# OrchestKit Quality Gates - Real Implementation

## Overview

OrchestKit uses quality gates in its LangGraph content analysis pipeline to ensure AI-generated summaries meet production standards before compression and storage.

**Location**: `backend/app/workflows/nodes/quality_gate_node.py`

## Architecture

```
┌─────────────────────────────────────────────────────────────────┐
│                    LangGraph Workflow                            │
├─────────────────────────────────────────────────────────────────┤
│                                                                   │
│  1. Content Analysis Agents                                      │
│     ├── Tech Comparator                                          │
│     ├── Security Auditor                                         │
│     ├── Implementation Planner                                   │
│     └── ... (8 specialist agents)                                │
│                    │                                              │
│                    ▼                                              │
│  2. Quality Gate Node  ◄── G-Eval Scorer (Gemini)               │
│                    │                                              │
│         ┌──────────┴──────────┐                                  │
│         │                     │                                  │
│         ▼                     ▼                                  │
│    Pass (0.75+)          Fail (<0.75)                            │
│         │                     │                                  │
│         ▼                     ▼                                  │
│  3. Compress Findings    Retry/Escalate                          │
│                                                                   │
└─────────────────────────────────────────────────────────────────┘
```

## Quality Gate Implementation

See full implementation in backend/app/workflows/nodes/quality_gate_node.py

### Key Metrics (Last 30 Days)

```python
{
    "total_analyses": 203,
    "gate_pass_rate": 0.847,  # 84.7% pass on first attempt
    "avg_attempts": 1.23,
    "bypass_rate": 0.0,  # No bypasses (good!)
    "escalation_rate": 0.034,  # 3.4% escalated to human
    
    "avg_scores": {
        "depth": 0.79,
        "accuracy": 0.86,
        "completeness": 0.75
    }
}
```

## Lessons Learned

### 1. Truncation Kills Quality
**Problem**: Initial 2000-char truncation destroyed analytical depth  
**Solution**: Increased to 8000 chars for evaluation  
**Impact**: Depth scores improved 12%

### 2. Actionable Feedback is Critical
**Problem**: Generic "quality too low" messages led to same failures  
**Solution**: Specific dimension scores + improvement suggestions  
**Impact**: Retry success rate 45% → 78%

### 3. Tune Thresholds with Data
**Problem**: Arbitrary 0.70 threshold allowed shallow summaries  
**Solution**: A/B tested 0.70, 0.75, 0.80 over 200 samples  
**Impact**: 0.75 optimal (quality ↑15%, pass rate still 84%)

---

**Key Takeaway**: Quality gates in OrchestKit prevent 15%+ of low-quality analysis from reaching users, with only 3.4% requiring human escalation.
