Multi-Agent Automation of Proactive Customer Outreach for Tariff Optimization

This workflow automates the identification and conversion of customers onto cost-optimal utility tariffs, directly impacting customer satisfaction and operational margin. It eliminates the manual analysis of usage profiles against complex, evolving rate structures—a task that is too granular for human teams at scale. The operational upside comes from increased uptake of higher-margin or grid-optimizing rate plans, reduced churn risk through demonstrated savings, and the deflection of reactive billing inquiries.

Implementation integrates with Meter Data Management (MDMS) and Customer Information Systems (CIS) like Oracle CC&B or SAP for data ingestion. The orchestration layer, built with LangGraph, coordinates specialized agents for analysis, communication, and execution. Critical controls include regulatory compliance checks within the rule engine, approval gates for exception routing, and full audit trails for outreach and plan changes. Rollout requires phased testing by customer segment and close monitoring of conversion rates and system load.

This workflow automates the costly, manual process of analyzing customer usage against complex rate plans and executing outreach. It eliminates the operational bottleneck of analysts manually running queries and crafting communications, creating savings through labor leverage and increased conversion of customers to higher-margin or more efficient tariffs. The architecture must integrate with the Customer Information System (CIS), meter data management platforms, and communication APIs to function.

Implementation centers on a LangGraph or custom orchestrator managing three specialized agents. The Analysis Agent queries historical usage, applies rate logic, and flags savings opportunities. The Outreach Agent personalizes messages using retrieved account context. A mandatory human review gate for high-value segments provides control. The system logs all actions for audit and integrates feedback on conversion rates to retune candidate selection, ensuring continuous ROI improvement and compliance with communication regulations.

Phase 1 establishes the core data pipeline and analysis engine. We integrate with the Customer Information System (CIS) and Meter Data Management System (MDMS) to ingest historical usage profiles. An orchestration agent, built on LangGraph, triggers monthly batch analyses, comparing each customer's consumption against all available rate plans using utility-specific pricing models. This identifies the cohort with significant savings potential, typically 5-15% of the customer base, creating the initial target list for outreach and quantifying the business case.

Phase 2 operationalizes the outreach loop with controls. The outreach agent, using templated but personalized messaging, dispatches communications via the utility's CRM (e.g., Salesforce Marketing Cloud). It manages opt-outs, tracks engagement, and for interested customers, can initiate a guided, API-driven tariff change process within the CIS, requiring final customer confirmation. Implementation includes governance gates for message approval, exception routing for complex cases to human agents, and observability dashboards to track adoption rates, savings delivered, and program ROI.

A governed rollout begins with a pilot targeting a low-risk, high-savings customer segment, using a subset of rate plans. The core control is a pre-flight approval gate where the orchestrator agent submits its outreach list—including identified savings, recommended tariff, and proposed communication—to a human-in-the-loop dashboard for batch review and sign-off. This gate ensures compliance with state utility commission marketing rules and internal brand guidelines before any customer contact is initiated, mitigating regulatory risk from the outset.

Post-approval, the execution agent manages the outreach via integrated comms APIs, while a separate response agent handles inbound replies, escalating complex queries. A continuous compliance monitor audits all agent decisions and customer interactions against a rules engine, flagging anomalies for review. Phased expansion proceeds by gradually increasing the customer segment size, complexity of tariff analysis, and autonomy of the approval gate, measured by reduction in manual review time and increase in successful, complaint-free customer migrations.