Effective risk management remains one of the most persistent challenges in information technology project delivery. Traditional approaches rely heavily on periodic manual assessments, static checklists, and subject-matter intuition, which collectively fail to keep pace with the dynamic and interconnected nature of modern software projects. This paper presents an AI-Powered IT Project Risk Management System that addresses these limitations through a coordinated multi-agent architecture orchestrated via LangGraph, augmented with Retrieval-Augmented Generation (RAG) backed by ChromaDB, and driven by large language models accessed through the Groq API. The system comprises four specialised agents—a Market Analysis Agent, a Risk Scoring Agent, a Project Tracking Agent, and a Reporting Agent—that operate in a defined pipeline to evaluate both exogenous market signals and endogenous operational indicators. Risk dimensions including market, technical, financial, regulatory, and operational factors are individually scored on a 0–100 scale and consolidated into a structured JSON report surfaced through an interactive Streamlit dashboard. Empirical evaluation on a representative ERP implementation scenario yields an overall risk score of 66/100 (High) with a 68 % schedule-delay probability, demonstrating the system’s capacity to produce actionable, prioritised mitigation guidance. The architecture is designed for extensibility and real-world deployment, with future work targeting live Jira integration, reinforcement-learning-based adaptive scoring, and mobile-accessible reporting interfaces.
Changder et al. (Wed,) studied this question.