This chapter proposes a systematic architecture for translation workflows enabled by Large Language Models (LLMs), elevating isolated model functions into controllable, scalable process systems. It differentiates agent‑level (functional execution) and platform‑level (project governance) workflows and analyzes long‑form requirements: multi‑level contextual scaffolding (document → paragraph → sentence → word), machine‑executable personalization rules, and efficiency/reuse via versioning, caching, near‑duplicate clustering, and quality estimation–post‑editing loops. A modular reference architecture is detailed (user interface, functional nodes, shared storage, workflow engine, and external integration), together with interaction logic, engine selection criteria (Airflow, Prefect, and BPMN engines), UI design for multi‑role collaboration, structured storage modeling of heterogeneous artifacts, and standardized integration of machine translation, LLMs, and auxiliary tools. Practical pipeline construction across increasing node granularities shows that well‑designed orchestration improves terminological consistency, structural coherence, and cost efficiency compared with monolithic direct LLM translation, while gaps remain for nuanced literary style. The chapter positions workflows as the foundation of an extensible ecosystem spanning multimodal translation, real‑time adaptation, and cross‑domain language services.