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

This study introduces an LLM-powered pipeline to analyze governance discourse in AI agent interoperability standards. The authors compare two contrasting models: ERC-8004 (a permissionless, on-chain DAO process) and Google A2A (a corporate-led, Linux Foundation-governed protocol). By processing 4,323 governance records, the researchers integrated automated LLM-assisted annotation, neural topic modeling (BERTopic and Thematic-LM), and multi-layer network analysis to map how institutional design influences community participation and thematic priorities.

The research reveals that governance form fundamentally shapes the focus of deliberation. DAO-based governance (ERC-8004) concentrates discourse on security mechanisms and protocol principles, reflecting its permissionless, rough-consensus nature. In contrast, corporate governance (A2A) distributes deliberation across a wider array of engineering-execution workstreams, carrying a higher procedural coordination burden. Despite these thematic differences, both regimes suffer from comparable levels of participation inequality, where a small subset of actors dominates the discourse, and community fragmentation, where participants often operate in disconnected silos.

As autonomous AI agents begin to coordinate across organizational boundaries, the rules governing their interoperability will define the power structures of future AI infrastructure. This study demonstrates that simply adopting a DAO structure does not automatically guarantee higher decentralization or broader participation. By providing a computational lens to evaluate these governance "black boxes," the authors offer a framework for designers to build more equitable and transparent standards for AI agentic communication.