Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols
This paper introduces an LLM-powered comparative pipeline for large-scale governance discourse analysis of AI agent protocols. It validates the pipeline on two contrasting standards: ERC-8004 (permissionless, on-chain) and Google A2A (corporate-led). Analyzing 4,323 governance participation records, it finds that while governance form influences substantive focus, both regimes exhibit comparable participation inequality and community fragmentation. However, discourse alignment is denser in the permissionless setting, suggesting open governance may foster greater thematic convergence.
[2606.26203] Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols
[Submitted on 24 Jun 2026]
Title:Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols
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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.
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2606.26203 [cs.AI]
(or arXiv:2606.26203v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.26203
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Luyao Zhang [view email] [v1] Wed, 24 Jun 2026 16:34:54 UTC (870 KB)
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