Advanced Agent Evaluation Dimensions:

1. Tool Usage Evaluation (Deep Dive):

# Tool Correctness Assessment Framework
tool_evaluation = {
    "tool_selection": "Did agent choose right tools?",
    "parameter_extraction": "Were parameters correct?",
    "execution_success": "Did tool calls succeed?",
    "result_interpretation": "Did agent understand results?",
    "redundancy_detection": "Any unnecessary calls?"
}

2. Path and Reasoning Evaluation:

• Path Convergence: How often does agent take optimal route?
• Reasoning Relevancy: Each step contributes to goal?
• Common Pathologies Detection:
  • ⚠️ Infinite loops (stuck in cycles)
  • ⚠️ Tool hallucination (calling non-existent tools)
  • ⚠️ Goal drift (losing focus on objective)

3. Workflow Evaluation for Multi-Step Tasks:

• Planning Quality: Can agent break down complex tasks?
• Adaptation Capability: Adjusts plan based on results?
• Error Recovery: Handles tool failures gracefully?

Advanced Agent Evaluation Techniques:

Beyond basic metrics, modern agent evaluation requires sophisticated approaches:

1️⃣ Custom G-Eval Metrics for Agents:

G-Eval allows you to define evaluation criteria in natural language, making it perfect for qualitative agent assessment:

# Example G-Eval criteria for agent evaluation
agent_quality_criteria = {
    "transparency": "Is the agent's reasoning process clear and explainable?",
    "user_friendliness": "Does the agent communicate in a helpful, non-technical way?",
    "efficiency": "Does the agent take the most direct path to completion?"
}

Use cases:

• Evaluating agent "personality" and communication style
• Assessing user satisfaction beyond task completion
• Measuring adherence to brand guidelines

2️⃣ Component Tracing and Observability:

For complex agents, you need to trace execution at a granular level:

Key tracing capabilities:

• End-to-End Tracing: Follow a request through all components
• Component Performance Isolation: Identify bottlenecks
• State Transition Monitoring: Track internal state evolution

3️⃣ Safety and Constraint Evaluation:

Autonomous agents need special safety checks:

Agent Evaluation Frameworks and Tools:

Specialized tools have emerged for agent evaluation:

Evaluation Integration Patterns:

Best practices for integration:

• Continuous Integration: Run automated tests on every commit
• A/B Testing: Compare agent versions with real traffic
• Human-in-the-Loop: Sample 5-10% for human review
• Regression Testing: Ensure changes don't break existing capabilities

🎯 Fine-tuned Model Evaluation

Decision Matrix: Should You Fine-tune?

# Catastrophic Forgetting Assessment
catastrophic_forgetting_score = {
    "general_qa_accuracy": 0.85,  # vs baseline: 0.92 ❌ (>10% drop = red flag)
    "reasoning_tasks": 0.88,      # vs baseline: 0.90 ✅ (acceptable)
    "language_understanding": 0.91, # vs baseline: 0.93 ✅ (minimal drop)
    "mathematical_ability": 0.75,  # vs baseline: 0.89 ❌ (significant drop)
    "coding_capability": 0.82,     # vs baseline: 0.87 ✅ (acceptable)
    "avg_degradation": 4.2%       # Average drop
}

# Red Flags:
# - ANY task drops >10% from baseline
# - Average degradation >5%
# - Critical capabilities completely lost

# Domain Expertise Assessment
domain_expertise_metrics = {
    # Core domain performance
    "domain_task_accuracy": 0.89,      # vs baseline: 0.72 ✅ (+17%)
    "terminology_precision": 0.93,     # Correct term usage
    "edge_case_performance": 0.78,     # vs baseline: 0.55 ✅ (+23%)
    
    # Depth indicators
    "concept_explanation_quality": 0.87,  # LLM-as-Judge
    "technical_detail_accuracy": 0.91,    # Expert validation
    "clinical_note_quality": 0.85      # Human expert rating
}

# Success Criteria:
# - Domain accuracy improvement >20% (minimum)
# - Terminology usage >90% precision
# - Edge cases improve >25%

# Style Consistency Assessment
style_metrics = {
    "tone_consistency": 0.92,          # Target: >0.85
    "format_adherence": 0.89,          # Follows templates
    "length_compliance": 0.94,         # Within target range
    "brand_phrase_usage": 0.87,        # Uses approved terminology
    "prohibited_term_avoidance": 0.98, # Avoids banned words
    "style_similarity_score": 0.87     # Compared to reference examples
}

# Evaluation Prompt for LLM-as-Judge:
"""
Evaluate if the following response matches our brand style guide:

Brand Style Criteria:
- Professional but friendly tone
- Active voice preferred
- Concise (max 3 paragraphs)
- Avoid jargon except when technical accuracy requires it
- Always end with a clear call-to-action

Response to evaluate: {response}

Score 0-1 on style adherence: [SCORE]
"""

# Overfitting Assessment
overfitting_metrics = {
    "train_accuracy": 0.95,
    "validation_accuracy": 0.88,       # Gap: 7% ✅ (acceptable)
    "test_accuracy": 0.85,             # Gap: 10% ⚠️ (monitor)
    
    "ood_performance": 0.78,           # Out-of-distribution
    "memorization_score": 0.12,        # Target: <0.20
    "novel_input_quality": 0.83        # Performance on completely new scenarios
}

# Red Flags:
# - Train-validation gap >15%
# - Train-test gap >20%
# - OOD performance drops >25%
# - Memorization score >30%

fine_tuning_roi = {
    # Costs
    "training_compute": "$5,000",
    "data_preparation": "$15,000",
    "evaluation_testing": "$8,000",
    "ongoing_maintenance": "$3,000/month",
    
    # Benefits
    "performance_gain": "+22% domain accuracy",
    "latency_reduction": "-45% (2.1s → 1.2s)",
    "cost_per_query": "-60% ($0.15 → $0.06)",
    "quality_improvement": "+18% user satisfaction",
    
    # ROI calculation
    "break_even_point": "250,000 queries",
    "monthly_savings": "$9,000",
    "payback_period": "5 months"
}

query_monitoring = {
    "avg_query_length": {
        "baseline": 45,
        "current": 62,
        "alert": "⚠️ Query length +38% - users asking more complex questions"
    },
    "out_of_domain_rate": {
        "baseline": 0.03,
        "current": 0.08,
        "alert": "🔴 OOD queries at 8% - corpus gaps detected"
    }
}

retrieval_drift_indicators = {
    # Pattern 1: Gradual quality decline
    "context_precision_trend": "0.88 → 0.85 → 0.82 → 0.78 (declining) ⚠️",
    
    # Pattern 2: Latency increase
    "retrieval_latency_p95": "420ms → 580ms → 750ms (spike) 🔴",
    
    # Pattern 3: Empty results increasing
    "zero_results_rate": "2% → 5% → 9% (growing) ⚠️",
    
    # Pattern 4: Source concentration
    "source_distribution": "Top 3 sources now 80% vs baseline 45% (concentration) ⚠️"
}

agent_health_metrics = {
    "task_completion": {
        "success_rate": 0.87,        # ✅ Above 0.85 threshold
        "partial_rate": 0.09,        # ✅ Below 0.10 threshold
        "failure_rate": 0.04,        # ✅ Below 0.05 threshold
        "trend": "stable"            # ✅ No concerning patterns
    },
    "tool_usage": {
        "selection_accuracy": 0.83,  # ⚠️ Below 0.85 threshold
        "success_rate": 0.96,        # ✅ Above 0.95 threshold
        "redundancy": 0.15,          # ⚠️ Above 0.10 threshold
        "top_failing_tool": "web_search",  # 🔍 Investigate
        "alert": "Tool selection degrading - review tool descriptions"
    }
}

catastrophic_forgetting_monitor = {
    # Multi-dimensional tracking
    "general_capabilities": {
        "qa_accuracy": {"baseline": 0.92, "current": 0.89, "delta": -3.3, "status": "⚠️"},
        "reasoning": {"baseline": 0.88, "current": 0.83, "delta": -5.7, "status": "⚠️"},
        "math": {"baseline": 0.85, "current": 0.72, "delta": -15.3, "status": "🚨"},  # Critical!
        "language": {"baseline": 0.94, "current": 0.93, "delta": -1.1, "status": "✅"}
    },
    
    # Aggregate assessment
    "avg_degradation": -6.4,  # Average drop across all tasks
    "critical_failures": 1,    # Math capability dropped >15%
    "alert_level": "🚨 CRITICAL - Math capability severely degraded",
    "recommendation": "Immediate retraining with mixed dataset (80% domain, 20% general)"
}

finetuning_roi_monitor = {
    "performance_advantage": {
        "domain_accuracy_lift": "+17%",      # vs base model
        "latency_improvement": "-43%",       # faster
        "style_consistency_lift": "+16%",    # better brand fit
        "status": "✅ Maintaining advantage"
    },
    
    "cost_tracking": {
        "inference_cost_savings": "$2,400/month",  # vs GPT-4
        "maintenance_cost": "$800/month",          # monitoring + updates
        "net_savings": "$1,600/month",             # positive ROI
        "status": "✅ Cost-effective"
    },
    
    "degradation_risk": {
        "time_since_training": "6 months",
        "performance_drift": "-8%",           # approaching retraining threshold
        "estimated_retraining_need": "2 months",
        "status": "⚠️ Plan retraining soon"
    }
}

canary_deployment = {
    "rollout_stages": [
        {"percentage": 5, "duration": "2 hours", "pass_criteria": "No critical alerts"},
        {"percentage": 25, "duration": "8 hours", "pass_criteria": "Metrics within 5% of baseline"},
        {"percentage": 50, "duration": "24 hours", "pass_criteria": "User satisfaction maintained"},
        {"percentage": 100, "duration": "ongoing", "pass_criteria": "All metrics stable"}
    ],
    
    "rollback_triggers": [
        "Error rate >2x baseline",
        "Latency P95 >1.5x baseline",
        "User satisfaction drops >10%",
        "Any critical alert"
    ]
}

2.3 AI/ML Observability

Observability is about understanding system behavior from external outputs. In AI/ML systems, this means being able to diagnose complex issues by analyzing traces, logs, and metrics across multiple layers.

Core Concepts

The Distinction from Monitoring:

The Six Layers of AI/ML Observability:

Complete observability requires visibility across multiple layers of the stack:

Why Six Layers? The Complete Diagnostic Picture:

Think of it like investigating a car problem - you need multiple perspectives:

Detailed Layer Breakdown:

🔧 Layer 1: Technical Infrastructure (Logs & Traces Level)

• What to observe: System health, resource utilization, error patterns
• Key components:
  • Inference logs (request/response pairs)
  • Server errors and exceptions
  • Resource metrics (CPU, GPU, memory)
  • API latency breakdown
• Use cases: Debugging infrastructure issues, capacity planning
• Tools: OpenTelemetry, Datadog, New Relic

🤖 Layer 2: Model Performance (ML/AI Level)

• What to observe: AI quality metrics, degradation patterns
• Key components:
  • Accuracy, precision, recall, F1-score
  • Model-specific metrics (BLEU, ROUGE for text generation)
  • Data drift detection (input distribution changes)
  • Model degradation and anomaly detection
• Use cases: Detecting when model needs retraining, A/B test validation
• Tools: MLflow, Weights & Biases, TensorBoard

📊 Layer 3: Data Quality (Data Level)

• What to observe: Input data characteristics and validity
• Key components:
  • Input distribution vs training distribution
  • Missing values, noise, anomalies
  • Feature drift and statistical tests
  • Data completeness and format validation
• Use cases: Preventing "garbage in, garbage out" scenarios
• Tools: Great Expectations, Evidently AI, Deepchecks

💡 Layer 4: Explainability & Fairness (Decision Level)

• What to observe: How and why decisions are made
• Key components:
  • Feature attributions (SHAP, LIME)
  • Bias detection across demographics (gender, age, ethnicity)
  • Fairness metrics and equitable outcomes
  • Decision transparency and interpretability
• Use cases: Building trust, debugging unexpected predictions, regulatory compliance
• Tools: SHAP, LIME, Fairlearn, AI Fairness 360

🛡️ Layer 5: Ethics & Security (Governance Level)

• What to observe: Compliance, privacy, and security
• Key components:
  • Privacy compliance (GDPR, data anonymization)
  • Security monitoring (adversarial attacks, data poisoning)
  • Ethical AI guidelines adherence
  • Responsible AI practices validation
• Use cases: Regulatory compliance, risk management, trust building
• Tools: Microsoft Presidio, AWS Macie, custom compliance frameworks

🎯 Layer 6: Business Impact (Value Level)

• What to observe: Real-world impact and ROI
• Key components:
  • Business KPIs (conversion rate, customer satisfaction, revenue)
  • Cost tracking and ROI measurement
  • User engagement metrics
  • Strategic alignment validation
• Use cases: Proving AI value, budget justification, prioritization
• Tools: Custom dashboards, BI tools (Tableau, PowerBI)

📈 The 80/20 Rule in Observability:

In our experience:

• 80% of issues can be diagnosed with Layers 1-3 (Infrastructure + Performance + Data)
• 20% of issues require Layers 4-6 (Explainability + Ethics + Business)

However, the remaining 20% are often the most critical:

• Bias issues (Layer 5) can destroy brand reputation
• Poor business impact (Layer 6) can kill the entire project
• Unexplainable decisions (Layer 4) can prevent adoption

💡 Key Principle: Start with Layers 1-3 for quick wins, but don't neglect Layers 4-6 for long-term success. Problems can originate anywhere, and symptoms in one layer often have root causes in another. The richness of information across all layers is what makes you proactive rather than reactive.

Architecture-Specific Observability Deep Dive

Now that we've covered the universal foundation, let's explore how to implement observability for different AI architectures. Each has unique challenges and observability needs.

🔍 RAG System Observability

RAG-Specific Observability Focus:

RAG systems require tracing through multiple stages (query → embedding → retrieval → context assembly → generation). Observability must capture the complete pipeline to identify failure points.

Key Observability Dimensions for RAG:

Practical RAG Observability Example:

🔍 Investigation: "Faithfulness Score Dropped to 0.65"

Trace Analysis:
├── Query Stage: ✅ Queries parsed correctly
├── Embedding: ✅ Vectors generated (45ms avg)
├── Retrieval: ⚠️ Retrieved chunks have relevance score 0.72 (baseline: 0.85)
│   └── Root Cause Found: New documents with different formatting
├── Context Assembly: ⚠️ Chunks reordered incorrectly
│   └── Root Cause Found: Missing section metadata in new docs
└── Generation: ✅ LLM generating faithfully from provided context

Conclusion: Issue at Data Ingestion → Poor chunk metadata
Fix: Re-process new documents with proper metadata extraction
Prevention: Add metadata completeness check to ingestion pipeline

🤖 Agent System Observability

Agent-Specific Observability Focus:

Agents make autonomous decisions across tools and reasoning steps. Observability must capture the decision chain, tool interactions, and state evolution.

Key Observability Dimensions for Agents:

Practical Agent Observability Example:

🔍 Investigation: "Tool Selection Accuracy Dropped to 0.76"

Trace Analysis:
├── Task: "Check weather and book restaurant"
├── Planning: ✅ Plan created: [weather_check → restaurant_search → booking]
├── Step 1 - Tool Selection:
│   ├── Available: [weather_api, web_search, restaurant_api, booking_api]
│   ├── Chosen: web_search ❌ (Should be weather_api)
│   └── Reasoning: "Agent confused - new weather_api lacks examples"
├── Step 2 - Tool Selection:
│   ├── Available: [weather_api, web_search, restaurant_api, booking_api]
│   ├── Chosen: restaurant_api ✅
└── Step 3 - Tool Selection:
    ├── Available: [weather_api, web_search, restaurant_api, booking_api]
    └── Chosen: booking_api ✅

Root Cause: New weather_api tool added without description/examples
Pattern: 18 similar failures across weather-related tasks in last 24h
Fix: Add comprehensive description + example usage to weather_api
Prevention: Tool onboarding checklist + 24h monitoring for new tools

🎯 Fine-tuned Model Observability

Fine-tuning-Specific Observability Focus:

Fine-tuned models need dual-track observability: domain performance AND general capability preservation. Must detect catastrophic forgetting early.

Key Observability Dimensions for Fine-tuned Models:

Practical Fine-tuned Model Observability Example:

🔍 Investigation: "Math Capability Critical Alert (-15.3%)"

Dual-Track Analysis:

Domain Performance (Medical):
├── Medical Diagnosis: 0.89 (baseline: 0.92) ⚠️ -3.3%
├── Terminology Usage: 0.93 (baseline: 0.94) ✅ -1.1%
└── Edge Cases: 0.78 (baseline: 0.82) ⚠️ -5.1%

General Capabilities:
├── QA Accuracy: 0.89 (baseline: 0.92) ⚠️ -3.3%
├── Reasoning: 0.83 (baseline: 0.88) ⚠️ -5.7%
├── Math: 0.72 (baseline: 0.85) 🚨 -15.3% CRITICAL
└── Language: 0.93 (baseline: 0.94) ✅ -1.1%

Root Cause Analysis:
├── Training data contained only 2% math examples
├── Fine-tuning: 10 epochs, high learning rate
└── Result: Over-optimization on medical domain + catastrophic forgetting of math

Impact Assessment:
├── 8% of production queries involve calculations
├── Math errors affecting dosage calculations (safety critical!)
└── User trust declining

Immediate Actions:
1. Roll back to previous model version for safety
2. Retrain with mixed dataset (80% medical, 20% general incl. math)
3. Add continuous math capability monitoring
4. Implement pre-deployment general capability tests

Advanced Observability Techniques

Beyond basic tracing, modern AI systems benefit from sophisticated observability approaches. Here are five advanced techniques to enhance your observability capabilities:

1️⃣ Distributed Tracing for Multi-Component Systems:

For complex architectures (RAG + Agents, or chained agents), trace across components:

Request Flow with Distributed Tracing:

Trace ID: abc-123-xyz
├── Span 1: User Query [25ms]
├── Span 2: RAG Retrieval [450ms]
│   ├── Span 2.1: Embedding [45ms]
│   ├── Span 2.2: Vector Search [380ms] ⚠️ Bottleneck!
│   └── Span 2.3: Context Assembly [25ms]
├── Span 3: Agent Planning [120ms]
├── Span 4: Tool Execution [2100ms]
│   ├── Span 4.1: API Call 1 [800ms]
│   └── Span 4.2: API Call 2 [1200ms]
└── Span 5: Final Response [80ms]

Total: 2775ms
Bottleneck: Vector Search (14% of total time)
Action: Optimize vector DB indexing

2️⃣ Anomaly Detection with Machine Learning:

Use statistical models to automatically detect unusual patterns:

3️⃣ Explainability Integration:

Connect observability to explainability for complete understanding:

Observability + Explainability Example:

Request ID: req-456
├── Observability Data:
│   ├── Prediction: "High Risk"
│   ├── Confidence: 0.87
│   ├── Latency: 320ms
│   └── Model: risk-model-v3
│
└── Explainability Data (SHAP):
    ├── Top Feature: transaction_amount (0.45 contribution)
    ├── 2nd Feature: merchant_category (0.32 contribution)
    ├── 3rd Feature: time_of_day (0.12 contribution)
    └── Counterfactual: "If amount < $500, would be Low Risk"

Combined Insight:
"High-risk prediction driven primarily by $2,500 transaction amount.
Model is working as designed for large transactions at electronics merchants."

4️⃣ Continuous Feedback Loops:

Connect observability data back to improvement cycles:

Examples of feedback loops:

• Observability → Evaluation: Detected failure patterns become new test cases
• Observability → Training: Identified weak areas trigger targeted data collection
• Observability → Monitoring: New anomalies inform alert thresholds

5️⃣ Synthetic Transaction Monitoring:

Proactively test system behavior with predefined scenarios:

4️⃣ Continuous Feedback Loops:

Create self-improving systems by connecting observability insights back to evaluation and monitoring:

Self-Improving Cycle:

Day 1: Observability detects "30% failures on queries >100 tokens"
Day 2: Root cause: Token limit issues with long queries
Day 3: Evaluation tests query truncation strategies
Day 4: Monitoring adds "query length distribution" metric
Day 5: Observability now includes query length in all traces
Day 30: System automatically handles long queries + alerts on new patterns

Result: Each issue discovered makes the system smarter

5️⃣ LLM-as-Judge for Automated Root Cause Analysis:

How it works:

• Input: Complete trace with all spans, logs, and metrics
• Analysis: LLM evaluates the entire request flow contextually
• Output: Structured diagnostic feedback with identified failure points and suggested fixes

Benefits:

• Automated diagnostics: No manual trace inspection for common issues
• Context-aware analysis: Understands relationships between components
• Natural language explanations: Makes root causes accessible to non-experts
• Pattern recognition: Learns from historical traces to identify recurring issues

Example Use Case:

Trace submitted to LLM-as-Judge:

Input: Full RAG pipeline trace with faithfulness score 0.62
LLM Analysis Output:
"Root cause identified: Retrieval stage returned chunks with relevance score <0.65.
Issue traced to recent document ingestion batch #1247 which lacks proper metadata.
3 similar patterns detected in last 48 hours affecting medical terminology queries.
Recommended action: Re-process batch #1247 with metadata extraction enabled.
Prevention: Add metadata quality gate to ingestion pipeline."

Result: Automated, actionable root cause in seconds instead of hours

Integration with observability:

• Monitoring alerts → Trigger LLM-as-Judge analysis
• LLM findings → Update evaluation criteria and monitoring metrics
• Continuous learning → Build knowledge base of trace patterns and solutions

2.4 Putting It All Together - The Transversal Nature

Now that we've explored each pillar individually, let's acknowledge the elephant in the room: these boundaries are intentionally fuzzy.

The Overlap Matrix

The Unified Mental Model

Think of the three pillars as different lenses looking at the same system:

📊 Evaluation asks: "What should good look like?"
📈 Monitoring asks: "Are we still good?"
🔍 Observability asks: "Why are we (not) good?"

Each lens provides unique value, but the magic happens when you use all three together. A metric like "answer relevance" isn't confined to one pillar—it:

• Gets defined through evaluation
• Gets tracked through monitoring
• Gets explained through observability

How Metrics Flow Through the System

Let's see how a single metric like Context Precision flows through all three pillars in practice:

Example: Context Precision in a RAG System

• As Evaluation: "Our system achieves 0.85 context precision" (baseline setting)
• As Monitoring: "Alert! Context precision dropped to 0.65" (deviation detection)
• As Observability: "Low precision traced to new document format causing chunking issues" (root cause)

This demonstrates how metrics flow through the system:

1. Evaluation establishes what "good" looks like
2. Monitoring detects when we deviate from "good"
3. Observability explains why we deviated
4. The cycle continues with improved understanding

The Complete Production Lifecycle

Here's how the three pillars work together across the entire AI lifecycle:

Key Insights:

• Pre-production: Evaluation establishes baselines and thresholds
• Production: All three pillars work continuously and interdependently
• Feedback loops: Each pillar enriches the others, creating an ascending spiral of improvement

Practical Takeaway

Don't get paralyzed by trying to perfectly categorize every metric or tool. Instead:

1. Start with Evaluation to establish what success means
2. Implement Monitoring to know when you deviate from success
3. Add Observability to understand and fix deviations
4. Iterate using insights from all three to continuously improve

The goal isn't perfect separation—it's comprehensive coverage that helps you build, maintain, and improve AI systems that deliver real value. Remember: these pillars are designed to work together, creating an ascending spiral of continuous improvement.

Part III: Maturity Model

3.1 The Journey to Evaluation Excellence

Evaluation Maturity Levels

Maturity Assessment Checklist

✅ Level 1: Ad-hoc (Getting Started)

• ☐ Manual test cases exist (minimum 50)
• ☐ Basic accuracy metrics tracked
• ☐ Testing before major releases
• ☐ Document test results

🔄 Level 2: Systematic (Building Foundation)

• ☐ Structured test suites (200+ examples)
• ☐ Multiple metrics tracked (accuracy, latency, cost)
• ☐ Evaluation framework chosen (RAGAS, DeepEval)
• ☐ Regular evaluation schedule
• ☐ Baseline metrics established

📊 Level 3: Automated (Scaling Up)

• ☐ Automated evaluation pipeline
• ☐ LLM-as-Judge implemented
• ☐ CI/CD integration complete
• ☐ A/B testing framework
• ☐ Evaluation results dashboard

🚀 Level 4: Continuous (Production Excellence)

• ☐ Production traffic sampling (10-20%)
• ☐ Real-time evaluation metrics
• ☐ Automated alerts on degradation
• ☐ User feedback integration
• ☐ Shadow model evaluation
• ☐ Cost-quality optimization

⭐ Level 5: Self-Improving (Industry Leading)

• ☐ RLHF loops implemented
• ☐ Auto-retraining triggers
• ☐ Predictive quality metrics
• ☐ Multi-model ensemble evaluation
• ☐ Automated prompt optimization
• ☐ Self-healing capabilities

3.2 Common Pitfalls and How to Avoid Them

The Pitfall Chain - What to Watch Out For:

Part IV: Implementation Guide

4.1 When to Use Which Architecture

Architecture Selection Guide

Part V: Troubleshooting Guide

5.1 Common Issues and Solutions

Troubleshooting Decision Tree:

5.2 The Feedback Loop in Action

This creates an ascending spiral of improvement, not just a loop! Each cycle:

• Adds new knowledge to your system
• Improves evaluation criteria
• Enriches monitoring capabilities
• Deepens observability insights
• Makes your AI system more robust