Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols

2026-06-24

AI Summary

This study addresses the empirical gap in understanding power distribution and participation patterns within interoperability standard-setting processes for AI agent protocols. It introduces, for the first time, a large language model–driven mixed-methods framework that integrates automated text annotation, BERTopic neural topic modeling, and multilayer network analysis to conduct a large-scale comparative examination of governance mechanisms led by decentralized autonomous organizations (DAOs) and corporations. Analysis of 4,323 governance records reveals that, although governance form shapes issue focus, both models exhibit pronounced participation inequality. Notably, in permissionless environments, discursive alignment is more densely clustered, suggesting that open governance may foster greater thematic consensus. The findings offer novel empirical evidence and a methodological framework for assessing the institutional design effects in AI governance.

Abstract

As AI agent protocols proliferate, the governance structures shaping their interoperability standards remain empirically underexamined. We introduce an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automated annotation, neural topic modeling, and multi-layer network analysis to study socio-technical power structures at scale. We validate it on two contrasting standards for agent interoperability: ERC-8004 (permissionless, on-chain) and Google A2A (corporate-led). Analyzing 4,323 governance participation records, we combine LLM-assisted coding, topic modeling, and multi-layer network analysis to examine how institutional design shapes thematic priorities and community structure. We find that while governance form influences substantive focus, both regimes exhibit comparable levels of participation inequality and community fragmentation. Discourse alignment is denser in the permissionless setting, suggesting that open governance may foster greater thematic convergence despite decentralized participation. These findings illustrate how LLM-assisted methods can advance the empirical study of technology governance, with implications for designing more equitable agentic AI standards. All data and code are openly available.

Problem

• AI agent protocols
• governance structures
• interoperability standards
• DAO
• corporate AI

Innovation

• LLM-powered pipeline
• comparative governance analysis
• agent interoperability standards
• multi-layer network analysis
• neural topic modeling