tractatus/demos/agent-lightning-integration/demo3-full-stack/README.md

Demo 3: Full-Stack Production Architecture

Purpose

This demo shows a production-ready implementation of Agent Lightning + Tractatus integration with:

• Complete observability (metrics, logging, tracing)
• Error handling and recovery
• Scaling considerations
• Deployment architecture
• Monitoring dashboards

Architecture

┌──────────────────────────────────────────────────────────┐
│                  PRODUCTION SYSTEM                        │
├──────────────────────────────────────────────────────────┤
│                                                           │
│  ┌─────────────────────────────────────────────────┐    │
│  │          TRACTATUS GOVERNANCE LAYER             │    │
│  │  • BoundaryEnforcer                             │    │
│  │  • PluralisticDeliberator                       │    │
│  │  • CrossReferenceValidator                      │    │
│  │  • ContextPressureMonitor                       │    │
│  │  • MetacognitiveVerifier                        │    │
│  └─────────────────────────────────────────────────┘    │
│                          ↓                               │
│  ┌─────────────────────────────────────────────────┐    │
│  │       AGENT LIGHTNING PERFORMANCE LAYER         │    │
│  │  • AgentLightningClient (training)              │    │
│  │  • AgentLightningServer (serving)               │    │
│  │  • LightningStore (data repository)             │    │
│  └─────────────────────────────────────────────────┘    │
│                          ↓                               │
│  ┌─────────────────────────────────────────────────┐    │
│  │           OBSERVABILITY LAYER                   │    │
│  │  • Prometheus metrics                           │    │
│  │  • OpenTelemetry tracing                        │    │
│  │  • Structured logging                           │    │
│  │  • Grafana dashboards                           │    │
│  └─────────────────────────────────────────────────┘    │
│                                                           │
└──────────────────────────────────────────────────────────┘

Features

1. Governance Features

• ✓ Real-time boundary enforcement
• ✓ Stakeholder deliberation workflows
• ✓ Constraint validation
• ✓ Audit trail (all decisions logged)
• ✓ Emergency stop mechanisms

2. Performance Features

• ✓ RL-based optimization
• ✓ Continuous learning
• ✓ Multi-agent coordination
• ✓ Horizontal scaling
• ✓ Load balancing

3. Observability Features

• ✓ Metrics: Performance, governance, system health
• ✓ Tracing: Request flows, decision paths
• ✓ Logging: Structured, searchable
• ✓ Dashboards: Real-time monitoring

4. Production Features

• ✓ Error recovery
• ✓ Circuit breakers
• ✓ Rate limiting
• ✓ Health checks
• ✓ Graceful degradation

Use Cases

This architecture supports:

1. High-throughput AI applications (e.g., content moderation at scale)
2. Safety-critical systems (e.g., healthcare, finance)
3. Multi-stakeholder platforms (e.g., social media, marketplaces)
4. Regulated industries (e.g., legal, government)

Running the Demo

Prerequisites

# Docker & Docker Compose (for observability stack)
docker --version
docker-compose --version

# Python environment
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Start Observability Stack

# Start Prometheus, Grafana, Jaeger
docker-compose up -d

Run System

python main.py

Access Dashboards

• Grafana: http://localhost:3000 (admin/admin)
• Prometheus: http://localhost:9090
• Jaeger: http://localhost:16686

Directory Structure

demo3-full-stack/
├── main.py                      # Main application entry point
├── requirements.txt             # Python dependencies
├── docker-compose.yml           # Observability stack
├── governance/
│   ├── __init__.py
│   ├── boundary_enforcer.py    # Production BoundaryEnforcer
│   ├── deliberator.py           # Production Deliberator
│   └── validator.py             # Production Validator
├── performance/
│   ├── __init__.py
│   ├── al_client.py             # AL client wrapper
│   └── optimizer.py             # Optimization logic
├── observability/
│   ├── __init__.py
│   ├── metrics.py               # Prometheus metrics
│   ├── tracing.py               # OpenTelemetry setup
│   └── logging.py               # Structured logging
├── config/
│   ├── governance_rules.yaml    # Governance configuration
│   ├── al_config.yaml           # AL configuration
│   └── observability.yaml       # Metrics/tracing config
└── dashboards/
    ├── governance.json          # Grafana dashboard
    ├── performance.json         # Performance metrics
    └── system-health.json       # Overall health

Key Metrics

Governance Metrics

tractatus_boundary_checks_total
tractatus_approvals_total
tractatus_rejections_total
tractatus_deliberation_duration_seconds
tractatus_constraint_violations_total

Performance Metrics

al_training_rounds_total
al_optimization_duration_seconds
al_task_success_rate
al_performance_improvement_percent

System Metrics

system_request_duration_seconds
system_error_rate
system_throughput_requests_per_second

Deployment

Docker

docker build -t governed-agent:latest .
docker run -p 8000:8000 governed-agent:latest

Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: governed-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      app: governed-agent
  template:
    metadata:
      labels:
        app: governed-agent
    spec:
      containers:
      - name: governed-agent
        image: governed-agent:latest
        ports:
        - containerPort: 8000
        env:
        - name: AL_SERVER_URL
          value: "http://al-server:8080"

Next Steps

• Deploy to staging environment
• Load testing (target: 1000 req/s)
• Security audit
• Compliance review
• Documentation finalization
• Production deployment

Files

All implementation files are in this directory. See code for production-grade examples.

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Last Updated: November 2, 2025 Purpose: Production-ready governed AI system Status: Reference architecture (implementation in progress)