Agentic active Asset Administration Shell for circular manufacturing

The transition to Circular Manufacturing (CM) demands interoperable coordination of heterogeneous Product, Process, and Resource (PPR) data across organizational boundaries and shopfloor systems. Existing semantic technologies for interoperable knowledge representation, such as ontologies and the Asset Administration Shell (AAS), remain passive data carriers decoupled from data integration and operation, while shopfloor orchestration approaches lack grounding in interoperable data standards, leaving a critical gap between structured lifecycle knowledge and closed-loop manufacturing decisions. To resolve this isolation, this paper proposes the Agentic Active AAS (A 4 S), a neural-symbolic architecture that unifies representation, integration, and orchestration within a single framework. A 4 S makes three core contributions. First, A 4 S extends the AAS standard (IEC 63278) from a passive interface to a proactive interoperability layer by leveraging Large Language Models (LLM), which implement the well-known concept of “Active AAS” or “Type3 AAS”. A graph-like recursive encoding of AAS instances is proposed to preserve the strict parent-child topology of AAS shells, submodels, and submodel elements as a symbolic graph traversable by LLM retrieval. Building on this representation, a suite of AAS tools is formalized as the LLM-agent’s bounded action space, enabling the agent to dynamically construct, query, and manipulate AAS instances. Second, A 4 S introduces Agentic Operation Delegation, which abstracts heterogeneous shopfloor operation interfaces into unified AAS operation schemas and mathematically bounds the LLM’s token generation through schema-constrained decoding. Third, building upon the Active AAS layer, an LLM-based Multi-Agent System (LMAS) is developed, in which the Orchestrator Agent and specialized sub-agents interact through the unified AAS substrate. This simultaneously enables automatic creation of Digital Product Passports (DPP) for cross-organizational value chain exchange and intra-enterprise PPR integration for CM operations. The proposed approach is evaluated in a battery remanufacturing scenario with a dynamic system environment. Experimental results show that A 4 S achieves over 95% success rate in complex multi-step shopfloor orchestration tasks, consistently outperforming state-of-the-art LMAS approaches across all model scales. Notably, open-source models (e.g., Qwen3.6–35B) under A 4 S surpass large proprietary models such as GPT-5.2 in orchestration tasks, demonstrating the effectiveness of the proposed neural-symbolic constraints. For DPP generation, A 4 S-GPT5.2 achieves 90% success rate, where tool execution correctness reaches 100%. These results highlight that A 4 S effectively enables more robust orchestration in the constrained CM environment. The code is released at https://github.com/quickhdsdc/aas-mcp-server.git .