Abstract
The process of ingesting and cataloging large volumes of digital media can be inefficient and difficult to scale, often relying on manual workflows that may be slow and error-prone. A multi-agent artificial intelligence framework may be used for automated media content ingestion and metadata enrichment. Such a system can comprise a central orchestrator agent that analyzes incoming media and delegates processing tasks to multiple specialist agents that operate in parallel. A confidence-based routing mechanism can assign a score to generated metadata, where high-confidence results may be automatically committed and low-confidence results can be directed to a human-in-the-loop review process. This automated, modular approach may improve the speed, accuracy, and consistency of cataloging large volumes of diverse digital media assets.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Nayak, Shravan, "The Multi-Agent AI System for Automated Media Ingestion and Metadata Enrichment", Technical Disclosure Commons, (August 20, 2026)
https://www.tdcommons.org/dpubs_series/11451