Anomaly Detection & Investigation Digital Worker
A DAG-based agentic workflow orchestrates specialized AI agents that ingest data streams, detect anomalies using ML models, perform root cause analysis with chain-of-thought reasoning, assess impact, and automatically execute remediation actions while maintaining full observability..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Workflow configuration interface with LLM settings, detection thresholds, and meter data input
DAG-based agent orchestration showing completed validation, enrichment, and detection stages
Chain-of-thought reasoning with root cause analysis, similar case retrieval, and tool call execution
Workflow completion summary showing 15-agent collaboration with 94% efficiency in 25.2 seconds
AI Agents
Specialized autonomous agents working in coordination
Data Ingestion Agent
Smart meters and SCADA systems generate massive volumes of time-series data that must be collected, normalized, and validated before analysis can begin.
Core Logic
Connects to iMSys, SMGW, and SCADA endpoints via standardized protocols. Performs real-time data validation, normalization to common schema, and buffering. Detects data quality issues (gaps, outliers, transmission errors) and triggers re-collection when needed. Supports 15-minute interval data at scale.
Anomaly Detection Agent
Identifying true anomalies from millions of data points requires sophisticated pattern recognition that distinguishes genuine issues from normal variations and seasonal patterns.
Core Logic
Employs ensemble ML models including statistical methods (Z-score, IQR), time-series analysis (Prophet, ARIMA), and deep learning (LSTM autoencoders). Maintains baseline profiles per meter/asset type. Classifies anomalies by severity and type (consumption spike, reverse flow, meter tampering, grid fault). Uses GPT-4 Turbo at temperature 0.1 for deterministic classification.
Root Cause Analysis Agent
Detected anomalies require expert-level investigation to determine underlying causes, which traditionally requires experienced engineers and significant time.
Core Logic
Implements chain-of-thought reasoning using Claude 3.5 Sonnet to systematically analyze anomalies. Correlates with weather data, grid topology, historical incidents, and customer profiles. Generates structured reasoning traces with evidence, hypotheses, and confidence scores. Produces human-readable investigation reports with recommended next steps.
Impact Assessment Agent
Understanding the business, operational, and customer impact of anomalies is essential for prioritization but requires cross-domain analysis.
Core Logic
Calculates financial impact (revenue loss, penalty exposure), operational impact (equipment stress, safety risks), and customer impact (affected accounts, SLA violations). Integrates with asset management and CRM systems. Uses predictive models to forecast cascading effects. Prioritizes issues by composite risk score.
Decision Engine Agent
Determining appropriate response actions requires balancing multiple factors including severity, cost, regulatory requirements, and available resources.
Core Logic
Evaluates remediation options against configurable business rules and constraints. Considers regulatory requirements (EnWG, MsbG), SLA commitments, and resource availability. Generates ranked action recommendations with cost-benefit analysis. Routes critical decisions to human approvers via HITL workflow.
Action Executor Agent
Approved remediation actions must be executed across multiple downstream systems reliably and with full audit trails.
Core Logic
Orchestrates execution across MDM, billing, field service, and communication systems. Generates work orders, adjusts billing, sends notifications, and updates asset records. Implements rollback capabilities for failed actions. Maintains immutable audit logs for compliance. Confirms execution success and triggers follow-up monitoring.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization