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GMPilot: An Expert AI Agent For FDA cGMP Compliance

arXiv AI Archived Mar 24, 2026 ✓ Full text saved

arXiv:2603.20815v1 Announce Type: new Abstract: The pharmaceutical industry is facing challenges with quality management such as high costs of compliance, slow responses and disjointed knowledge. This paper presents GMPilot, a domain-specific AI agent that is designed to support FDA cGMP compliance. GMPilot is based on a curated knowledge base of regulations and historical inspection observations and uses Retrieval-Augmented Generation (RAG) and Reasoning-Acting (ReAct) frameworks to provide rea

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    Computer Science > Artificial Intelligence [Submitted on 21 Mar 2026] GMPilot: An Expert AI Agent For FDA cGMP Compliance Xiaohan Wang, Nan Zhang, Sulene Han, Keguang Tang, Lei Xu, Zhiping Li, Xiue (Sue)Liu, Xiaomei Han The pharmaceutical industry is facing challenges with quality management such as high costs of compliance, slow responses and disjointed knowledge. This paper presents GMPilot, a domain-specific AI agent that is designed to support FDA cGMP compliance. GMPilot is based on a curated knowledge base of regulations and historical inspection observations and uses Retrieval-Augmented Generation (RAG) and Reasoning-Acting (ReAct) frameworks to provide real-time and traceable decision support to the quality professionals. In a simulated inspection scenario, GMPilot shows how it can improve the responsiveness and professionalism of quality professionals by providing structured knowledge retrieval and verifiable regulatory and case-based support. Although GMPilot lacks in the aspect of regulatory scope and model interpretability, it is a viable avenue of improving quality management decision-making in the pharmaceutical sector using intelligent approaches and an example of specialized application of AI in highly regulated sectors. Comments: 14 pages, 1 figure Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.20815 [cs.AI]   (or arXiv:2603.20815v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.20815 Focus to learn more Submission history From: Xiaomei Han [view email] [v1] Sat, 21 Mar 2026 13:21:24 UTC (246 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Mar 24, 2026
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    Mar 24, 2026
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