###### Abstract  
Industry 4.0 (I4.0) aims at achieving the interconnectivity of multiple industrial assets from different hierarchical layers within a manufacturing environment. The Asset Administration Shell (AAS) is a pilot component of I4.0 for the digital representation of assets and can be applied in both physical and digital assets, such as enterprise software, artificial intelligence (AI) agents, and databases. Multi-agent systems (MASs), in particular, are useful in the decentralized optimization of complex problems and applicable in various planning or scheduling scenarios that require the system's ability to adapt to any given problem by using different optimization methods. In order to achieve this, a universal model for the agent's information, communication, and behaviors should be provided in a way that is interoperable with the rest of the I4.0 assets and agents. To address these challenges, this work proposes an AAS-based information model for the description of scheduling agents. It allows multiple AI methods for scheduling, such as heuristics, mathematical programming, and deep reinforcement learning, to be encapsulated within a single agent, making it adjustable to different production scenarios. The software implementation of the proposed architecture aims to provide granularity in the deployment of scheduling agents which utilize the underlying AAS metamodel. The agent was implemented using the SARL agent-oriented programming (AOP) language and deployed in an open-source MAS platform. The system evaluation in a real-life bicycle production scenario indicated the agent's ability to adapt and provide fast and accurate scheduling results. the I4.0 ecosystem, e.g., machines, production modules, products, software installations, or human resources. The primary goal of I4.0 is to provide interoperability within the system; however, it is due to the Asset Administration Shell (AAS) that this level of interoperability and abstraction can be achieved [2]. AAS is the digital representation of an asset within the I4.0 ecosystem and provides a detailed description of the asset's different domains, e.g., identification, communication, safety, security, etc. The AAS, in essence, is a metamodel, which consists of sub-models (SMs) and sub-model elements (such as collections, properties, events, etc. ), which are the basis for describing all the properties of an asset. On top of that, semantics are utilized within each entity to allow higher abstraction as well as accessibility. Organization management and control systems, which drive the efficiency of industrial processes and achieve automation, are built on top of seamless communication between physical and digital production assets. Agents and Multi-agent systems (MASs) are digital assets with advanced characteristics in autonomous decision making that are deployed within the environment. From the manufacturer's side, interoperability between assets is one of the primary goals, as it is trying to implement solutions that are free from dependencies and have modular characteristics. AAS has been proposed to address this challenge by encapsulating the asset in a digital form that can be accessed from other assets in a standardized way. In the case of software components, this is equally important in order to provide support interoperability with other software and hardware components and build reusable solutions. In the case of agents, it means that information structures, communication pipelines, and operational features will be described in a way that other assets can exploit. In practice, the AAS concept holds the intermediate layer between the asset and the external world. The reason is that in this way, the asset may not implement custom interaction and communication with other assets but rather use the AAS as the metamodel to describe the domains of the asset and allow third-party components to interact with the asset indirectly through the AAS services and information provided by the hosting platform.