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

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Publisher: SMPTE — Pasadena, CA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 00, pp. 1–12
Abstract: 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.
Publication Date: 2025-10-13
ISBN: Electronic: 978-1-61482-966-9
Author(s): Tejaswi Mulagada
Keyword(s): ST 2110, Multi-Agent AI, Broadcast Automation, Metadata Tracking, Queue Buffer Management, Device Configuration, Large Language Models (LLMs), IP Media Workflows, Anomaly Detection, NMOS Interoperability
Copyright: © 2025 SMPTE

Bibliographic Reference(s)

SMPTE, “ST 2110 Professional Media Over Managed IP Networks,” https://www.smpte.org/standards/st2110, 2017. EXTERNAL
SMPTE, ER 1010:2023, “Artificial Intelligence (AI) and Machine Learning (ML) in Media Production,” Society of Motion Picture and Television Engineers: White Plains, N.Y., 2023. EXTERNAL
OpenAI, “A Practical Guide to Building Agents,” Available: https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf, 2023. EXTERNAL
NewscastStudio Staff, “Industry Insights: The State of AI in Broadcasting and Production,” NewscastStudio, Feb. 6, 2025, https://www.newscaststudio.com/2025/02/06/industry-insights-the-state-of-ai-in-broadcasting-and-production/ EXTERNAL
European Parliament and Council, “Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act),” Official Journal of the European Union, July 12, 2024, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEx%3A32024R1689 EXTERNAL
IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, “Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, Second Edition,” IEEE: Piscataway, N.J., 2023, Available: https://standards.ieee.org/wp-content/uploads/2023/06/EAD2e.pdf EXTERNAL
C. Lapp, “Busting the Buffer Myth for Multicast Media Over IP,” Cisco Blogs, Available: https://blogs.cisco.com/sp/busting-the-buffer-myth-for-multicast-media-over-ip EXTERNAL
Intel, “SMPTE ST 2110 FPGA-based NIC Solution Brief,” Intel, 2022. Available: https://www.intel.com/content/dam/www/central-libraries/us/en/documents/2022–09/smpte-2110-fpga-based-nic-solution-brief.pdf EXTERNAL
M. Y. Khan, “Anomaly Detection in Key-Management Activities Using Metadata: A Case Study and Framework,” ResearchGate, 2024. EXTERNAL
SuperAGI, “Self-Healing AI Agents vs Traditional Monitoring Systems: A Comparative Analysis of Efficiency and Cost,” 2024. Available: https://superagi.com/self-healing-ai-agents-vs-traditional-monitoring-systems-a-comparative-analysis-of-efficiency-and-cost/ EXTERNAL
ns-3 Documentation, https://www.nsnam.org/docs/release/3.38/manual/html/index.html.2023 EXTERNAL

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Tejaswi Mulagada; Leveraging Multi-Agent AI Systems for SMPTE ST 2110 Broadcast Automation, MTS 2025, Article 6 (pp. 1 to 12); SMPTE, 2025, ISBN: 978-1-61482-966-9

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<span class="citation">Tejaswi Mulagada; <cite>Leveraging Multi-Agent AI Systems for SMPTE ST 2110 Broadcast Automation</cite>, MTS 2025, Article 6 (pp. 1 to 12); SMPTE, 2025, ISBN: 978-1-61482-966-9</span>

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Tejaswi Mulagada; Leveraging Multi-Agent AI Systems for SMPTE ST 2110 Broadcast Automation, MTS 2025, Article 6 (pp. 1 to 12); SMPTE, 2025, ISBN: 978-1-61482-966-9

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Tejaswi Mulagada; <cite id="bib-978-1-61482-966-9-6">Leveraging Multi-Agent AI Systems for SMPTE ST 2110 Broadcast Automation</cite>, MTS 2025, Article 6 (pp. 1 to 12); SMPTE, 2025, ISBN: 978-1-61482-966-9
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