The Machine Leadership Journal

This paper details the implementation of an AI Agent and MLOps architecture designed to modernize Competency Maps (C-MAPs), On-the-Job (OTJ) Task Books, and technical skills assessments within an engineering organization. In data-driven organizations, static learning documents often lead to progressive knowledge decay. This solution utilizes a Lang Graph-driven Retrieval-Augmented Generation (RAG-A) framework to create a "self-healing" talent ecosystem. By autonomously monitoring industry standards (Professional Societies, Tier 1 Competitors, Patent Filings) and internal SOPs, the Agent proactively suggests competency updates and personalized learning paths based on the domain.

The architecture is designed for multi-HRIS compatibility, providing seamless API orchestration with SAP, Oracle, and Workday. This ensures that talent intelligence is embedded directly into existing workflows. Supported by a robust MLOps pipeline—including model versioning via MLFlow, GitHub actions, and automated evaluation—the system maintains high accuracy and security within private cloud environments. By integrating Human-in-the-Loop (HITL) governance, the AI serves as a high-fidelity "Co-Pilot" for HR and Engineering leaders. This transition from manual learning updates to proactive talent intelligence significantly reduces time-to-autonomy, enhances process reliability, and secures the technical succession pipeline for data-driven organizations.