A Technical Architecture for Federated Governance of Agentic AI Systems: Integrating NIST AI RMF, ISO/IEC 42001, and MCP/A2A Protocols for Interoperable Oversight

A Technical Architecture for Federated Governance of Agentic AI Systems: Integrating NIST AI RMF, ISO/IEC 42001, and MCP/A2A Protocols for Interoperable Oversight | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 February 2026 V1 Latest version Share on A Technical Architecture for Federated Governance of Agentic AI Systems: Integrating NIST AI RMF, ISO/IEC 42001, and MCP/A2A Protocols for Interoperable Oversight Author : Satyadhar Joshi 0009-0002-6011-5080 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177187616.63699099/v1 298 views 172 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The proliferation of autonomous agentic AI systems across distributed computing environments exposes a critical gap between high-level governance principles and their technical operationalization. Existing standards-including NIST AI Risk Management Framework 1.0 and ISO/IEC 42001:2023-articulate organizational controls but provide insufficient technical specification for runtime policy enforcement in multi-agent systems. This paper conducts a systematic review of the literature on technical architectures for federated AI governance, analyzing three interdependent architectural concerns: (1) machine-readable policy representations that translate normative controls from ISO/IEC 42001, NIST AI RMF, and IEEE 7000 into executable rule sets; (2) interoperability layers utilizing Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols as substrates for policy-aware inter-agent communication; and (3) distributed audit subsystems employing cryptographic primitives for tamperevident logging of autonomous decisions. We critically analyze proposed Compliance API specifications, examine audit data schemas from the literature, and survey domain-specific reference architectures for healthcare (HL7 FHIR integration) and financial services (SR 11-7 alignment). We synthesize reported performance characteristics and identify unresolved research challenges including policy expressiveness trade-offs, cross-domain policy composition, and formal verification of agent behavior compliance. This review identifies open research problems and articulates requirements for future empirical evaluation of governance architectures. Supplementary Material File (techarxiv_v2.pdf) Download 121.02 KB Information & Authors Information Version history V1 Version 1 23 February 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords agentic ai ai governance distributed audit interoperability iso/iec 42001 multi-agent systems reference architecture Authors Affiliations Satyadhar Joshi 0009-0002-6011-5080 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 298 views 172 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Satyadhar Joshi. A Technical Architecture for Federated Governance of Agentic AI Systems: Integrating NIST AI RMF, ISO/IEC 42001, and MCP/A2A Protocols for Interoperable Oversight. Authorea . 23 February 2026. DOI: https://doi.org/10.22541/au.177187616.63699099/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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