# The Role of Multi-Agent Systems in Realizing Asset Administration Shell Type 3
## 4 Agent-Based Reference Model for Realizing AAS Type 3
### Alignment with RAMI4.0  
While RAMI4.0 provides a valuable framework for developing I4.0-compliant solutions, its high level of abstraction and generality may pose challenges when it comes to implementing autonomous and intelligent behaviors within AAS instances. To address these challenges, the reference model proposed in this work incorporates principles from MAS, enabling a structured approach to designing AAS Type 3. By introducing a separation between the information and intelligent parts, the model provides clearer guidance for integrating autonomy and proactive behavior in a way that remains compatible and aligned with the RAMI4.0.  
As previously mentioned, the AAS reflects the asset in the digital world and is conceptually aligned with the upper five layers of RAMI4.0, namely _integration_, _communication_, _information_, _functional_, and _business_. However, the main AAS specifications (Part \(1\)[10] and Part 2[14]) primarily address the _communication_ and _information_ layers. While these existing specifications provide a solid foundation for organizing and exchanging asset information, the _integration_, _functional_, and _business_ layers are not explicitly defined.  
The _integration_, _functional_, and _business_ layers of RAMI4.0 can theoretically be implemented using various approaches/technologies. However, it is crucial to select one that also offers the intelligence, autonomy, and collaboration capabilities envisioned for AAS Type 3. In this context, agents are particularly well-suited to address these requirements due to their unique characteristics, as previously discussed. Agents bring the essential capabilities of autonomy, decision-making, and collaboration, which are critical for enabling AAS to act as a self-managing, intelligent component.  
In this context, the _integration_ layer can be enhanced through industrial agents, which are typically designed to interface with assets to collect data or to adapt control. The _functional_ layer can be improved by incorporating agents that utilize AI techniques for tasks such as monitoring, diagnosis, optimization, and predictive analysis. Additionally, the _business_ layer can benefit from agents' ability to make autonomous decisions, optimizing processes like resource allocation, predictive maintenance scheduling, and fault management in alignment with business strategies. Additionally, agents can enhance the _communication_ layer by facilitating autonomous, decentralized communication through strategies like collaboration and negotiation. In summary, the map of the proposed model to the RAMI4.0 layers is described in Table 4.  
In this context, the benefits of the proposed model are reflected across multiple layers of RAMI4.0, particularly _integration_, _communication_, _functional_, and _business_. It enables the seamless integration of assets into I4.0, supports decentralized communication through strategies such as collaboration and negotiation, empowers assets to perform autonomous functions based on AI techniques, and improves decision-making processes aligned with business objectives.  
Since the information part is already well supported by formal specifications and standards provided by the IDTA (e.g., through the metamodel[10] and standardized API interfaces[14], which agents can access via standardized protocols), the following sections discuss how the agent (intelligent part) can be designed in compliance with existing standards and best practices to support broader industrial adoption and enable AASType 3 capabilities. In this sense, the following sections address key features highlighted in Figure 5 (blue box) and Table 4, including interfacing with the physical asset for data collection and control adaptation, communication between AAS Type 3 instances, and strategies for developing AI-based behaviors.