Construction sites represent hazardous work environments, necessitating comprehensive risk assessments to ensure worker safety. Traditional methods of risk assessment are critical for identifying and mitigating potential hazards. However, the complex nature of these safety documents often poses a significant challenge. This gap in safety knowledge can lead to inadequate risk assessments and heightened workplace hazards. The emergence of advanced technologies in Artificial Intelligence (AI) such as Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) presents new opportunities to address these challenges. While LLMs enhance natural language processing, this hallucination and lack of reliable source referencing can undermine their effectiveness. RAG offers a promising solution by integrating information retrieval, though its application in construction safety engineering remains unexplored. This paper introduces a safety risk assessment platform leveraging agentic AI systems, LLMs, hybrid semantic search, and RAG to generate expert-level safety risk assessments. The system utilized extensive safety knowledge sources from Safe Work Australia into a framework where users can create risk assessment reports for specific work tasks purely based on natural language input. Preliminary evaluations of the platform demonstrate its effectiveness in generating such reports, although improvements are necessary to reduce document verbosity and enhance usability.