Master's Thesis: Multi-Agent Root Cause Analysis for Industrial Engineering

We are offering a Master’s thesis opportunity in collaboration with academic and industrial partners contributing to the MONA LISA research initiative, which focuses on monitoring and analytics across the whole lifecycle of cyber-physical systems, from concept and models to deployed systems. The thesis is aimed at students who want to work at the intersection of AI, software engineering, industrial analytics, and trustworthy decision support.

The proposed topic centres on AI-agent support for root cause analysis in complex engineering environments. In the documented Alstom / MONA LISA use case, AI is intended to support engineers by combining evidence from issue tickets, logs, product documentation, historical cases, source code, and engineering workflow items in order to shorten analysis time and improve issue handling. The long-term ambition is not to replace engineering expertise, but to provide AI support that helps engineers navigate fragmented information more efficiently and with better traceability.

Thesis objective
The objective of the thesis is to design, implement, and evaluate a multi-agent or orchestrator–worker AI architecture for industrial root cause analysis. The system may include specialised agents that work with specific evidence types, for example an agent for ticket and defect history, an agent for product documentation, an agent for source-code context, an agent for work-item relations, or an agent for log-based clues.