The secondary use of broadcast archive assets is essential for maximizing content value. However, a significant barrier is the high workload of manually logging rights information. This process is not only labor-intensive but also suffers from a fundamental lack of governance.

Existing archive rights records often suffer from inconsistent quality due to operator-dependent data entry methods. Without clear oversight or standardized processes, this human-maintained database contains redundant entries, fragmented information, and ambiguous data. This lack of governance creates assets with unclear rights status, preventing their reuse and leaving valuable content locked away.

To solve this critical challenge, we report on GEAR (Governance Engine for Archives Rights), a multi-AI agent system designed not just to automate, but to introduce essential governance into the rights logging workflow.

The system employs a Human-in-the-Loop (HITL) approach, balancing AI-driven efficiency with the critical accuracy required for broadcast operations. GEAR’s architecture is built on core AI agent functions that enforce governance:

Task Orchestration Agent: Acts as the central controller, managing information from the front-end and other agents. It enforces standardized workflows by routing requests to the appropriate agent and managing sessions to ensure complex processes are handled reliably.

Rights Detection Agent: Utilizes a Large Multimodal Model (LMM) to analyze video content, identifying potential copyrighted materials with timestamps. It then queries the existing, human-maintained rights database via an MCP (Model Context Protocol) server to cross-reference the detected items. It searches for any associated contact/liaison information already logged by operators and scores these potential matches for relevance.

Output Formatting Agent: Processes the raw, scored data retrieved from the database. It enforces data quality by filtering, de-duplicating, and standardizing the often-inconsistent information, ensuring that all data presented to the operator meets a consistent quality standard before review.

Content-Rights Linking Agent: This agent facilitates the crucial HITL validation, which acts as the final governance checkpoint. It presents formatted data to the operator via a checklist for final “adoption.” Once confirmed, this agent creates an link between the content and its verified rights in the archive database.

By introducing GEAR, we transform rights logging from a manual, inconsistent task into a governed, standardized, and efficient workflow. This system drastically reduces the logging burden while simultaneously enhancing data integrity, consistency, and reliability. This presentation will detail the GEAR architecture, its assistive agentic workflows, and the impact of AI-driven governance on maximizing the true value of broadcast archives.