Agentic System for Broadband Internet Service Outage Resolution and Credit Issuance

The workflow's core savings come from intelligent first-call resolution. An AI agent instantly cross-references a customer's reported address with network management system (NMS) alarms and smart modem pings. By autonomously confirming a network-side outage or identifying a premise-specific issue, it prevents unnecessary dispatches. For a large ISP, eliminating even a fraction of false dispatches saves millions annually in technician labor, fuel, and vehicle maintenance.

Automating the initial diagnostic and communication loop removes the most repetitive segment of an outage call. The agent handles identification, provides instant status from the NMS, sets accurate restoration expectations, and initiates proactive SMS updates. This deflects calls entirely or reduces average handle time from 8-10 minutes to under 4 minutes, allowing human agents to focus on complex technical support or escalations, effectively increasing agent capacity without hiring.

Upon restoration, the system automatically applies credit policies based on confirmed outage duration, customer tier, and regulatory rules (e.g., FCC's "Your Rights, Our Responsibilities"). It directly interfaces with the billing system (e.g., Oracle BRM, Salesforce Billing) to post credits, generate notifications, and maintain an audit trail. This eliminates 100% of manual claim forms, back-office processing, and the associated labor cost, while ensuring policy consistency and compliance.

Proactive, accurate communication is the primary driver of satisfaction during outages. The workflow provides instant acknowledgment, realistic ETAs, and automated progress updates via the customer's preferred channel. By resolving the credit issuance automatically post-restoration, it delivers a positive 'make-good' experience without the customer having to ask. This directly improves CSAT scores for outage-related interactions and reduces churn intent among affected customers.

The system enforces consistent application of mandatory service credit policies (e.g., for outages exceeding 24 hours). By automating the entire detection, duration calculation, and credit application process, it creates a defensible, auditable record that proves compliance with state PUC or FCC guidelines. This reduces the legal and financial risk of customer disputes, class-action lawsuits, or fines for failing to meet published service level agreements.

The unified data layer built for the agents—correlating customer reports, NMS events, modem data, and restoration times—becomes a powerful analytics asset. It identifies chronic trouble spots, correlates specific network equipment with frequent outages, and quantifies the customer and financial impact of each event. This data-driven intelligence allows network engineering teams to prioritize capital expenditure (CAPEX) on the infrastructure upgrades that will yield the highest reliability and ROI.

This agent is the workflow's trigger. It ingests a customer report (via IVR, chat, or portal) and cross-references the address against real-time SCADA alarms, smart meter last-gasp signals (MDMS), and live outage maps (OMS). Its role is to confirm the outage, differentiate between a meter failure and a grid fault, and classify the incident's likely cause (e.g., vegetation, equipment). This prevents unnecessary truck rolls, saving ~$500-$1,000 per avoided dispatch.

Upon validation, this agent takes over customer interaction. It retrieves an Estimated Restoration Time (ERT) from the OMS, segments the customer (e.g., critical care, business), and initiates proactive, multi-channel updates via SMS, IVR, or email. It manages the entire communication lifecycle, reducing inbound call volume by 30-40% during major events and providing a single source of truth for customers.

This is the financial automation layer. It monitors the confirmed outage duration from the OMS and applies pre-programmed business rules and regulatory mandates (e.g., California's Rule 14) to determine credit eligibility and amount. It then autonomously generates the credit transaction, posts it to the Customer Information System (CIS), and triggers a notification to the customer. This eliminates manual claim processing and ensures consistent, auditable policy application.

For outages requiring repair, this agent orchestrates the physical response. It queries the workforce management system for crew availability, skills, and location. Using the fault location from the diagnosis agent, it creates an optimized work order, dispatches the nearest appropriate crew, and provides dynamic ETAs back to the communication agent. This reduces dispatch latency and optimizes crew travel, improving restoration speed.

After service is restored, this agent manages the quality and learning cycle. It contacts the customer for a sentiment-tagged satisfaction survey, analyzes feedback for systemic issues (e.g., repeated poor ETAs), and routes negative cases for human follow-up. It also compiles key metrics (CAIDI, CSAT) for autonomous internal reporting. This creates a closed-loop system that continuously improves the triage process.

Not an agent, but the critical middleware that enables the workflow. This is a custom-built virtual data hub that unifies APIs and data models from CIS, OMS, MDMS, GIS, and workforce systems. It provides a single, low-latency query interface for all agents, handles legacy system integration via RPA wrappers where APIs are lacking, and maintains conversation state for seamless handoffs. This architecture is the foundation for a scalable, maintainable agentic system.

The primary source of truth for grid health. Agents query these systems in real-time to validate outage reports against live breaker status, voltage readings, and fault indicators. This prevents false dispatches and provides the technical baseline for estimating restoration scope. Integration typically requires API gateways or direct ODBC/JDBC connections to legacy SCADA historians.

Critical for granular, premise-level diagnosis. Agents poll smart meter 'last-gasp' and heartbeat signals to confirm loss of service at the specific address, differentiating between a meter failure and a grid outage. This data is also used post-restoration to calculate exact outage duration for automated credit issuance, pulling interval data to comply with regulatory credit policies.

The system of record for customer identity, account status, and financial transactions. Agents read customer profiles for communication preferences and critical care status. Upon confirmed restoration, they write credit transactions directly into the billing system, applying the correct tariff-based or regulatory-mandated amount. This requires a robust, auditable API layer with idempotency controls to prevent duplicate credits.

Provides the spatial and logistical model of the grid. Agents use OMS/GIS to map reported outages to specific circuits, predict affected customer counts, and generate accurate Estimated Restoration Times (ERTs) by analyzing crew locations and repair complexity. This integration feeds the public outage map and enables proactive notifications to customers in the predicted path of cascading faults.

The execution layer for customer-facing interactions. Orchestration agents format personalized messages (status updates, credit confirmations) and dispatch them via telecom and email APIs based on customer channel preference and outage phase. This requires integration with high-throughput, carrier-grade platforms to manage surge volumes during storm events without degradation.

Enables closed-loop coordination with field crews. Agents can create dispatch tickets, assign them to the nearest available crew based on real-time location feeds, and push job details to technicians' mobile devices. Post-repair, the system provides the final restoration timestamp, triggering the credit workflow. Integration often uses REST APIs or middleware like MuleSoft to bridge scheduling and field execution systems.