Abstract: Generative artificial intelligence (AI) has become a cornerstone of modern content production, yet the deployment of externally hosted models poses significant data security and compliance challenges. This study presents a fully agentic AIsystem that integrates locally hosted Gamma and Qwen large language models (LLMs) to autonomously draft, validate, and govern content for web page articles, external email responses, and internal knowledge base entries. We describe a multi agent architecture in which a Drafting Agent employs Qwen 2.0 for rapid generation, a Validation Agent uses Gamma Prime for factual correctness and policy compliance, and a Governance Agent enforces brand voice, privacy, and regulatory rules via a policy driven rule engine. The system runs on a private GPU cluster, encrypts data at rest and in transit, and maintains full audit trails. Over a three month pilot (June – August 2026) we produced 360 content artifacts; the agentic pipeline achieved an average fluency score of 4.3 / 5, a brand voice consistency rate of 92 %, and a policy violation incidence of 0 %. Productivity gains include a 40 % reduction in drafting time and a 12 % increase in email click through rates. These results demonstrate that locally hosted, agentic LLMs can deliver high quality, compliant content at scale, offering a viable alternative to cloud based generative services for regulated enterprises.
Keywords: Generative AI, Agentic AI, Local LLM, Gamma, Qwen, Content Automation, Data Governance, Compliance, Personalisation.
Title: Agentic AI for Secure, Proprietary Content Creation: Leveraging Locally Hosted Gamma and Qwen Models for Drafting, Validation, and Governance
Author: Asif Ali Khan
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 14, Issue 3, July 2026 - September 2026
Page No: 101-105
Research Publish Journals
Website: www.researchpublish.com
Published Date: 15-September-2026
