Translation in the Age of AI Agents: الترجمة في عصر الذكاء الاصطناعي - Bousetouane, Fouad (Maalim, 2026)
Translation is entering a new technological phase. For decades, machine translation systems converted sentences from one language to another with increasing fluency, yet they remained constrained by context loss, domain ambiguity, cultural nuance, and limited mechanisms for verification. The emergence of large language models (LLMs) and AI agents changes the translation function from a passive text-conversion task into an active, tool-using, context-aware workflow. An AI translation agent can retrieve domain knowledge, consult glossaries, check terminology, preserve institutional tone, compare alternative translations, evaluate uncertainty, and request human review when risk is high. This paper argues that agentic translation represents a paradigm shift in diplomatic, scientific, technical, legal, healthcare, and enterprise communication. The shift is especially important because the web is linguistically imbalanced: English dominates online content, while French and Arabic occupy much smaller shares, shaping the available knowledge base for AI systems. This imbalance creates both an opportunity and a risk. Agentic translation can bridge knowledge gaps by grounding translation in trusted corpora and domain-specific references, but it must be evaluated through human-in-the-loop processes and automated adversarial evaluation frameworks such as ProofAgent Harness. The paper proposes a reference architecture for agentic translation, identifies key domain use cases, and outlines evaluation dimensions for reliable deployment.