AI Agentic Workflow for Unified Viewer Profiles Across Devices

The fragmented viewer problem directly impacts subscription retention and ad yield. When a household's streaming activity is split across a smart TV, a tablet, and a mobile browser, you cannot accurately measure total engagement, predict churn, or serve relevant content. This siloing forces manual data stitching, delays campaign decisions, and degrades personalization, costing media operators in missed upsell opportunities and preventable subscriber loss. A unified profile is the operational prerequisite for modern media economics.

Implementing this workflow requires integrating with device graph providers like LiveRamp, CMS platforms like Brightcove, and session data from CDNs. The orchestrator, built with LangGraph, manages agents for matching, conflict resolution, and consent checks. Controls are critical: low-confidence matches route to a human review dashboard, and all graph updates are logged for GDPR compliance. The result is a real-time, actionable viewer profile that drives automated personalization and retention actions.

This workflow automates the manual data engineering required to reconcile fragmented viewer sessions across devices, a critical bottleneck for personalization and retention. The operational upside comes from eliminating batch-based reconciliation, which delays campaign measurement and degrades recommendation accuracy. By unifying signals in real time, media companies can improve targeting quality, reduce subscriber churn, and increase content engagement, directly impacting lifetime value and advertising yield.

Implementation requires a graph database (e.g., Neo4j) as the persistent profile store, with orchestration logic managed via LangGraph. The Identity Resolution Agent applies probabilistic matching using keys from providers like LiveRamp, while a separate Governance Agent enforces consent flags from a preference hub. Observability is built in via the audit log, and low-confidence matches are routed to a human review interface in tools like ServiceNow before profile updates are committed, ensuring accuracy and compliance.

The operational bottleneck is fragmented session data trapped in device-specific silos, forcing manual stitching for basic personalization and churn analysis. Automating this unification directly improves targeting accuracy and campaign efficiency, reducing viewer acquisition costs and increasing lifetime value. The architecture must integrate with device graph providers (e.g., LiveRamp) and content management systems (e.g., Brightcove, Mux) to ingest and resolve signals in real time, governed by consent management platforms.

Implementation begins with a pilot on web and mobile app data, establishing the matching logic and observability layer before introducing complex TV signals. The core orchestration, built on LangGraph, manages specialized agents for ingestion, probabilistic matching, and conflict resolution. Critical controls include automated confidence scoring to route low-probability matches for human review, continuous monitoring for match-rate drift, and immutable audit logs for all profile merges to ensure governance and explainability.

Operationalizing a unified viewer profile workflow demands robust governance to manage consent, data quality, and match accuracy. The architecture must embed controls at each stage: identity signals from smart TVs, mobile apps, and web sessions are ingested through validated APIs, scrubbed for PII compliance, and passed to a probabilistic matching engine. A rules engine applies business logic for match confidence thresholds and consent flags before any profile merge is committed. This prevents erroneous linkages and ensures GDPR/CCPA adherence, turning a complex data operation into a governed, automated system.

A phased rollout mitigates implementation risk. Phase 1 targets deterministic matches (logged-in users) to validate the core data pipeline and matching logic within a single content domain. Phase 2 introduces probabilistic matching for anonymous sessions, initially in shadow mode to compare AI-proposed profiles against ground truth before activating live updates. Phase 3 scales the workflow to additional device types and integrates with downstream systems like recommendation engines and churn models. Each phase includes defined KPIs for match accuracy, system latency, and exception rates, ensuring the workflow delivers measurable improvements in targeting quality and operational leverage before full deployment.