Agent Nexus: Industry 4.0 Autonomous Decision Twin

About this project

What it does: Decision Twin is an agentic AI system for manufacturing plants that automates operational decisions when a machine reports an anomaly, abnormal vibration, temperature, pressure, etc. Instead of one model producing a single verdict, it launches a full multi-agent negotiation: Sensor, Maintenance, Memory, Production, Quality, Inventory, and Finance agents gather evidence in parallel, then a Devil's Advocate and Safety agent stress test the leading proposal against risk thresholds and hard SOP rules (like budget caps). If a proposal gets challenged, the system renegotiates and re run a quantitative scenario simulation comparing options like immediate repair, delayed repair, or reduced capacity operation, each scored on cost and risk. It converges on a final recommendation with a confidence score, an auto generated work order, and a supervisor notification flagging whether approval is required. Who it's for: Plant managers, maintenance engineers, and operations teams who need fast, defensible decisions on machine downtime and repair timing without manually cross-checking sensor data, incident history, spare parts inventory, and financial impact by hand every time an alert fires. What makes it special: It's not a black-box classifier but it's an auditable negotiation. Agents can genuinely veto and challenge each other (example: Safety blocking an "immediate repair" that breaches a budget cap, forcing a pivot to "delay repair"), and every round of that back and forth is logged. It transparently reconciles conflicting data sources (user-submitted readings vs. live sensor telemetry), showing exactly which value was trusted and why. Every decision comes with a full trace agent-by-agent reasoning, precedent incidents consulted, vetoes, and simulation scores, giving operators a decision they can actually inspect and trust, not just accept.