We present AIDA Agents, a multi-agent translation platform that orchestrates LLM-based agents—for translation, rating, post-editing, and re-rating—delivering context-aware translations without model fine-tuning. Optional retrieval-augmented generation (RAG) injects translation memories, terminology, and style guidelines at every pipeline stage. On WMT24++ (Deutsch et al., 2025) (11 languages), AIDA Agents outperforms all systems on 10 of 11 pairs. On an industrial benchmark, 70–98% of segments are publication-ready without human post-editing. The platform is deployed with native XLIFF integration.

Accurate terminology is a non-negotiable requirement in industrial localization processes: a single mistranslated domain term can violate contractual obligations and erode client trust.We present AIDA_term, a deployed multi-agent LLM pipeline that orchestrates four specialized agents—Analysis, Translation, Post-editing, and Review—for terminology-constrained machine translation.The system introduces terminology-aware pre-analysis, explicit glossary injection at every pipeline stage, and a reasoning-enabled Review agent.We evaluate six configurations on the WMT25 Terminology Translation benchmark (Track 1: en→de/es/ru, IT domain), enabling systematic ablation of each design choice.Our best configuration achieves 99.4% average terminology accuracy while attaining the highest ChrF2++ scores across all three language pairs, outperforming all 20 systems submitted to the shared task.Unlike other multi-agent approaches in WMT25 that rely on generate-and-select strategies, AIDA_term is the first to apply a role-specialized sequential pipeline to terminology-constrained MT, and is deployed with native XLIFF integration for seamless CAT tool interoperability.The system processes thousands of terminology-constrained requests daily at a large localization provider.