GMPilot: An Expert AI Agent For FDA cGMP Compliance
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866912977386471424 |
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| author | Wang, Xiaohan Zhang, Nan Han, Sulene Tang, Keguang Xu, Lei Li, Zhiping Xiue Liu Han, Xiaomei |
| author_facet | Wang, Xiaohan Zhang, Nan Han, Sulene Tang, Keguang Xu, Lei Li, Zhiping Xiue Liu Han, Xiaomei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20815 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GMPilot: An Expert AI Agent For FDA cGMP Compliance Wang, Xiaohan Zhang, Nan Han, Sulene Tang, Keguang Xu, Lei Li, Zhiping Xiue Liu Han, Xiaomei Artificial Intelligence 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. |
| title | GMPilot: An Expert AI Agent For FDA cGMP Compliance |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.20815 |