I like that this focuses on orchestration instead of model comparisons. In production, the model is often the simplest part the hard problems are handling long-running jobs, retries, idempotency, state transitions, and recovery when one stage fails.

I'd also add observability to the checklist. Once these workflows span uploads, multiple AI services, webhooks, and background workers, having end-to-end tracing and task-level logs becomes invaluable for debugging failures and understanding where latency is introduced. That's usually what separates a reliable production pipeline from a demo.