NVIDIA has introduced a GPU-accelerated AI agent designed to automate industrial alarm management, addressing the growing data overload faced by maintenance technicians and engineering teams. As machinery becomes increasingly interconnected, operational facilities generate hundreds of alarms hourly, overwhelming personnel who traditionally spend extensive time triaging alerts, cross-referencing sensor data, consulting documentation, and drafting work orders. To streamline this workflow, NVIDIA developed a per-alarm analysis agent powered by its Nemotron open-weight models, NeMo libraries, and OpenShell secure runtime, accessible through a single HTTP endpoint for seamless integration into existing operator interfaces and SCADA systems. The agent operates through a structured three-phase process: evidence gathering, specialist verification, and action generation. It utilizes NVIDIA CUDA-X libraries, including cuDF and cuVS, to rapidly filter structured sensor data and search historical remedy databases, enabling the system to identify recurring fault patterns and validate solutions against past tickets. For unstructured documentation such as technical manuals and operational playbooks, NeMo Retriever accelerates retrieval-augmented generation, indexing complex documents for instant access. When ambiguous alerts arise, domain-specific subagents perform advanced diagnostics using specialized tools like cuFFT and cuML, confirming whether warnings stem from genuine component failure or sensor anomalies. Intelligent reasoning and orchestration are handled by the Nemotron 3 Nano and Super models, which process prompts, manage tool dispatch, and synthesize findings into structured observations, root-cause hypotheses, and recommended actions. High-confidence outputs meeting safety and policy thresholds are automatically dispatched, while lower-confidence cases are escalated to technicians with precompiled evidence. The entire workflow runs within a sandboxed environment governed by OpenShell, which enforces declarative YAML policies to prevent unauthorized network access, data exfiltration, and uncontrolled tool execution, ensuring compliance with enterprise security standards. By consolidating multi-system data retrieval, expert-level signal analysis, and automated report drafting, the agent significantly reduces manual triage time and improves resolution accuracy. NVIDIA positions the solution as a scalable architecture that continuously improves as additional alarm data trains its knowledge base. The stack is deployable via the NVIDIA NeMo Agent Toolkit and aligns with the company broader AI-Q Blueprint for enterprise intelligence. Organizations can access optimized Nemotron containers through NVIDIA NIM or Hugging Face, enabling rapid prototyping and domain-specific fine-tuning for industrial environments seeking to minimize downtime and optimize maintenance operations.