Agentic MT frameworks often adopt human-inspired roles, layered post-editing, and complex workflows. To stress-test these designs, we examine cost–performance trade-offs across five multi-agent MT workflows on specialized long documents. Using six language pairs from two financial test sets and LLMs spanning lower to higher capacity and cost tiers, we evaluate translation quality and terminology accuracy relative to token-level cost. We find: (i) lean pipelines match the quality of more complex systems at lower cost; (ii) terminology accuracy depends more on model capacity than on workflow complexity; (iii) document-length resilience is not universal, as some simple and some complex workflows maintain quality on longer documents; and (iv) post-editing agents offer a better return on model capacity than base translation. Edited-span analysis further suggests that many post-edits are stylistic or lexical alternatives rather than consistently preferred improvements.