Agentic AI for Manufacturing Data: Building Intelligent Systems That Query, Reason, and Act

Engineering notes

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Chinese explanation / 中文解读

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Original abstract

Most manufacturing companies already own the data needed to answer their operational questions. Getting an answer still takes days, because the question crosses four systems and four people, and every crossing costs time. This book is about closing that distance with agentic systems that query the source systems directly. The architecture is worked out against a composite tier-two aerospace supplier: four sites, 2,100 employees, machined structural components. SAP ECC for orders and financials. A commercial MES at two plants and a homegrown SQL Server application at the two acquired in 2019. Teamcenter holds the engineering bill of material and the manufacturing bill is re-derived in SAP. Nonconformances live in a separate quality system with its own part master. Machine telemetry sits in a process historian owned by the controls group. Eighty-seven registered datasets, roughly 3,600 tables, about 92,000 columns. The numbers stay consistent across all sixteen chapters, so a figure introduced in Chapter 1 still holds in Chapter 12. Part I, Chapters 1 to 4, establishes the problem: why manufacturing data resists ordinary approaches, what an agent is and when not to use one, how the systems landscape is structured, and why published text-to-SQL accuracy does not predict what you get against a plant schema. Part II, Chapters 5 to 9, is the architecture and the core of the book. Routing a question across a registry far too large to put in a context window. SQL generation for HANA, Oracle, PostgreSQL, T-SQL, and Trino, and why a process historian does not fit that pattern. Access control that filters schemas before the model sees them, because table and column names are often the controlled artifact. Memory, and how the system fits the plant's decision cadence. Part III, Chapters 10 to 13, is construction: implementation, onboarding datasets without code, deploying where the network does not reach the internet, and knowing whether any of it works. Part IV, Chapters 14 to 16, covers what becomes possible afterwards, the ontology in full, and running this as an enterprise capability. Four appendices give a dataset definition reference, a specialist prompt template, a security review checklist, and a two-way glossary. Shop floor supervisors, quality engineers, facilities staff, and plant maintenance technicians will find a clear account of what these systems can actually do against their data. Technologists who have built retrieval and text-to-SQL pipelines will find the domain material that the public benchmarks leave out. An executive deciding whether to fund the work should read Chapter 1, the limits section at the end of Chapter 2, and Chapter 16. That is about forty minutes and covers the honest case. You should know working SQL and have a general familiarity with how language models are prompted. No machine learning background is required. Very little manufacturing knowledge is assumed; the book builds the vocabulary as it goes. The code is mostly Python and is illustrative rather than executable as printed. The limits are stated where they belong rather than collected at the end. An agent does not repair bad source data. It does not settle which of three defensible on-time-delivery definitions your company means; that decision stays with people. Compressing a retrieval loop from days to under a minute changes which questions get asked, and that is a large enough effect without overstating it.

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