Modern manufacturing systems demand real-time, trustworthy, and interpretable insights into anomalies and their underlying causes. However, conventional pipelines treat anomaly detection, causal inference, and decision-making as siloed tasks, lacking integration, explainability, and adaptability. We present CausalPulse, an intelligent, multi-agent copilot for automated Root Cause Analysis (RCA) in industrial settings. Built on a modular and extensible architecture, the system leverages standard agentic protocols, including Model Context Protocol (MCP), Agent2Agent (A2A), and LangGraph for dynamic tool and agent discovery and seamless orchestration of tasks. Agents dynamically interact to perform data preprocessing, anomaly detection, causal discovery, and root cause analysis through a neurosymbolic workflow that combines symbolic reasoning with neural methods. Intelligent postprocessing pipelines enable automatic chaining of agent tasks, enhancing contextual awareness and adaptability. CausalPulse is evaluated using both an academic public dataset (i.e., Future Factories) and an industrial proprietary dataset (i.e., Planar Oxygen Sensor Element) and shows that the system outperforms traditional baselines in interpretability, trustworthiness, and operational utility.

Modern manufacturing environments demand not only accurate predictions but also interpretable insights to process anomalies, root causes, and potential interventions. Existing AI systems often function as isolated black boxes, lacking the seamless integration of prediction, explanation, and causal reasoning required for a unified decision-support solution. This fragmentation limits their trustworthiness and practical utility in high-stakes industrial environments. In this work, we present CausalTrace, a neurosymbolic causal analysis module integrated into the SmartPilot industrial CoPilot. CausalTrace performs data-driven causal analysis enriched by industrial ontologies and knowledge graphs, including advanced functions such as causal discovery, counterfactual reasoning, and root cause analysis (RCA). It supports real-time operator interaction and is designed to complement existing agents by offering transparent, explainable decision support. We conducted a comprehensive evaluation of CausalTrace using multiple causal assessment methods and the C3AN framework (i.e. Custom, Compact, Composite AI with Neurosymbolic Integration), which spans principles of robustness, intelligence, and trustworthiness. In an academic rocket assembly testbed, CausalTrace achieved substantial agreement with domain experts (ROUGE-1: 0.91 in ontology QA) and strong RCA performance (MAP@3: 94%, PR@2: 97%, MRR: 0.92, Jaccard: 0.92). It also attained 4.59/5 in the C3AN evaluation, demonstrating precision and reliability for live deployment.

According to a study done in 2014 by National Health Interview Survey around 6.3 million children in United States suffer from asthma [1]. Asthma remains one of the leading reasons for pediatric admissions to children's hospitals, and has a prevalence rate of approximately 10% in children and it leads to missed days from school and other societal costs. This occurs despite improved medications to control asthma symptoms. Asthma management is challenging as it involves understanding asthma causes and avoiding asthma triggers that are both multi- factorial and individualistic in nature. It is almost impossible for doctors to constantly monitor each patient's health and environmental triggers. According to a recent article, the IoT device market in health-care will increase to a worth of $117 billion by the year 2020 [2]. The monitoring segment of IoT devices have predicted to increase $15 billion in 2017 [5]. The sales of smart watches, fitness and health trackers, are expected to account for more than 70% of all wearables sale worldwide in 2016 [6]. According to IBM, the volume of health-care data has reached to 150 exabytes in 2017 [7]. The data generated from these consumer graded devices is increasing day by day. This data collection has exacerbated the problem of understanding the data and making sense of it. We can use these low-cost sensors and consumer graded devices for continuous monitoring and management of asthma patients. We developed kHealth1, a framework for continuous monitoring of the patient's personal, public and population-based health signals and send alerts to the patient when a condition deserves patient's or clinician's attention. This can assist the clinician in determining the triggers and deciding the future course of action for prevention and treatment of the disease. More importantly, it can also help a patient to better take control of his/her health management by taking more timely actions(e.g., in case of asthma, using an inhaler in a more timely manner to ward off an attack). Our kHealth framework goes well beyond the efforts of data collection and focuses on contextual and personalized processing of multi-modal data to help understand asthma control level and vulnerability score (change in conditions that increases the chances of an adverse event, thus requiring proactive action). Another unique aspect of our research is close collaboration with clinician combined with on-going evaluation of clinician's at the Dayton Children's hospital which involves an ongoing trial of our novel technical approach with a cohort of 200 patients.