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Abstract Monitoring daily drilling mud data is essential for maintaining operational performance and rig-site safety. However, frequent variations in mud report templates across vendors make centralized parsing and digitalization highly labor-intensive and costly. Our previous study demonstrated the technical and economic feasibility of automating this process using generative AI (GenAI.) Building on that foundation, this work introduces a self-improving, template-agnostic agentic GenAI framework that fully automates the parsing process, including for new and previously unseen report formats, achieving substantial gains in efficiency, scalability, and adaptability for large-scale deployment and potentially transforming the operational model of mud monitoring services. An autonomous cloud-based workflow was developed to enable end-to-end mud report processing, covering email ingestion, GenAI-driven parsing, database integration, and dashboard visualization. At its core, the system employs a multi-agent architecture that integrates drilling and fluid domain expertise with generative AI reasoning to extract structured data from unstructured PDF reports. Within this architecture, a supervisory GenAI agent oversees the parsing process, evaluates performance, and autonomously optimizes the prompts used by a secondary extraction agent to improve accuracy and adaptability. Through this self-improving feedback mechanism, the system continuously refines prompt quality to accommodate new and evolving report templates. By learning from prior examples and analyzing report layouts, the framework generalizes effectively to unseen formats without manual intervention. The resulting solution autonomously parses complex mud reports with high accuracy and low cost, accelerates new vendor onboarding, and delivers reliable structured data in a fraction of the time required by traditional manual workflows.