Agentic AI for Safety-Aware Process Monitoring and Fault Diagnosis: A Review

Process industries, including chemical, petrochemical, energy, and wastewater treatment systems, operate under high-dimensional, nonlinear, dynamic, and safety-critical conditions. Data-driven process monitoring and fault detection and diagnosis (FDD) have progressed from multivariate statistical monitoring to machine learning and deep learning. Yet many deployed or prototype systems still behave mainly as fault classifiers: they detect deviations, but offer limited causal explanation, weak integration of plant knowledge, and insufficient support for safe operator action. Recent advances in large language models, retrieval-augmented generation, digital twins, explainable artificial intelligence, and multi-agent systems make it timely to revisit FDD as an agentic decision-support workflow. This focused review examines how agentic AI can support process-industry monitoring and diagnosis by integrating process data, engineering knowledge, model outputs, and safety constraints. We synthesize established FDD foundations, deep-learning-based FDD, process-safety context, bridging technologies, and emerging LLM- and agent-based studies. The review argues that the near-term value of industrial agents lies not in unrestricted autonomous plant control, but in safety-aware, explainable, and human-in-the-loop decision support. We propose a process-industry-oriented taxonomy of agents, summarize enabling technologies and representative application settings, and identify evaluation criteria, benchmark requirements, limitations, and deployment conditions for trustworthy industrial agents.