AI-Powered IT Project Risk Management System Using Multi-Agent Architecture, RAG, and LangGraph

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.