# The Role of Multi-Agent Systems in Realizing Asset Administration Shell Type 3
## 3 AAS Type 3 Approaches Based on Multi-Agent Systems
Through secure and standardized communication protocols, agents representing assets from different organizations can negotiate, allocate resources, and coordinate production tasks seamlessly. Another important aspect concerns the interface with physical assets for data collection or control adaptation. While the AAS provides I4.0-compliant interfaces to enable seamless data exchange at the information level, it is not inherently designed to interface directly with physical assets. Instead, this connection requires customized implementation tailored to the specific characteristics, communication protocols, and constraints of each asset. In this sense, agents (or well-known industrial agents) are suitable for this purpose, as they are primarily designed to interact with both the physical and digital layers. They can serve as intelligent intermediaries that interpret and process submodel data and translate high  
Figure 3: Conceptual view of a negotiation-based production scenario using agent-based AAS. level commands into asset-specific control actions, thus enabling flexible and autonomous monitoring and control of physical assets. Although existing agent-based AAS approaches vary from simple rule-based logic to more advanced AI-based behaviors, most current implementations tend to rely primarily on the former, with limited exploration of sophisticated AI techniques. However, the broader agent literature highlights the potential for agents to act as \"model consumers\" of AI techniques, transitioning toward the so-called AI agents. This includes the integration of advanced learning algorithms, reasoning capabilities, and adaptive behaviors, which could significantly enhance the intelligence and autonomy of AAS-based solutions. Additionally, some research efforts also explore the use of agents for service orchestration and the adoption of recursive architecture inspired by holonic systems (see Figure 4). In this context, each agent-based AAS encapsulates the capabilities of its associated assets and exposes them as services, enabling the dynamic composition and orchestration of services to realize higher-level services aligned with production goals. For example, consider an illustrative example of a work cell comprising a robot, a gripper, and a conveyor. Individually, their respective agent-based AAS expose atomic services such as movement, hold, and transport and are responsible for managing their own services. In this context, the agent-based AAS associated with the work cell contains an orchestration engine to coordinate the execution of these services, relying on effective communication and negotiation between agents to ensure proper sequencing and synchronization. Through this engine, the work cell agent can compose atomic services into a higher-level service, such as pick and place. Extending this concept further, a station agent can orchestrate multiple work cell agents, which in turn coordinate their assets, enabling the composition of complex production tasks to be executed efficiently and flexibly. Finally, the agent-based AAS approach is not limited to typical physical assets such as machines and production units but is also being applied to represent human operators. In this context, agent-based AAS can be specifically designed to encapsulate operator-related information, such as skills, preferences, fatigue levels, and availability. By leveraging these data, agents can dynamically allocate tasks in a manner that aligns with the operator's current capabilities and conditions, thereby enhancing task performance, ensuring quality of work, and supporting overall human well-being. Figure 4: Service orchestration of holonic agent-based AAS. In summary, the integration of MAS with AASs represents a promising pathway toward the realization of AAS Type 3, enabling autonomous, intelligent, and collaborative behaviors in industrial environments.