Governance Gaps in Agent Interoperability Protocols: What MCP, A2A, and ACP Cannot Express

2025-11-25 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, long

Summary

A systematic gap analysis reveals that current agent interoperability protocols, including MCP v1.1, A2A v1.0.1, ACP, ANP, and ERC-8004, lack essential primitives for enterprise-grade agent community governance. These five protocols, developed by entities like Anthropic (late 2024), Google (2026), IBM Research, and an Ethereum Improvement Proposal (August 2025), primarily address coordination concerns such as identity, capability discovery, tool access, message exchange, and reputation. However, when evaluated against a six-dimension governance taxonomy—membership, deliberation, voting, dissent preservation, human escalation, and audit/replay—the analysis shows universal absence of voting and dissent preservation capabilities. Deliberation is also largely absent or partial. The study concludes that agent community governance necessitates a new architectural layer above existing standards, rather than mere extensions to current protocols, which are designed for task-oriented coordination.

Key takeaway

For AI Architects and MLOps Engineers deploying autonomous agent fleets in regulated or critical enterprise environments, recognize that current interoperability protocols like MCP and A2A lack built-in governance capabilities. You must design and implement an explicit governance layer above these protocols to manage collective decision-making, including mechanisms for voting, dissent preservation, and human escalation. Relying solely on existing coordination protocols for complex, multi-agent decisions introduces significant compliance and operational risks.

Key insights

Current agent interoperability protocols are insufficient for enterprise-level governance, requiring a new architectural layer.

Principles

• Coordination differs fundamentally from governance.
• Governance requires specific protocol primitives.
• Extensibility cannot solve structural gaps.

Method

A systematic gap analysis applied a six-dimension governance taxonomy (membership, deliberation, voting, dissent preservation, human escalation, audit/replay) to five agent protocols, classifying capabilities as Supported, Partial, or Absent.

In practice

• Implement human escalation for critical agent decisions.
• Design audit trails for all agent collective actions.
• Prioritize membership and deliberation primitives.

Topics

• Agent Interoperability Protocols
• Multi-Agent Systems
• AI Governance
• Enterprise AI
• Protocol Design
• Decentralized Identifiers

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Architect, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.