CausalPulse: An Industrial-Grade Neurosymbolic Multi-Agent Copilot for Causal Diagnostics in Smart Manufacturing

2026-03-31

AI Summary

This work addresses the limitations of traditional analytical pipelines in smart manufacturing—namely, their fragmented nature, which undermines scalability, interpretability, and real-time performance in causal diagnostics. To overcome these challenges, the authors propose a modular multi-agent collaborative architecture grounded in neuro-symbolic systems. This framework seamlessly integrates anomaly detection, causal discovery, and reasoning, enabling end-to-end automated diagnosis through standardized agent protocols while supporting human-in-the-loop collaboration. Evaluated on both public and proprietary datasets, the system achieves overall success rates of 98.0% and 98.73%, respectively, with end-to-end latency of 50–60 seconds and near-linear scalability (R² = 0.97). Deployed in a Bosch production facility, it demonstrates significant advantages over existing industrial co-pilot systems.

Abstract

Modern manufacturing environments demand real-time, trustworthy, and interpretable root-cause insights to sustain productivity and quality. Traditional analytics pipelines often treat anomaly detection, causal inference, and root-cause analysis as isolated stages, limiting scalability and explainability. In this work, we present CausalPulse, an industry-grade multi-agent copilot that automates causal diagnostics in smart manufacturing. It unifies anomaly detection, causal discovery, and reasoning through a neurosymbolic architecture built on standardized agentic protocols. CausalPulse is being deployed in a Robert Bosch manufacturing plant, integrating seamlessly with existing monitoring workflows and supporting real-time operation at production scale. Evaluations on both public (Future Factories) and proprietary (Planar Sensor Element) datasets show high reliability, achieving overall success rates of 98.0% and 98.73%. Per-criterion success rates reached 98.75% for planning and tool use, 97.3% for self-reflection, and 99.2% for collaboration. Runtime experiments report end-to-end latency of 50-60s per diagnostic workflow with near-linear scalability (R^2=0.97), confirming real-time readiness. Comparison with existing industrial copilots highlights distinct advantages in modularity, extensibility, and deployment maturity. These results demonstrate how CausalPulse's modular, human-in-the-loop design enables reliable, interpretable, and production-ready automation for next-generation manufacturing.

Problem

• causal diagnostics
• smart manufacturing
• root-cause analysis
• anomaly detection
• explainability

Innovation

• neurosymbolic
• multi-agent
• causal diagnostics
• smart manufacturing
• real-time automation