Towards Agentic Support for the Chaos Engineering of Cyber Physical Systems

Cyber-Physical Systems (CPS) require rigorous robustness testing, yet this activity remains largely manual and dependent on expert knowledge. Chaos engineering provides a structured methodology through characterizing the system, defining metrics, formulating hypotheses, and planning perturbations. However, it lacks systematic support. This paper investigates how AI can assist the systematic design of chaos engineering experiments for CPS. We show how the different phases of chaos engineering can be adapted to CPS contexts and how they can be effectively and efficiently supported by a multi-agent architecture, with phase-specific agents providing detailed justifications while keeping a human analyst in the loop either at the phase level or through global iterations. Partial results obtained from three industrial case studies show significant benefits from AI assistance, with around 50% efficiency gains in the design phase while improving coverage. Beyond the CPS domain, we also discuss applicability to other target domains (e.g. industrial or financial systems) and properties like secure chaos engineering.