Leveraging Multi-Agent AI Systems for SMPTE ST 2110 Broadcast Automation

This paper proposes a multi-agent AI system to automate key tasks in SMPTE ST 2110 IP-based media workflows, focusing on real-time metadata tracking, dynamic queue buffering, and device configuration management. Orchestrated by a large language model (LLM), the system integrates specialized agents to improve efficiency, reduce errors, and ensure standards compliance, with human-in-the-loop safeguards for ethical alignment. Evaluations, conducted via network simulations using tools like ns-3 and Python-based emulators, demonstrate median latency reductions of 25-30% compared to FIFO baselines, false-positive rates of 2-5% for metadata anomaly detection, and recovery rates of 95-98% for configuration errors. These results are derived from synthetic datasets mimicking UHD video streams and benchmarked against traditional SNMP systems and modern reinforcement learning (RL) approaches. Developed as an original framework by the author, this system leverages collaborations for potential pilot testing and establishes a foundation for explainable AI in broadcast environments, with extensions to domains like live sports production.

Published

2025-10-13

Content type

Original Research

Keywords

smpte st 2110, multi-agent ai, broadcast automation, metadata tracking, queue buffer management, device configuration, large language model, ip media workflows, anomaly detection, nmos interoperability

ISBN

978-1-61482-966-9