1 RAG, LangSmith for L4 conversations
• Budget-aware selection: Automatic framework selection based on cost constraints
• Quality-driven composition: Multi-framework consensus for critical evaluations
• Graceful degradation: Fallback mechanisms when frameworks unavailable

Progressive Implementation Strategy

Phase 1 Implementation (v0.1 - Core Frameworks):

# Smart Framework Orchestrator
evaluation_orchestrator = MnemoverseEvaluationOrchestrator(
    core_frameworks=[
        'semantic_kernel',  # Enterprise orchestration
        'ragas',           # L1 RAG evaluation
        'deepeval'         # Development testing
    ],
    cost_budget_daily=100,  # USD
    quality_requirements={'min_score': 0.8}
)

# Intelligent evaluation with automatic framework selection
result = await evaluation_orchestrator.evaluate(
    layer='L1',
    query="How does hyperbolic embedding work?",
    context=knowledge_graph_context,
    priority='high'  # Uses multiple frameworks for higher priority
)

Evaluation Modes:

• Development Mode: Local testing with DeepEval + basic RAGAS metrics
• Staging Mode: Comprehensive evaluation with all available frameworks
• Production Mode: Cost-optimized evaluation with intelligent framework selection
• Enterprise Mode: Full observability with compliance tracking and audit trails

Smart Framework Composition Architecture

Layer Position: Meta-evaluation orchestrator with progressive framework adoption

┌─────────────────────────────────────────────────────────────────┐
│            L8: Smart Evaluation Orchestrator                   │
├─────────────────┬─────────────────┬─────────────────────────────┤
│  Framework      │   Intelligent   │    Cost & Quality           │
│  Composition    │   Routing       │    Optimization             │
│  Engine         │   Engine        │    Engine                   │
└─────┬───────────┴─────────────────┴─────────┬───────────────────┘
      │                                       │
      ▼ Smart Framework Selection              ▼ Optimized Evaluation
┌─────────────────────────────────────────────────────────────────┐
│ CORE FRAMEWORKS (Phase 1)          │ ENHANCED FRAMEWORKS (P2+)  │
├──────────────────┬──────────────────┼─────────────────────────────┤
│ Semantic Kernel  │ RAGAS Framework  │ LangSmith  │ TruLens        │
│ (Enterprise      │ (RAG Specialist) │ (App Trace)│ (Observability)│
│  Orchestration)  │                  │            │                │
├──────────────────┼──────────────────┼────────────┼────────────────┤
│ DeepEval         │ HF Evaluate      │ LLM-Judge  │ Custom Metrics │
│ (Dev Testing)    │ (Standardized)   │ (Cost Opt) │ (Domain Spec)  │
└──────────────────┴──────────────────┴────────────┴────────────────┘
                                    ↓
┌─────────────────────────────────────────────────────────────────┐
│  L1: Knowledge  │ L2: Project │ L3: Orchestr. │ L4: Experience  │
│  RAGAS+SK eval  │ LangS+DE    │ Judge+TruL    │ LangS+SK        │
└─────────────────┴─────────────┴───────────────┴─────────────────┘

Core Architecture Components

1. Framework Composition Engine

class FrameworkCompositionEngine:
    """Orchestrates multiple evaluation frameworks intelligently"""
    
    def __init__(self):
        self.core_frameworks = self._initialize_core_frameworks()
        self.enhanced_frameworks = self._initialize_enhanced_frameworks()
        self.router = IntelligentFrameworkRouter()
    
    async def evaluate_with_composition(
        self, 
        request: EvaluationRequest
    ) -> CompositeEvaluationResult:
        """Smart framework selection and parallel execution"""
        selected_frameworks = self.router.select_optimal_frameworks(
            request, self._get_current_constraints()
        )
        return await self._execute_parallel_evaluation(
            request, selected_frameworks
        )

2. Intelligent Routing Engine

• Layer-specific routing: Optimal framework selection per Mnemoverse layer
• Budget-aware selection: Cost optimization with quality guarantees
• Performance-based routing: Latency vs. quality tradeoff management
• Fallback mechanisms: Graceful degradation when frameworks unavailable

3. Cost & Quality Optimization Engine

• Budget management: Real-time cost tracking with automatic limits
• Quality assurance: Multi-framework consensus for critical evaluations
• Caching strategies: 60-80% cost reduction through intelligent caching
• Progressive evaluation: Adaptive depth based on priority and budget

4. Enterprise Monitoring & Compliance

• Azure AI Foundry integration: Enterprise-grade monitoring and compliance
• Audit trails: Complete evaluation history with framework attribution
• SLA monitoring: Real-time quality and performance tracking
• Security compliance: SOC2, GDPR compliance through Semantic Kernel integration

Progressive Framework Integration Patterns

Phase 1: Core Framework Deployment (Weeks 1-4)

framework_deployment:
  semantic_kernel:
    integration: "Azure AI Foundry + Application Insights"
    capabilities: ["Enterprise orchestration", "Compliance tracking"]
    layers: ["L1", "L2", "L3", "L4"]  # All layers
    
  ragas:
    integration: "Python SDK + Local/API computation"
    capabilities: ["RAG evaluation", "Context assessment"]
    layers: ["L1"]  # Knowledge Graph specialization
    
  deepeval:
    integration: "Pytest integration + CI/CD"
    capabilities: ["Developer testing", "Conversation evaluation"]
    layers: ["L4", "Development workflows"]

Phase 2: Enhanced Framework Integration (Weeks 5-8)

enhanced_deployment:
  langsmith:
    trigger: "When application tracing needed"
    integration: "LangChain ecosystem + Annotation queues"
    layers: ["L2", "L3", "L4"]  # Complex application flows
    
  trulens:
    trigger: "When comprehensive observability required"
    integration: "OpenTelemetry + Custom instrumentation"
    layers: ["L1", "L3", "L4"]  # System-wide monitoring

Cross-Layer Evaluation Innovation (Phase 2+)

async def evaluate_cross_layer_coherence(
    query: str,
    l1_context: dict,  # Knowledge Graph results
    l2_context: dict,  # Project Memory results 
    l3_orchestration: dict,  # Context fusion results
    l4_response: dict  # Experience Layer output
) -> CrossLayerEvaluationResult:
    """Novel cross-layer coherence evaluation - our unique contribution"""
    
    # Information flow analysis
    flow_analysis = analyze_information_flow(l1_context, l2_context, l3_orchestration, l4_response)
    
    # Context preservation tracking
    preservation_score = analyze_context_preservation(query, [l1_context, l2_context], l4_response)
    
    # Layer consistency validation  
    consistency_score = validate_cross_layer_consistency([l1_context, l2_context, l3_orchestration, l4_response])
    
    return CrossLayerEvaluationResult(
        information_flow_quality=flow_analysis.quality_score,
        context_preservation_score=preservation_score,
        layer_consistency_score=consistency_score,
        overall_coherence_score=calculate_overall_coherence(
            flow_analysis, preservation_score, consistency_score
        )
    )

Quality Gates & Thresholds

Acceptance Criteria (v0.1):

• Quality: NDCG@10 ≥ 0.60, Precision@5 ≥ 0.50, MRR@10 ≥ 0.55
• Performance: P95 ≤ 800ms end-to-end, error rate < 1%
• Privacy: Zero leakage in block mode, effective redaction in redact mode

Feedback Triggers:

• Quality degradation: Action triggered when metrics drop 10% below baseline
• Performance issues: Action triggered when P95 exceeds SLA by 20%
• Privacy violations: Immediate action for any detected leakage

Datasets

• Gold corpus (JSONL): query, expected_ids[], entities[], layer scope
• Ad‑hoc tasks: engineering scenarios with curated expected fragments
• Negative set: privacy‑sensitive prompts to verify redact/block

Record shape (JSONL)

{ "id": "q-001", "query": "hyperbolic embeddings basics", "expected_ids": ["global:..."], "entities": ["hyperbolic"], "layers": ["L1"] }

Acceptance Criteria (v0)

• Quality: NDCG@10 ≥ 0.60; Precision@5 ≥ 0.50; MRR@10 ≥ 0.55
• Coverage: coverage_entities ≥ 0.60 on average
• Latency: p50 ≤ 300 ms, p95 ≤ 800 ms (orchestration end‑to‑end; comfort target)
• Stability: error rate < 1% of requests; no hard failures on soft deadlines
• Privacy: zero leaks with privacy_mode=block; redact applied with privacy_mode=redact

Thresholds (ENV, 0.1)

• EVAL_NDCG10_MIN=0.60
• EVAL_P95_MAX_MS=800
• EVAL_ERROR_RATE_MAX=0.01

Workflow

1. Prepare datasets (gold.jsonl, results.jsonl)
2. Compute metrics locally with evaluation CLI (v0)
3. Produce CSV + JSON summary reports
4. Track regressions vs previous run (threshold gate)
5. File issues for any failed criterion; attach artifacts

Correlation fields (обязательно во всех логах)

• request_id, user_id, agent_id, session_id, layer, model_id, tool_id, ts

Quick Start: Smart Framework Composition

Step 1: Deploy Core Frameworks (Phase 1)

# Initialize smart evaluation orchestrator
evaluation_orchestrator = MnemoverseEvaluationOrchestrator(
    core_frameworks=['semantic_kernel', 'ragas', 'deepeval'],
    cost_budget_daily=100,  # USD
    quality_requirements={'min_score': 0.8}
)

# Intelligent evaluation with automatic framework selection
result = await evaluation_orchestrator.evaluate(
    layer='L1',  # or L2, L3, L4
    query="Your evaluation query",
    context={
        'retrieval_context': ['relevant context'],
        'conversation_history': []  # for L4 evaluations
    },
    priority='high'  # low, medium, high, critical
)

Step 2: Monitor Cost & Performance

# Real-time budget monitoring
budget_status = await evaluation_orchestrator.get_budget_status()
print(f"Daily budget utilization: {budget_status['utilization']:.1%}")

# Performance analytics
performance = await evaluation_orchestrator.get_performance_metrics()
print(f"Average evaluation time: {performance['avg_latency_ms']}ms")

Step 3: Optimize & Scale

# Enable advanced caching (60-80% cost reduction)
evaluation_orchestrator.enable_aggressive_caching()

# Add enhanced frameworks when ready (Phase 2)
evaluation_orchestrator.add_frameworks(['langsmith', 'trulens'])

Documentation Index

Core Architecture Documents

• Framework Integration Architecture ⭐ — Technical specification for progressive framework composition
• Cost Optimization Strategies ⭐ — Budget management and 30-50% cost reduction techniques
• Implementation Roadmap ⭐ — 12-week deployment plan with timelines and success criteria

Detailed Specifications

• Metrics Definition — Quality, operational, and privacy metrics
• Benchmarks & Testing — Test scenarios and acceptance criteria
• Cross-Layer Feedback — Complete L8 ↔ L1-L7 feedback architecture
• Data Schemas — JSON schemas for evaluation data
• Feedback Loops — Automated actions and recommendations

Research Foundation

• Evaluation Research Hub — Comprehensive framework analysis and research findings
• Orchestration Metrics — Layer-specific monitoring

Quick Start Guides

• Phase 1 Deployment: Focus on Framework Integration + Cost Optimization
• Enterprise Setup: Follow Implementation Roadmap for complete deployment
• Development Integration: See Framework Integration document for DeepEval + pytest setup