Can LLMs build P&ID diagrams from text?
An Agentic Approach to Automatic Creation of P&ID Diagrams from Natural Language Descriptions
This paper introduces an AI-powered copilot for automatically generating Piping and Instrumentation Diagrams (P&IDs) from natural language descriptions. It uses a multi-agent system based on the "Plan and Execute" paradigm, where agents collaborate to translate user prompts into a structured intermediate representation and then deterministically generate DEXPI-compliant XML, which can be visualized as a P&ID. This approach enhances reliability, completeness, and provenance compared to directly prompting LLMs with zero-shot or few-shot learning. The system is designed for iterative, subsystem-level generation, allowing engineers to refine designs and integrate with existing workflows. Key benefits for LLM-based multi-agent systems include improved reliability in XML generation, efficient handling of complex prompts, and facilitation of downstream tasks like Q&A and root cause analysis.