AI-Powered IT Project Risk Management System Using Multi-Agent Architecture, RAG, and LangGraph
Year: 2026
Type: article
Abstract: 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.
Authors: Soumyadip Changder, Pinaki Karmakar
Institution: University of Engineering & Management
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7155227906
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