Structure Over Scale: Towards the Dependable Integration of Language Agents in Industry

Abstract

Language-model  agents are increasingly proposed to operate and query industrial systems, yet their adoption meets a persistent obstacle: scaling improves reliability, but not to the level an industrial environment demands, where a wrong decision can cause losses. This work argues a position, structure before scale, whereby, without denying the role of model capability, the greatest lever for reliability on the plant floor is the structure imposed on the model, which moreover does not require the infrastructure cost of a larger model. The position is built from recent literature, which converges on three enablers (knowledge graphs, agents and the MCP protocol) and on a well-documented reliability ceiling, and is illustrated with one deployment: operating a SCADA system through an agent and MCP, where encapsulating brittle coordination in code cuts model invocations from 20--50 to a single one.