mlsanityData to 14 Oct 2026

Overcoming the Regulatory Bottleneck via Agent-to-Agent Protocols: A Nuclear Case Study

arXiv 2606.07866 ↗First posted 5 Jun 2026cs.AI cs.MAcited by 0

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Abstract (arXiv)

Regulatory review of advanced nuclear reactor designs routinely spans more than three years and consumes hundreds of millions of dollars in combined regulator and applicant labor. We present the Regulatory Context Protocol (RCP), an Agent-to-Agent communication standard that replaces the formal human-to-human pipeline between regulators and applicants with a structured, auditable agentic channel, while preserving human oversight at safety-significant decision points. The protocol is calibrated against an analysis of 1,236 documents from U.S. Nuclear Regulatory Commission advanced reactor dockets and demonstrated with a working multi-agent pilot. Against an 89M USD, 42-month Reconstructed Baseline, RCP cuts costs by 50-77 percent (21M-44M USD) and timelines by 65 percent (15 months). Without a shared protocol, Standalone Agents reach only 54M-74M USD and 21 months. The residual cost-and-time gap is structural, not algorithmic: it traces to the inter-organizational pipeline that only an agent-to-agent standard can compress. The same bottleneck - formal multi-party review under strict auditability requirements - characterizes pharmaceutical approvals, environmental permitting, financial supervision, and aviation certification. The US regulatory paperwork burden carries a 426.5 billion USD annual opportunity cost; replicated broadly, the projected 50-77 percent reduction implies savings on the order of 210-330 billion USD per year - approaching 1 percent of US GDP.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 40 references that are in the map. First posted on arXiv 5 Jun 2026.

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3 of 40 references.

References · 3

  1. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274
  2. 2025Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcementcited by 17
  3. 2024Integrating LLMs for Explainable Fault Diagnosis in Complex Systemscited by 7

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