Your Python Workflow Crashes Between Steps. Here Is Why, and How to Fix It.

The gap between task queues and durable execution, and what fills it in 2026 Most Python applications reach for Celery when they need background processing. It is a reasonable default. It is mature, well-documented, and the operational model is familiar to almost every backend engineer in the ecosystem. The problem surfaces later, usually in production, usually at an inconvenient time. You have a three-step workflow: charge a card, reserve inventory, send a confirmation. The worker crashes after