LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturing

Abstract:Factories are shifting toward smaller lot sizes with high product customization, requiring frequent re-programming of flexible and reconfigurable automation systems. LLM-based agents can be deployed in two complementary roles: Offline, they generate deterministic production sequences, reducing programming effort; online, they operate live machines and handle unforeseen runtime faults that static programs cannot anticipate. We propose a solution in which each factory module is paired with a dedicated LLM-based agent and an MCP tool server that exposes the module's skills via OPC UA method calls, with agents coordinating over MQTT and grounded by real-time updates of the factory state. We compare three agent architectures (orchestrator, peer-to-peer, and monolithic) across nine production challenges of increasing complexity in a simulation of a physical six-module hexagonal factory, including silent hardware fault detection. The monolithic and peer-to-peer architectures both achieve the highest mean solve rate (93\%), while the orchestrator uniquely resolves a silent conveyor-belt fault in all ten runs by autonomously rerouting plates around the blocked segment. All architectures exhibit emergent fault-diagnosis behavior without any explicit failure-handling logic, establishing standardized MCP tooling, MQTT-based inter-agent communication, and real-time state injection as a viable and reproducible foundation for LLM-programmed smart manufacturing.

Accepted at the 2026 IEEE 31st International Conference on Emerging Technologies and Factory Automation (ETFA). 8 pages, 5 figures, 3 tables

Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)

arXiv:2610.01364 [cs.MA]

(or arXiv:2610.01364v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2610.01364 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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