To achieve a highly agile and flexible production, it is envisioned that industrial production systems gradually become more decentralized, interconnected, and intelligent. Within this vision, production assets collaborate with each other, exhibiting a high degree of autonomy. Furthermore, knowledge about individual production assets is readily available throughout their entire life-cycles. To realize this vision, adequate use of information technology is required. Two commonly applied software paradigms in this context are Software Agents (referred to as Agents) and Digital Twins (DTs). This work presents a systematic comparison of Agents and DTs in industrial applications. The goal of the study is to determine the differences, similarities, and potential synergies between the two paradigms. The comparison is based on the purposes for which Agents and DTs are applied, the properties and capabilities exhibited by these software paradigms, and how they can be allocated within the Reference Architecture Model Industry 4.0. The comparison reveals that Agents are commonly employed in the collaborative planning and execution of production processes, while DTs typically play a more passive role in monitoring production resources and processing information. Although these observations imply characteristic sets of capabilities and properties for both Agents and DTs, a clear and definitive distinction between the two paradigms cannot be made. Instead, the analysis indicates that production assets utilizing a combination of Agents and DTs would demonstrate high degrees of intelligence, autonomy, sociability, and fidelity. To achieve this, further standardization is required, particularly in the field of DTs.

翻译：为实现高度敏捷灵活的生产，工业系统逐步向去中心化、互联化与智能化方向演进。在此愿景下，生产资产之间相互协同，展现出高度自主性；同时，各生产资产全生命周期的知识可实时获取。该愿景的实现需要充分运用信息技术，其中软件代理与数字孪生是两种常见的软件范式。本研究系统比较了软件代理与数字孪生在工业应用中的差异、相似性及潜在协同效应。比较维度包括：两类范式的应用目的、所展现的属性与能力，以及在工业4.0参考架构模型中的定位。结果表明：软件代理通常用于生产过程的协同规划与执行，而数字孪生更倾向于在监测生产资源与处理信息时扮演被动角色。尽管两类范式在能力与属性上呈现特征化差异，但两者之间无法进行明确的分界。分析表明，融合软件代理与数字孪生的生产资产将展现高度的智能性、自主性、社交性与保真度。为实现这一目标，需进一步推动标准化工作，尤其是在数字孪生领域。