Plant engineering greatly sped up using code and artificial intelligence
Engineering process plants as code in the age of agentic AI
Computational Engineering, Finance, and Science
Summary
Designing big industrial plants usually takes many years because information is spread out and handled manually. The authors suggest treating the entire plant design as code—organized, machine-readable files that can be tracked and updated like software. This approach allows AI agents to coordinate tasks and run automated checks, making the process much faster and less error-prone. They also highlight challenges and actions needed to make this shift practical and safe.
What this means in practice
- •For process plant engineers: Coordinate and speed up multistep plant design work by managing all data as linked code files with AI-driven task automation and verification.
- •For software developers in industrial automation: Build AI agents and tools that replace manual data handoffs and checks by operating on standardized plant code repositories.
A position paper. It proposes an approach and reports no results.
Authors
Artur M. Schweidtmann, Davy Demeyer
Abstract
Agentic artificial intelligence (AI) could one day shorten process plant engineering from years to weeks. Today, however, plant information is scattered across documents and tools and must be carried between them by hand, so an AI agent placed into current workflows inherits the same silos. In this Comment, we argue for Plant-as-Code: defining the engineering of a plant as a version-controlled collection of machine-readable files that follow, where possible, existing standards such as DEXPI and IFC. On this foundation, Design-Ops extends DevOps principles to plant engineering, linking every engineering item to the information it derives from and replacing manual checking with automated tests. Agents can then coordinate routine work across disciplines while deterministic tools perform the calculations. Verification sets the pace: agents propose, tools check, and engineers review and authorize every change. We outline the risks of this shift and the research, industry and policy actions it requires.