AI SCADA Alert Triage Agent

How Cassidy automates alarm management using AI

Step 1: Ingest alarms and normalize context

The Workflow triggers on new alarms from your SCADA, DCS, or historian via OPC UA, MQTT, or direct integration. Cassidy normalizes tag names and maps each alarm to your asset hierarchy, equipment, and current operating mode.

Step 2: Detect floods and correlate sequences

Cassidy analyzes alarm timing and topology to flag flood conditions, identify probable first-out alarms, and cluster related alerts by equipment or process unit—filtering chatter and duplicates automatically.

Step 3: Apply dynamic prioritization

Using your Alarm Philosophy Document and risk matrix stored in the Knowledge Base, Cassidy assigns context-aware priority based on consequence severity, time-to-respond, and operating state—surfacing the minimum actionable set.

Step 4: Recommend suppression and grouping

For consequential alarms following a root cause, Cassidy recommends dynamic suppression or grouping per your shelving and state-based alarming policies—reducing operator overload while maintaining full traceability.

Step 5: Deliver guidance with Human-in-the-Loop approval

Cassidy presents operators with the prioritized alarm, likely cause hypothesis, recommended action steps, and links to SOPs or P&IDs. Operators accept, reject, or annotate recommendations—capturing feedback for governed model improvement.

Step 6: Escalate and integrate

If response deadlines are exceeded or severity warrants, Cassidy automatically creates CMMS work orders, routes tickets, or triggers callout escalations—ensuring nothing slips through.

Step 7: Measure and report KPIs

Cassidy continuously tracks EEMUA 191/ISA-18.2 metrics—alarms per 10 minutes, flood frequency, bad actors, priority distribution, and response times—generating dashboards and audit-ready reports for lifecycle compliance.