AI Agentic Workflow for Linking Network QoS Metrics to Customer Proactive Care

This foundational agent continuously ingests raw, high-volume metrics from RAN, core, and transport layers (e.g., RSRP, latency, packet loss, jitter) via streaming APIs from systems like Ericsson OSS or Cisco NSO. It normalizes data into a unified customer-impact schema, mapping cell-level KPIs to individual subscriber sessions and service plans. This creates the clean, queryable data layer essential for accurate experience scoring, replacing error-prone manual data pulls from network operations centers.

This analytical agent applies rule-based and ML models to the normalized telemetry. It maps technical KPIs to perceived customer experience (e.g., 'video buffering likely', 'dropped call risk high') and flags statistically significant deviations from individual or segment baselines. The agent outputs a real-time stream of customer-level experience scores and anomaly alerts, which become the primary triggers for the proactive care workflow, moving from network-centric monitoring to customer-centric insight.

The workflow's business logic core. This agent evaluates each anomaly against the customer's value segment, current plan, service history, and available commercial actions. Using a configurable rules engine (potentially built with LangGraph), it decides on an intervention type—such as a speed boost, a network optimization ticket, or a personalized plan-upgrade offer. It checks eligibility, simulates margin impact, and, for certain actions, seeks approval via a human-in-the-loop gateway before proceeding.

This integration agent is responsible for the secure, auditable execution of decisions. It possesses the credentials and API logic to make atomic updates across disparate enterprise systems: applying a temporary speed boost via the policy control function (PCF), creating a trouble ticket in ServiceNow for network optimization, generating a discount code in the billing system (e.g., Oracle BRM), or queuing a personalized outbound message in the marketing automation platform (e.g., Salesforce Marketing Cloud). It handles retries, logs all actions, and reports status back to the orchestration layer.

For interventions requiring customer interaction, this agent generates and delivers context-aware, compliant communications. It crafts proactive SMS or in-app notifications explaining a detected issue and the applied fix (e.g., 'We noticed slow speeds in your area and have optimized your connection'). For offer scenarios, it can power a conversational interface in chat or IVR to present the offer, handle simple Q&A, and capture acceptance, ensuring a seamless handoff to the execution agent for fulfillment.

This monitoring agent ensures the entire automated loop is auditable, explainable, and tunable. It tracks key performance indicators (e.g., anomalies detected vs. interventions triggered, offer acceptance rates, subsequent churn reduction) in a real-time dashboard. It logs every agent's decision rationale for regulatory and operational review, and can be configured to flag edge cases for human analyst review. This agent is critical for maintaining control, measuring ROI, and continuously refining the business logic based on outcomes.

This workflow automates the manual, siloed analysis of network performance data and its translation into customer-impact insights. By linking network degradation directly to at-risk accounts, it shifts operations from costly reactive support to proactive, revenue-preserving interventions. The primary savings come from reducing churn driven by poor service perception and lowering inbound complaint volume. The upside is captured through targeted upsell campaigns (e.g., speed upgrades, premium support) triggered by network improvement opportunities, directly improving customer lifetime value.

The architecture is built on a streaming data pipeline. 1. Ingestion Layer: Pulls real-time QoS metrics (latency, jitter, packet loss) from network OSS (e.g., Ericsson, Nokia) and correlates them with customer location data from the CRM. 2. Inference Agent: A dedicated AI agent maps raw KPIs to probable customer experience (e.g., 'buffering during peak hours'). It uses a rules engine and ML model to score impact severity. 3. Decision Orchestrator: This component, often built with LangGraph or Temporal, evaluates the impact score against customer value, plan type, and service history. It decides the action: proactive credit, care outreach, or marketing offer. 4. Action Dispatcher: Integrates via APIs to execute the decision in the billing system (e.g., SAP BRIM), customer comms platform (e.g., Twilio), and marketing automation (e.g., Salesforce Marketing Cloud).

Phase 1: Pilot on a Controlled Segment. Start with a single network issue type (e.g., peak-hour congestion) and a high-value customer segment. Build the data connector from the Network Management System (NMS) and establish the first mapping rules. Phase 2: Scale the Inference Logic. Expand the QoS-to-experience mapping model using historical trouble ticket data for training. Integrate with the CRM (e.g., Salesforce Service Cloud) to pull full customer context into the decision loop. Phase 3: Automate Full Action Orchestration. Connect the dispatcher to the billing system for automated goodwill credits and to the marketing platform for targeted 'network upgrade' offer campaigns. Implement approval gates for high-value credits or deep discounts. Phase 4: Observability & Optimization. Deploy monitoring for the entire workflow—tracking prediction accuracy, offer acceptance rates, and downstream impact on churn and NPS.

Automation requires strict controls to avoid brand damage and revenue leakage. Approval Gates: Any credit above a pre-defined threshold or any plan downgrade offer must route to a human analyst for approval via a Slack or ServiceNow ticket. Model Governance: The QoS-impact model must be regularly validated against actual customer complaint data to prevent alert fatigue from false positives. Audit Trail: Every automated action—from signal detection to credit issuance—must be logged with a full chain of evidence (QoS metrics, customer profile, decision logic) for compliance and root-cause analysis. Exception Routing: Customers who opt-out of automated communications or whose cases are too complex are automatically routed to a specialized 'proactive care' agent team with full context.

Success requires breaking down operational silos. Network Operations: Provides the QoS data; their incentive is reduced trouble ticket volume and more targeted capital expenditure. Customer Care: Receives pre-qualified, context-rich cases; their incentive is lower handle time, higher first-contact resolution, and a shift from complaint handling to relationship building. Marketing: Gains a hyper-targeted lead list for upsell campaigns based on actual network capability improvements; their incentive is improved campaign conversion and ARPU lift. Finance: Controls the credit approval rules; their incentive is predictable goodwill expense and reduced churn-related revenue loss. The ROI is measured through the blended cost savings (care efficiency, churn avoidance) and new revenue (upgrade conversion).