From Generation to Attribution: Music AI Agent Architectures for the Post-Streaming Era

2025-10-23

AI Summary

Generative AI is transforming music creation, yet existing streaming platforms suffer from structural deficiencies in attribution, rights management, and revenue distribution—ill-suited for AI-driven, large-scale, fine-grained content production. To address this, we propose the “Fair AI Media Platform,” a post-streaming architectural framework for music AI agents. Our approach introduces a content-based, fine-grained attribution model that decomposes musical works into traceable, quantifiable “block” units. We design BlockDB—a blockchain-based storage system with block-level indexing—and integrate AI agent coordination with real-time attribution event triggering, enabling automated, end-to-end copyright tracking and transparent, instantaneous royalty settlement within the creative workflow. Experimental evaluation demonstrates the framework’s feasibility in supporting collaborative, adaptive music ecosystems under large-scale AI generation. This work provides a deployable technical pathway toward decentralized music economies.

Technology Category

• Multiagent Systems: Mechanism Design
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Machine Learning: Efficient ML / Green AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Generative AI is reshaping music creation, but its rapid growth exposes structural gaps in attribution, rights management, and economic models. Unlike past media shifts, from live performance to recordings, downloads, and streaming, AI transforms the entire lifecycle of music, collapsing boundaries between creation, distribution, and monetization. However, existing streaming systems, with opaque and concentrated royalty flows, are ill-equipped to handle the scale and complexity of AI-driven production. We propose a content-based Music AI Agent architecture that embeds attribution directly into the creative workflow through block-level retrieval and agentic orchestration. Designed for iterative, session-based interaction, the system organizes music into granular components (Blocks) stored in BlockDB; each use triggers an Attribution Layer event for transparent provenance and real-time settlement. This framework reframes AI from a generative tool into infrastructure for a Fair AI Media Platform. By enabling fine-grained attribution, equitable compensation, and participatory engagement, it points toward a post-streaming paradigm where music functions not as a static catalog but as a collaborative and adaptive ecosystem.

Problem

• Addresses attribution gaps in AI-generated music creation
• Tackles opaque royalty systems in streaming for AI production
• Proposes infrastructure for fair compensation in music ecosystem

Innovation

• Content-based Music AI Agent architecture embeds attribution
• Block-level retrieval and agentic orchestration enable transparent provenance
• Granular components with real-time settlement for fair compensation