AI Agentic Workflow for Automated Meter Data Validation and Settlement

Revenue leakage in energy trading and microgrid settlement stems from manual validation of raw interval meter data, a process prone to gaps, anomalies, and calculation errors. This workflow automates ingestion from AMI head-ends or SCADA, applies multi-stage validation rules, and calculates final settlements for billing or market transactions. The operational upside is direct: it converts a labor-intensive, error-prone monthly close into a continuous, auditable process that protects margin and accelerates cash flow by ensuring accurate, timely settlements.

Implementation integrates with utility market interfaces, DERMS, and financial systems like SAP or custom billing platforms. The architecture employs a rules engine for validation (e.g., missing intervals, implausible values), with ML agents flagging subtle patterns indicative of meter drift or fraud. Exceptions route to human review queues in tools like ServiceNow, while approved data flows to the calculation engine. Controls include versioned rule sets, full audit trails, and reconciliation reports against ISO statements to close the governance loop.

This workflow automates the high-volume, error-prone back-office process of turning raw interval meter data into validated, settled transactions. It directly addresses revenue leakage from missed readings, validation failures, and manual calculation errors, which can cost asset operators and retailers 2-5% of revenue. The system ingests data from AMI head-ends, SCADA, or third-party aggregators, applying a rules engine and ML models to flag anomalies like gaps, spikes, or implausible values before any financial calculation begins.

Implementation integrates with core enterprise systems: validated data flows into billing platforms (like Oracle CC&B or SAP) or market settlement systems (e.g., ISO settlement engines), while exceptions are routed via ServiceNow or Jira for analyst review. The architecture is built for observability, with each agent logging decisions for audit trails. Key controls include approval gates for large-value discrepancies, automated retries for transient failures, and scheduled reconciliation reports to ensure the pipeline's financial integrity matches the physical meter.

Phase 1 establishes the core ingestion and validation engine, focusing on a single asset or market to prove the data pipeline. We integrate with the primary meter data management (MDM) system or AMI headend, implementing rule-based validation for gaps and spikes. This initial stage delivers immediate value by automating the most repetitive data-wrangling tasks, reducing manual review time by 60-80% for the pilot scope, and creating a clean, auditable data foundation for settlement calculations.

Phase 2 expands validation logic and integrates settlement calculation, connecting to ISO market systems (e.g., CAISO, PJM) or billing platforms. We introduce ML models for anomaly detection and agentic routing of exceptions to human reviewers via a dedicated UI. The final phase establishes full API-based orchestration with upstream SCADA/DERMS and downstream ERP/finance systems, implementing governance controls and detailed audit trails. This staged approach ensures each layer is stable and delivering measurable cost avoidance or revenue assurance before proceeding.

This workflow automates the back-office validation and settlement of energy meter data, a critical yet repetitive bottleneck for microgrid operators and asset aggregators. Manual reconciliation of interval data from thousands of meters against market-clearing prices or retail tariffs is slow and error-prone, leading to revenue leakage and delayed cash flow. A custom agentic system ingests raw data from AMI head-ends or SCADA, applies validation rules for gaps and spikes, and passes clean data to a settlement engine integrated with billing systems like Oracle CC&B or market settlement platforms from ISOs.

Implementation requires a multi-agent architecture where a validation agent, built with frameworks like LangChain, checks data against configurable business rules and statistical models. Exceptions are routed to a human review queue in systems like ServiceNow, while clean data flows to the settlement engine for calculation. The system integrates with utility DERMS or ETRM platforms, providing full audit trails and configurable approval gates before initiating payouts. This end-to-end flow reduces settlement cycle time from days to hours and cuts operational costs by over 60%.

This workflow automates the high-volume, error-prone back-office process of validating meter data and calculating settlements for energy trades or demand response events. It directly reduces revenue leakage from missed intervals, incorrect calculations, and manual reconciliation delays. The architecture ingests raw data from AMI, SCADA, or DERMS systems, applies a rules engine for gap detection and anomaly scoring, and integrates with billing or market settlement platforms like Oracle Utilities or custom ETRM systems to post finalized values.

Implementation requires building a multi-agent orchestrator, likely with LangGraph, to manage the validation, settlement, and exception routing logic. Each validation rule—checking for timestamp gaps, implausible values, or cross-meter consistency—must be codified and monitored for performance drift. The human-in-the-loop layer is critical; exceptions are routed to a review queue in systems like ServiceNow with full context, and approved corrections feed back into the automated flow. This creates an auditable, closed-loop system that scales without proportional headcount growth.