See It Work
Documentation Support Trust Center Pricing Partners
See It Work
SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+
Available CORE-ANOMALY-RCA-AGT-001 AI Agent

Agentic Anomaly Detection & Root Cause Analysis Agent

Continuously monitors process data to detect multi-variable anomalies using advanced algorithms, then performs intelligent causal diagnosis and delivers evidence-based root cause analysis with actionable recommendations.

ManufacturingMiningOil & GasEnergy & UtilitiesWater & Wastewater Anomaly Detection

Target outcome · Early detection of process anomalies and faster root cause identification — reducing unplanned downtime and recurring quality issues across production systems.

Business problem

Manufacturing operations generate vast amounts of process data, but extracting meaningful insights about anomalies and their causes remains complex. Traditional monitoring approaches detect problems only after they have already impacted production, provide limited causal understanding, and generate false alarms that overwhelm operators with noise rather than actionable intelligence.

Read more Show less

Static threshold-based monitoring misses subtle multi-variable patterns and complex causal relationships. Root cause analysis is time-consuming, manual, and often based on incomplete information. Critical process relationships and dependencies remain hidden until failures occur, while lessons from previous incidents are not systematically applied to prevent recurrence.

What it does

The Anomaly Detection and Root Cause Analysis Agent is an autonomous Decision Agent that continuously monitors process data using Composite AI — combining machine learning algorithms, statistical analysis, pattern recognition, and causal inference.

Read more Show less

It detects subtle anomalies across interconnected process variables, performs intelligent root cause analysis to identify true causal relationships, and provides actionable insights with transparent reasoning paths and confidence levels through XMPro's APEX AI orchestration layer.

Current process vs. with AI Agent

TODAY · ANOMALY DETECTIONREACTIVE
×
Anomaly identificationStatic thresholds trigger alerts after impact has already occurred
×
Root cause determinationManual investigation by engineers using fragmented data; hours or days to conclude
×
Alert prioritisationAll alarms treated equally, causing operator fatigue and missed critical signals
×
Cross-system correlationProcess data siloed across historians, SCADA, and MES with no integrated analysis

Outcomes and measurement

Anomaly detection lead time

Baseline Detected at or after point of impact
With agent Detected hours to days before production impact

Root cause analysis cycle time

Baseline 4–24 hours manual investigation
With agent Under 30 minutes with automated causal reasoning

False alarm rate

Baseline High false-positive rate causing alarm fatigue
With agent Significant reduction through intelligent filtering and contextual scoring

Recurring failure rate

Baseline Recurring issues due to symptom-only fixes
With agent Reduced recurrence through root-cause-targeted corrective actions

*All figures are typical ranges. Achievable range depends on existing control maturity, data quality, and site-specific conditions.

Data inputs

Other

Ingests real-time and historical process data via XMPro Data Stream Designerequipment status indicatorsquality measurementsalarm and event logsoperator actionsrecipe parametersand maintenance activity records

including process variables

temperaturepressureflowlevelcomposition

laboratory results

production schedules

*Categories only — no tag names or system-specific field references. Exact data mapping is scoped per site.

Scoping questions

Expect these questions in a first scoping conversation. They signal engineering discipline and help narrow the template to your specific site context.

  1. Which process systems, historians, and SCADA platforms will provide real-time data to the agent, and what is the current data refresh cadence?
  2. What are the highest-impact process anomalies or recurring failure modes you need the agent to detect first?
  3. Do you have labelled historical anomaly data or incident records that can be used to seed the causal models?
  4. What are the critical process safety limits that must be treated as hard escalation boundaries within the bounded autonomy framework?
  5. How will the agent's findings integrate into your existing corrective action and work order workflows in CMMS or MES?

Want our AI to walk you through these scoping questions?

SPEAK WITH OUR TEAM

Get specialist advice on scoping this for your site.

Our specialists will help you understand how the Agentic Anomaly Detection & Root Cause Analysis Agent fits your operations, what data you'd need, and what a scoping engagement typically looks like.

← Browse all templates