# The Role of Multi-Agent Systems in Realizing Asset Administration Shell Type 3
## 4 Agent-Based Reference Model for Realizing AAS Type 3
### Recommended Practices for Interfacing with Assets Supported by IEEE 2660.1 Standard  
The integration with the asset is a critical aspect of AAS solutions. While the AAS may describe how to interface with an asset through a suitable submodel (e.g., _Asset Interfaces Description_[15]), the actual connection between the AAS and the physical asset remains unspecified and lacks formal guidance. Consequently, this interface is often developed in a customized, case-by-case manner, depending on the specific characteristics and communication protocols of the asset. Furthermore, even when such connections are realized, data exchange is typically unidirectional since AAS Types 1 and 2 do not include the mechanisms required for direct, active, or bidirectional interaction with the physical asset. In this context, agents are particularly well-suited for interfacing with automation and control devices. Unlike passive data collection mechanisms, agents are intelligent components that not only gather data but also process them to make decisions and provide feedback to the asset. This enables bidirectional communication and real-time adaptability, which are essential for implementing the autonomy and intelligence envisioned in AAS Type 3 solutions. For example, consider an agent associated with a drilling machine. The agent can continuously gather real-time operational data to monitor the drilling process. If it detects an anomaly or suboptimal condition, it can autonomously respond, e.g., by adjusting a control parameter on the machine to correct the issue or by issuing a warning to a supervisory system or human operator. In order to achieve interoperability and facilitate this integration, it is fundamental to have an easy, transparent, and reusable way of interconnecting agents with different assets. In this context, the IEEE 2660.1 standard [27] presents generic interface practices based on interaction mode and location levels. These interface practices can be used in the context of the defined reference model, particularly to show the different possibilities and guide the selection of the best interface practice to integrate agents with assets, depending on the use case and application requirements. \begin{table}
\begin{tabular}{l l} \hline \hline
**RAMI4.0 Layer** & **Relation to Proposed Model** \\ \hline \hline \multirow{2}{*}{Asset} & \multicolumn{2}{l}{Represents the asset, i.e., every physical or logical object that has value for a company} \\ \hline \multirow{2}{*}{Integration (_intelligent part_)} & Industrial agents interface with the asset to collect data or adapt control processes (supported by the IEEE 2660.1 standard [27]) \\ \hline \multirow{2}{*}{Communication (_information and intelligent part_)} & Data exchange follows a client–server schema (supported by AAS Part 2 specification [14]). Additionally, agents enable decentralized communication through interaction strategies such as collaboration and negotiation (supported by FIPA specifications and VDI/VDE 2193 standards [42; 43]) \\ \hline \multirow{2}{*}{Information (_information part_)} & Data collected from the asset, including sensor readings and other relevant information, are stored in a standardized and structured manner using AAS submodels (supported by AAS Part 1 specification [10]) \\ \hline \multirow{2}{*}{Functional (_intelligent part_)} & Agents can autonomously execute adaptive functions (e.g., based on AI techniques) for tasks such as monitoring, diagnosis, optimization, and predictive analysis \\ \hline \multirow{2}{*}{Business (_intelligent part_)} & Agents facilitate self-optimizing, decentralized decision-making aligned with organizational business objectives to enhance operational efficiency \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relationship of the proposed model to the layers of RAMI4.0. As illustrated in Figure 6, the IEEE 2660.1 standard defines and compares four integration patterns based on two key factors: the interaction mode between the agent and the asset and the agent's deployment location.