[
    {
        "id": "osp-19846",
        "type": "article-journal",
        "title": "LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturing",
        "author": [
            {
                "family": "Köhle",
                "given": "Kay"
            },
            {
                "family": "Anicic",
                "given": "Darko"
            },
            {
                "family": "Runkler",
                "given": "Thomas A."
            },
            {
                "family": "Graf",
                "given": "René"
            }
        ],
        "URL": "https://omanscience.com/en/articles/llm-driven-multi-agent-control-for-skill-based-smart-manufacturing",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]