Are AI agents the new machine translation frontier? Challenges and opportunities of single- and multi-agent systems for multilingual digital communication (2025)

Abstract: The rapid evolution of artificial intelligence (AI) has introduced AI agents as a disruptive paradigm across various industries, yet their application in machine translation (MT) remains underexplored. This paper describes and analyses the potential of single- and multi-agent systems for MT, reflecting on how they could enhance multilingual digital communication. While single-agent systems are well-suited for simpler translation tasks, multi-agent systems, which involve multiple specialized AI agents collaborating in a structured manner, may offer a promising solution for complex scenarios requiring high accuracy, domain-specific knowledge, and contextual awareness. To demonstrate the feasibility of multi-agent workflows in MT, we are conducting a pilot study in legal MT. The study employs a multi-agent system involving four specialized AI agents for (i) translation, (ii) adequacy review, (iii) fluency review, and (iv) final editing. Our findings suggest that multi-agent systems may have the potential to significantly improve domain-adaptability and contextual awareness, with comparable translation quality to traditional MT or single-agent systems. This paper also sets the stage for future research into multi-agent applications in MT, integration into professional translation workflows, and shares a demo of the system analyzed in the paper.

## introduction in an increasingly interconnected world, the demand for accurate, efficient, and context-aware multilingual communication has surged, driven by globalization and digital transformation @xcite . mt systems face persistent challenges in handling domain-specific jargon, adapting to contextual particularities, and aligning with clientspecific guidelines @xcite . traditional neural machine translation (nmt) models, though advanced, often operate as monolithic systems, lacking the flexibility to dynamically integrate specialized knowledge or iterative quality controls without fine-tuning, a critical problem in high-stakes domains such as legal, medical, or technical translation @xcite . the emergence of ai agents-autonomous or semi-autonomous systems capable of reasoning about tasks, tool integration, and taking actions to achieve specific goals-may present a paradigm shift for mt. increasingly adopted in fields like software engineering @xcite , customer support @xcite , data analysis @xcite , and academic research @xcite , ai agents remain underexplored in translation workflows. in the context of mt, ai agents can be organized into single-agent systems for straightforward tasks or multi-agent systems for complex workflows requiring collaboration and iterative refinement (see figure 1 ). by leveraging highly customisable workflows, external tools (e.g., domainspecific glossaries, translation memories), memory, and advanced planning capabilities, multi-agent systems may be able to address the limitations of traditional mt systems. for instance, by decomposing translation tasks into specialized roles (e.g., translation, adequacy review, fluency editing) and enabling dynamic interaction between ai agents, multi-agent systems may mirror professional human workflows. the primary goal of this paper is to explore the capabilities of ai agent workflows for mt, with a focus on their organization, customization, and generalisability across fields requiring multilingual digital communication. this paper investigates the following research questions (rqs): rq1. how effective are multi-agent systems in legal mt compared to single-agent approaches? rq2. how do ai agent-based workflows align with professional human translation processes? rq3. how does model temperature impact translation performance in multi-agent systems? rq4. how does model size impact translation performance in multi-agent systems? this work makes several key contributions to the mt field. sections 2 and 3 provide a theoretical framework for organizing ai agents into singleagent and multi-agent systems, highlighting the use of customizable workflows and external tools. section 4 analyses the practical application of this framework through a pilot study, illustrating the potential of ai agents in translation workflows. the pilot study consists of four specialized agents for legal translation: (i) a translator-agent, (ii) an adequacy reviewer-agent, (iii) a fluency reviewer-agent, and (iv) an editor-agent. this structure simulates real-world translation processes in legal settings, where consistency, terminology accuracy, and compliance are paramount. while the pilot study provides a practical example of ai agents in action, the broader focus of this paper is on the theoretical and methodological implications of ai agent workflows for mt, emphasizing their potential to transform multilingual communication across diverse fields, offering a foundation for future research and implementation. we also share a multi-agent public demo for further analysis and replication: https://agentsparallel-2.streamlit.app/ . the concept of ai agents traces its roots to early ai research, where "rational agents" were defined as entities capable of autonomous action in pursuit of objectives @xcite . however, until very recently, most agent systems relied on rigid algorithmic structures @xcite . the emergence of large language models (llms) has marked a significant turning point in ai agent systems, with enhanced reasoning and contextual understanding capabilities, allowing for more flexible and adaptable workflows @xcite . this has transformed ai agents from theoretical constructs into practical tools @xcite . we could now define ai agents as autonomous or semi-autonomous software programs designed to reason about tasks and execute actions to achieve predefined goals. unlike traditional mt systems, which operate as static pipelines where an input in the source language is received by the system and an output in the target language is generated, agents can dynamically adapt their behaviour through a defined set of instructions, which allow them to plan, integrate tools, and iteratively refine their output @xcite . all these advancements have facilitated the emergence of structured ai agent workflows, which can broadly be categorized into single-agent workflows and multi-agent workflows. single-agent workflows involve only one ai agent that performs tasks within a given environment. these agents function independently, performing sequential tasks such as summarizing, translating, or processing data. they often rely on predefined prompts and reinforcement mechanisms to enhance performance @xcite . for example, in software engineering, single-agent systems have been successfully applied to automated debugging and code generation @xcite . in mt, a single-agent workflow could be instructing a traditional nmt system to translate something from an api call and/or using an llm with a simple prompt for mt. substantial research on the topic has already been conducted @xcite @xcite . multi-agent workflows consist of different ai agents collaborating to achieve a shared objective. these workflows enable specialization, with each ai agent performing a designated role within a sequential or iterative system @xcite . multiagent workflows have seen widespread adoption in domains such as software engineering (e.g., github copilot for code generation) @xcite , customer service (e.g., chatbots for query resolution) @xcite , data analysis (e.g., automated report generation) @xcite or academic research @xcite . their success in these fields stems from their ability to decompose tasks into subtasks, collaborate with external tools (web search, specific databases, etc.), and optimize outcomes through feedback loops, memory and/or reasoning. multi-agent systems have become a focal point of ai research due to their ability to tackle complex problems requiring distributed decisionmaking and contextual adaptation @xcite . several studies highlight the advantages of multi-agent collaboration, particularly in tasks requiring high levels of reasoning and iterative improvement @xcite . for instance, research on ai planning and task execution has demonstrated that multi-agent workflows lead to improved adequacy and efficiency compared to single-agent approaches @xcite . however, the application of ai agents in mt remains scarce, despite the alignment between agentbased workflows and the iterative, role-driven nature of professional translation processes. while traditional mt research prioritized model architecture improvements @xcite , the integration of ai agent workflows-inspired by frameworks like react @xcite and multistep planning-represents a shift toward mimicking human translation teams' collaborative dynamics. to date, only a few experiments on ai agents for mt have been published. for instance, @xcite introduced transagents, a multi-agent system designed to translate ultra-long literary texts. this system mimicked human editorial workflows by incorporating specialized agents for different translation tasks, including initial translation, localization, proofreading, and quality assessment. the authors report that despite achieving lower d-bleu scores, transagents-generated translations were preferred by human evaluators over conventional mt systems and even human references due to improved cultural and contextual adaptation. it is worth stressing, however, that the mt evaluation was not conducted by professional evaluators and could therefore have had an impact on the results @xcite . @xcite introduced another multi-agent workflow for mt, using three different ai agents: the first agent translates a text, the second agent provides improvement suggestions, a third agent produces a final translation after considering the suggestions. the author reports using bleu on standard translation datasets and suggests that this workflow has shown mixed results-sometimes competitive with, and occasionally falling short of, leading commercial translation systems-but no specific details nor human evaluation have been found. more recently, @xcite proposed a multi-agent system for translating hong kong legal judgments, comprising translator, annotator, and proofreader agents powered by gpt-3.5 turbo, and a memory-based few-shot prompting strategy was used for iterative quality improvement. the evaluation shows that the multi-agent system outperformed both traditional mt systems and even gpt-4o in accuracy, coherence, and style, offering a scalable solution for bilingual legal translation. this demonstrates that multi-agent systems for mt are a nascent area of research with great potential for further enhancement that lacks further empirical analysis. ## the potential of ai agents for mt from our perspective, the efficacy of ai agents in mt depends on four core attributes: autonomy: ai agents operate independently or with minimal human oversight once configured, provided they receive clear instructions (e.g., roles and tasks to conduct, style preferences, domain constraints). for instance, a translator-agent in a legal translation workflow could be instructed to provide translations while adhering to jurisdictional terminology from a specific country. tool use: agents can integrate external resources such as translation memory systems, domain-specific databases (e.g., legal glossaries from the client), and retrieval-augmented generation (rag) frameworks to enhance accuracy and consistency @xcite . early works have demonstrated the promising results of rag for mt @xcite . for example, the above translator-agent could cross-reference terminology from previously translated materials from a specific client to ensure compliance or have access to iate, if working with legal documents. memory: agents can learn from feedback loops, refining outputs iteratively @xcite . for example, a fluency reviewer-agent might prioritize syntax and style corrections based on recurring errors flagged in prior iterations. workflow customization: ai agents enable dynamic mt workflows through customizable architectures. figure 2 depicts five potential multi-agent workflows (not exclusive) that we define considering their application to mt challenges such as domain adaptation, scalability, and quality assurance 1 . workflows can be sequential or iterative. a sequential ai agent workflow is a structured process where tasks are executed in a strict order, with each step depending on the completion of the previous one. an iterative ai agent workflow is more dynamic and allows multiple tasks to be performed simultaneously, with results being refined through back-and-forth adjustments. ## the pilot study the above workflows remain, at this stage, theoretical constructs designed to explore the potential of multi-agent systems in mt. while they provide a structured reflection on how ai agents could be leveraged to improve translation workflows, their practical feasibility, efficiency, and effectiveness in real-world applications have yet to be empirically validated. to bridge this gap, we are conducting a pilot study to assess the viability of ai agent-based approaches in professional translation settings. ## discussion of the results this section presents the results of the comparative evaluation of the multi-agent and the nmt systems. first, the fluency and adequacy scores are discussed, followed by the overall ranking distribution. figure 4 both nmt systems analysed, deepl and google translate, obtained lower scores than both multi-agent big configurations, but higher scores than the multi-agent small systems. the two worst performing systems-multi-agent small 1.3 and multi-agent small 1.3/0.5-consistently scored lower across both fluency and adequacy metrics. multi-agent small 1.3 had a fluency score of 3.31 and an adequacy score of 3.47, slightly outperforming multi-agent small 1.3/0.5, which scored 3.23 in fluency and 3.44 in adequacy. these scores positioned the multi-agent workflows powered by smaller llms at the lower end of the performance spectrum. to complement the fluency and adequacy metrics, a ranking-based evaluation was conducted, where the evaluator assigned ordinal rankings (1st to 6th place) to each system's translations. since there were some ties in every segment, ranking scores only range from 1 to 4. figure 5 reveals that multi-agent big 1.3 secured the highest proportion of first-place rankings (64 out of 100), followed closely by the multi-agent big 1.3/0.5 system (57 out of 100). deepl, despite its higher average score overall within the nmt systems, received fewer first-place rankings (50) than google translate (56), indicating that while it produced adequate outputs, it may have struggled with certain domain-specific fluency constraints. by conducting a more qualitative analysis, we can see that the english text "usd 1,000,000" was incorrectly translated into spanish by the nmt systems as "$1.000.000" (deepl) and "usd 1,000,000" (google translate). in spanish, the dollar sign should go after the number, and dots should be the thousands separator. all the multi-agent systems (both in big and small sizes) correctly translated this currency as "1.000.000 usd". similarly, multi-agent systems demonstrated higher contextual coherence than nmt systems. the term "agreement" was coherently translated by all the multi-agent systems as "acuerdo" or "convenio", while the nmt systems offered different translations for the same source concept within the same translation. the two best performing systems were scored only with the ranking scores 1, 2 and 3, while both nmt systems and the multi-agent small configurations had a modest presence in the ranking scores 3 and 4. the multi-agent small 1.3 and multi-agent small 1.3/0.5 systems were rated with score 1 in only 39 and 37 instances, respectively, further reinforcing the observation that model size significantly impacts translation performance in a multi-agent setup. given the modest evaluation size and language pairs used in this study, definitive conclusions cannot yet be drawn. however, the findings provide valuable insights into the potential of multi-agent systems for mt and suggest several promising avenues for future research. the results indicate that multi-agent workflows may obtain higher transla-tion quality than nmt systems. both multi-agent big configurations outperformed traditional nmt models in adequacy and fluency. this suggests that integrating multiple specialized agents into mt workflows may allow for greater domain adaptation and content preservation, particularly in highstakes fields such as legal and medical translation. despite these promising results, the current multi-agent system was implemented with no external tools. the inclusion of memory, rag, domainand client-specific databases, and more granular agent role customization could further improve performance @xcite . future studies should explore how additional tooling and fine-tuned role assignments influence translation quality in multiagent systems. the study also suggests that the temperature setting plays a significant role in mt outcomes. higher temperatures for reviewer-agents correlated with stronger adequacy and fluency scores. a systematic investigation into the optimal balance between creative (higher temperature) and deterministic (lower temperature) agent behaviours could provide deeper insights into best practices for multi-agent mt workflows. the results also demonstrate that larger models tend to perform better in multi-agent settings. the multi-agent big configurations consistently outperformed the smaller multi-agent small systems, indicating that computational capacity is a critical factor in achieving high translation quality. future work should examine the trade-offs between computational efficiency and translation quality, particularly for organizations with limited resources. ## conclusion this paper has provided one of the early analysis of multi-agent systems in mt, comparing their performance with traditional nmt. first, we provided a thorough overview of the potential of ai agents for mt, both from a theoretical perspective-by exploring different workflows, potential use cases, and system architectures-as well as from a practical perspective-through a modest pilot study and a public demo designed for replication and further analysis. the paper opens an entirely new area of research focused on identifying optimal multi-agent configurations for mt and enhancing multilingual digital communication. our findings highlight that multiagent workflows obtain higher translation quality than traditional nmt systems and/or single-agent systems in our specific use case. research on multiagent systems is still in the early stages, and substantial empirical research is needed. so far, our pilot study highlights the impact of model size and temperature tuning on translation performance. besides these key findings, several areas for future research emerge: tool integration: what is the impact of integrating external resources such as rag, translation memories, specialized glossaries, and legal/medical databases to different multi-agent workflows? also, what agent should acquire this knowledge? it is worth stressing that our multiagent system is a basic workflow that obtains great results without tool access. adding tool access is a simple task that may improve translation performance even further, if compared with nmt, which would need to be fine-tuned to acquire this specific knowledge. scaling multi-agent systems: the scalability of multi-agent workflows for larger datasets and broader language pairs needs to be addressed. what is the performance of llm-powered multiagent systems in minor languages? evaluation methodologies: developing more rigorous human and automated evaluation frameworks tailored to multi-agent mt workflows is re-quired, as the potential workflows are limitless. how can we ensure a reliable evaluation of multiagent systems? cost and resource optimization: exploring the trade-offs between performance and sustainability, including token usage, computational costs, and energy efficiency in large-scale translation operations, is a crucial next step. resource optimization, particularly token management, is a critical factor. the cost of computing power and tokens includes all inputs fed to the translator, reviewer, and editor agents. while language model costs are decreasing, sustainability remains a pressing issue. one promising avenue is to explore hybrid workflows where low-cost models handle simple tasks while high-performance models are reserved for complex texts, ensuring both cost-effectiveness and sustainability. human-ai collaboration: examining how mt users interact with ai agents in translation workflows is also of relevance, not only in professional translation, but also for mt users beyond the language services industry, as most mt users are not professional translators. how can multi-agent systems be used for bridging language barriers and enhance multilingual digital communication in a human-centered way? @xcite this said, ai agents may represent a new frontier in mt, offering dynamic solutions to the rigidity of traditional mt systems. by integrating autonomy, context-awareness, and iterative refinement, multiagent systems may be able to enhance translation quality, scalability, and adaptability across domains. yet, this is still to be empirically tested. beyond technical advancements, ai agents unlock opportunities for societal equity, from bridging language divides in education and crisis response to preserving endangered linguistic heritage. however, their deployment is not without challenges. technical hurdles like latency and model dependency, ethical concerns around bias and accountability, and economic barriers such as high development costs demand urgent attention. the path forward requires interdisciplinary collaboration, ethical stewardship, and sustainable innovation. researchers must prioritize robust evaluation frameworks and low-resource language support, while industry stakeholders should invest in human-centered designs and green ai infrastructure. translators, as critical partners, will also need upskilling to navigate hybrid workflows that blend human expertise with ai efficiency.