LLM-Powered Pipeline Analyzes AI Protocol Governance Structures.

As AI agent protocols become more prevalent, understanding the governance structures that shape their interoperability standards is crucial but often underexamined. This research presents an innovative LLM-powered pipeline designed for large-scale comparative analysis of governance discourse. The pipeline integrates automated annotation, neural topic modeling, and multi-layer network analysis to uncover socio-technical power structures. It was validated by comparing two distinct AI agent interoperability standards: the permissionless, on-chain ERC-8004 and the corporate-led Google A2A. Analyzing over 4,300 governance participation records, the study found that while the form of governance influences thematic priorities, both decentralized and corporate regimes exhibited similar levels of participation inequality and community fragmentation. Interestingly, discourse alignment was denser in the permissionless setting, suggesting open governance might foster greater thematic convergence despite decentralized participation. This work highlights the utility of LLM-assisted methods for empirical technology governance studies.