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

TL;DR

This paper introduces a novel Music AI Agent architecture that embeds attribution into the creative process, enabling transparent provenance, real-time settlement, and fostering a participatory, ecosystem-based approach in the post-streaming era.

Contribution

It proposes a content-based, granular attribution system integrated into AI music creation, addressing rights and economic challenges of AI-driven music production.

Findings

Enables fine-grained, block-level attribution for AI-generated music

Supports transparent provenance and real-time settlement processes

Reframes AI as infrastructure for a participatory music ecosystem

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.