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Deploying to Production

This tutorial covers deploying Waxell agents to production with proper infrastructure.

Production Architecture​

┌─────────────────────────────────────────────────────────┐
│                     Load Balancer                        │
└─────────────────────────┬───────────────────────────────┘
                          │
┌─────────────────────────▼───────────────────────────────┐
│                    API Gateway                           │
│              (waxell-controlplane)                       │
└─────────────────────────┬───────────────────────────────┘
                          │
          ┌───────────────┼───────────────┐
          ▼               ▼               ▼
    ┌──────────┐    ┌──────────┐    ┌──────────┐
    │  Worker  │    │  Worker  │    │  Worker  │
    │ (Celery) │    │ (Celery) │    │ (Celery) │
    └────┬─────┘    └────┬─────┘    └────┬─────┘
         │               │               │
         └───────────────┼───────────────┘
                         ▼
          ┌──────────────────────────────┐
          │       Redis + PostgreSQL      │
          └──────────────────────────────┘

Infrastructure Requirements​

  • Redis: Task queue and rate limiting
  • PostgreSQL: Persistent storage
  • Celery Workers: Async task execution
  • Django: Control plane and API

Configuration​

Install the infra package:

pip install waxell-infra

Configure Django settings:

# settings.py
INSTALLED_APPS = [
    ...
    'waxell_infra',
]

# Redis connection
REDIS_URL = "redis://localhost:6379/0"

# Celery configuration
CELERY_BROKER_URL = REDIS_URL
CELERY_RESULT_BACKEND = REDIS_URL

Deploying an Agent​

# Register the agent
wax agent register ./my_agent.py

# Deploy to production
wax agent deploy my-agent --env production

# Monitor execution
wax agent logs my-agent --follow

Scaling​

Scale workers based on load:

# Kubernetes
kubectl scale deployment waxell-workers --replicas=10

# Docker Compose
docker-compose up -d --scale worker=10

Monitoring​

Monitor agents via the dashboard:

  • Execution metrics: Success rate, latency, throughput
  • Token usage: LLM token consumption
  • Error tracking: Failed executions and causes
  • Audit logs: All agent actions

Next Steps​