Language is a cornerstone of cultural identity, yet globalization and the dominance of major languages have placed nearly 3,000 languages at risk of extinction. Existing AI-driven translation models prioritize efficiency but often fail to capture cultural nuances, idiomatic expressions, and historical significance, leading to translations that marginalize linguistic diversity. To address these challenges, we propose a multi-agent AI framework designed for culturally adaptive translation in underserved language communities. Our approach leverages specialized agents for translation, interpretation, content synthesis, and bias evaluation, ensuring that linguistic accuracy and cultural relevance are preserved. Using CrewAI and LangChain, our system enhances contextual fidelity while mitigating biases through external validation. Comparative analysis shows that our framework outperforms GPT-4o, producing contextually rich and culturally embedded translations, a critical advancement for Indigenous, regional, and low-resource languages. This research underscores the potential of multi-agent AI in fostering equitable, sustainable, and culturally sensitive NLP technologies, aligning with the AI Governance, Cultural NLP, and Sustainable NLP pillars of Language Models for Underserved Communities. Our full experimental codebase is publicly available at: https://github.com/ciol-researchlab/Context-Aware_Translation_MAS

翻译：语言是文化身份的基石，然而全球化和主要语言的支配地位已使近3000种语言面临消亡风险。现有的人工智能驱动翻译模型虽注重效率，却往往难以捕捉文化细微差别、习语表达和历史意义，导致翻译结果边缘化语言多样性。为应对这些挑战，我们提出一种专为服务不足语言社区设计的文化自适应翻译多智能体人工智能框架。该方法利用专门负责翻译、解释、内容合成和偏见评估的智能体，确保语言准确性和文化相关性的保留。通过采用CrewAI和LangChain，我们的系统在借助外部验证缓解偏见的同时，增强了语境保真度。对比分析表明，该框架在生成富含语境且深植文化的翻译方面优于GPT-4o，这对原住民语言、区域语言及低资源语言而言是至关重要的进步。本研究彰显了多智能体人工智能在促进公平、可持续且文化敏感的NLP技术方面的潜力，符合"服务不足社区语言模型"的AI治理、文化NLP与可持续NLP三大支柱。完整实验代码库已公开于：https://github.com/ciol-researchlab/Context-Aware_Translation_MAS