AI Agentic Workflow for Subscriber Identity Across Mobile, Home, and IoT

A unified identity graph eliminates duplicate marketing spends and mis-targeted campaigns across siloed product lines (mobile, fiber, TV). By resolving a subscriber's devices and services to a single profile, marketing can target the right household with the right bundle offer, improving conversion rates and lowering cost per acquisition. Implementation requires integrating CRM, billing (e.g., SAP BRIM), and digital engagement data into a real-time matching service.

With a holistic view of a subscriber's services and usage, AI agents can identify and trigger personalized upsell opportunities—like adding a connected security camera to an existing fiber plan or upgrading a mobile line to a family data share. This moves upsell from reactive, call-center-driven processes to automated, event-triggered orchestration, directly impacting ARPU. The workflow integrates with product catalogs and real-time offer management platforms.

When a customer calls, support agents currently toggle between 3-5 systems to see mobile usage, home network status, and IoT device connections. A unified identity profile served to the CRM or ServiceNow interface gives agents a single pane of glass, eliminating 2-4 minutes of search time per call. The architecture requires a low-latency API layer that queries the central identity graph and federates data from network OSS and IoT platforms.

Fragmented service experiences are a primary driver of churn. A resolved identity enables proactive care: an AI agent can detect a subscriber experiencing poor mobile data and intermittent home Wi-Fi, infer a geographic network issue, and trigger a proactive credit or outreach before they consider switching. This predictive retention, powered by correlated signals, directly protects revenue. Implementation involves building a monitoring agent that analyzes the unified profile against churn models.

Manual stitching of subscriber data across billing, network authentication (AAA logs), and IoT platforms is a constant, resource-intensive task for data teams. An automated agentic workflow with deterministic and probabilistic matching rules runs continuously, maintaining the golden record. This turns a fixed cost center into a scalable, self-healing data asset, freeing engineers for higher-value work. The build uses a workflow orchestrator like LangGraph to manage matching, merging, and conflict resolution agents.

Launching a new IoT or bundled service today requires building new data pipelines to understand customer eligibility and adoption—a multi-month project. With a pre-built, unified identity graph, product teams can immediately query addressable households and their existing services, shrinking time-to-market. The economic impact is faster revenue capture and lower GTM cost. This requires designing the identity workflow as a reusable platform service with clean APIs for product and marketing systems.

This agent orchestrates the continuous, real-time ingestion of raw signals from billing systems (e.g., SAP), network authentication logs, IoT platforms, and CRM. It normalizes disparate data formats (JSON, XML, CDRs) into a unified schema, handling schema drift and data quality exceptions by routing incomplete records to a validation queue. This automation eliminates the manual ETL work that delays profile updates and creates data silos.

The core matching logic uses a graph-based model to probabilistically link device IDs, account numbers, IP addresses, and behavioral patterns. It applies deterministic rules (e.g., shared login) first, then fuzzy matching for harder cases, scoring confidence for each link. This component automates the manual detective work of analysts trying to stitch customer journeys, directly improving match accuracy for bundle targeting and support.

A critical control layer that intercepts all profile writes and queries to enforce GDPR/CCPA consent flags and internal data governance policies. It automatically suppresses or anonymizes data for opted-out subscribers and logs all access for audit trails. This automates compliance risk management, preventing manual errors that could lead to regulatory fines and brand damage.

A scheduled agent that continuously scans the unified identity graph for gaps, duplicates, or decaying attributes. It triggers automated enrichment from third-party sources, proposes profile merges, and assigns a 'health score.' Exceptions and low-confidence merges are routed to a human review queue in ServiceNow. This automates the ongoing data hygiene that typically consumes 15-20% of a data team's time.

This component listens for high-confidence profile updates and routes the enriched identity to downstream systems with sub-second latency. It formats payloads for APIs of campaign tools (Salesforce Marketing Cloud), support dashboards (ServiceNow), and recommendation engines. This automation closes the loop between data unification and operational action, enabling immediate personalization that manual batch updates cannot support.

Built on a framework like LangGraph, this layer coordinates the entire multi-agent workflow, managing state, retries, and error handling. It provides a centralized dashboard for monitoring pipeline health, match rates, and agent performance. This architectural component automates the operational oversight, reducing mean-time-to-resolution (MTTR) for failures by 80% compared to manual log digging.