Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context, determines the correct procedure, checks whether a specialist signal confirms the failure mode, and writes up a recommendation. This process remains consistent, and is well-suited for an AI agent .

This post discusses a per-alarm analysis AI agent built with NVIDIA NeMo libraries, using NVIDIA Nemotron open models for intelligence and the NVIDIA OpenShell secure runtime. Given an alarm with its sensor frame, the agent:

Gathers context (history, playbooks, similar past cases)

Runs specialist checks (for example, using NVIDIA nv-tesseract for anomaly detection metrics, OCR using NeMo Retriever for scanned playbooks)

Issues a structured evidence package: observation, root-cause hypothesis, remedy, recommended action

The agent is GPU-accelerated end-to-end and lives behind a single HTTP endpoint.…