Abstract
Traditionally, quality management relies on siloed systems of record such as quality management system (QMS), application lifecycle management (ALM), and manufacturing execution system (MES) platforms. These systems are often static, passive repositories that require significant manual effort to connect disparate data and derive actionable insights. Fragmentation and lack of proactive intelligence can lead to delays in identifying quality issues, ensuring compliance, and accelerating innovation. This disclosure describes a quality management framework to provide collaboration between human experts and specialized artificial intelligence (AI) agents for proactive and semi-autonomous quality management. The framework provides a distributed, intelligent ecosystem where a central AI engine can delegate specific, complex quality workflows to specialized AI agents that operate continually and autonomously, with a human-in-the-loop for final approval. The framework is built on a three-layer architecture that can be powered by a cloud computing platform.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Neelakandan, Ramakrishnan; Singh, Teginder; and Patel, Bakul, "Proactive and Semi-autonomous Agentic Quality Management Framework", Technical Disclosure Commons, (October 15, 2025)
https://www.tdcommons.org/dpubs_series/8727