Agentic AI Translate: An Agentic Translator Prototype for Translation as Communication Design

2026-05-16

AI Summary

This study addresses the limitations of traditional machine translation, which treats translation merely as a text-to-text conversion task and neglects its fundamental nature as a communicative design process, thereby failing to systematically model purpose, register, audience, and genre. To overcome this, the authors propose an agent-based translation architecture that reframes translation as a goal-oriented design activity through an interactive specification phase that constructs a structured brief, followed by an iterative “identify → prompt → generate → validate” cycle. The work innovatively translates metalinguistic concepts from translation studies into a generative AI instruction framework, explicitly integrating skopos theory, register theory, and genre conventions. It further incorporates a conversational brief, GEMBA-MQM error annotation, DelTA-lite proper noun memory, and bilingual summarization to ensure document-level coherence. Although not empirically evaluated, this approach offers a novel paradigm and actionable blueprint for translation as communicative design in the generative AI era.

Abstract

We present Agentic AI Translate, an agentic translator prototype that operationalises the thesis of Yamada (forthcoming) -- that the metalanguage of Translation Studies has become an instruction code for generative AI. The system replaces the dominant text-in / text-out paradigm of machine translation with a four-stage agentic cycle (Identify -> Prompt -> Generate -> Verify), preceded by an interactive specification phase in which the user composes -- through model-assisted dialogue -- a structured translation brief grounded in skopos theory, register, audience, and genre conventions. The verification stage adopts the GEMBA-MQM error-span protocol (Kocmi & Federmann, 2023) for evidence-grounded scoring, and document-level coherence is preserved through a DelTA-lite memory of proper nouns and a running bilingual summary, after Wang et al. (2025). We describe the philosophical motivation, the architectural commitments, the four reference-material categories the system consumes, and the principal design tensions the architecture makes explicit. Empirical validation is left for future work; the contribution here is conceptual and architectural -- an executable embodiment of the position that translation in the GenAI era is communication design, not text conversion.

Problem

• machine translation
• communication design
• translation brief
• skopos theory
• generative AI

Innovation

• agentic translation
• communication design
• structured translation brief
• GEMBA-MQM
• DelTA-lite memory