# Comparison of Multi-Agent Platform Usability for Industrial-Grade Applications
## 1 Introduction  
Software systems are becoming increasingly complex, rendering centralised approaches less feasible. This complexity arises from technological advancements and the dynamic nature of modern system environments. As a result, individual components, often limited in computational capabilities, are unable to efficiently manage entire processing tasks alone (e.g., in edge computing scenarios). Additionally, the competitive software market imposes stringent Quality of Service (QoS) requirements, particularly regarding fault tolerance, security, and responsiveness. These demands are frequently formalised in legally binding Service Level Agreements (SLAs), which can result in significant penalties whenever being violated. Given the widespread adoption of service provision over the Internet, such as in Cloud computing and, more recently, Cloud-Edge-Continuum (CEC) ecosystems, there is a critical need for applications that remain highly resilient at a large scale. Furthermore, data protection regulations (e.g., GDPR [1]) and/or restrictions on data sharing often preclude the centralisation of all necessary data storage and processing. These factors underscore the need for decentralised system operation approaches [2]. Within this domain, Distributed Artificial Intelligence (DAI) has gained particular attention [3], addressing challenges related to collaborative decision-making and collective learning. The increasing complexity of modern systems necessitates automation across multiple levels of autonomy, which can significantly reduce the need for costly and error-prone manual operations. One subdomain of DAI is _agent systems_, first introduced at the Workshop on Distributed Artificial Intelligence at MIT in June 1980 [4]. Although a single definition of an agent has not been universally accepted, several common characteristics have emerged [5], including autonomy, reactivity, social capabilities, proactiveness, goal orientation, and responsiveness. These attributes remain highly relevant in contemporary systems. Over the past four decades, significant contributions have been made toward conceptualising agent systems from both theoretical and practical perspectives [6]. However, despite the alignment of modern system requirements with what Multi-Agent Systems (MASs) promised to deliver, their adoption in the production environments remains limited. Developers and software architects frequently opt for alternative solutions, such as event-driven architectures, service-oriented architectures, microservices, or serverless computing. Although developers may unknowingly implement approaches similar to agent paradigms, core agent-specific terminology and tools are rarely employed. The contrast between agent systems and industrial applications has been recognised, as evidenced by the multistage AgentLink project [7; 8; 9], funded by the European Commission under FP6. During the project's third stage (AgentLink III), a strategic roadmap was developed to analyse the future maturity prospects of MAS [10]. At the time, it was estimated that agent systems would require at least 20 years to achieve industrial maturity, based on the historical evolution of other programming approaches like Object-Oriented Programming (OOP) [11]. It was assumed that progress in MAS would mirror the general trajectory of Software Engineering (SE) [12], where it typically takes 10-20 years for research to transition into widespread practice. Nevertheless, these predictions have not materialised, as there has been little change in the industrial acceptance of MAS since 2005. This raises important questions about the factors preventing MAS from being widely adopted. A critical argument in this context is the _divergence_ between the development trajectories of agent systems and industrial-grade applications. Real-world industrial applications must deliver tangible business value and adhere to stringent robustness requirements.