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<p>As AI agents evolve into autonomous economic actors, verifiable and legally binding identity frameworks become critical. This paper presents <i>Ricardian-TEA</i>, a novel architecture combining Triple-Entry Accounting (TEA), Ricardian Contracts, and Distributed Ledger Technology to assign “Legal-Technical Identities” to AI agents. We provide rigorous mathematical foundations: a Ricardian-TEA Integrity Theorem proving that constraint enforcement, non-disputability, and identity binding hold with overwhelming probability under standard cryptographic assumptions, and a Cyber-Chama Convergence Proposition characterising reputation-based trust dynamics. The framework ensures GDPR compliance via Zero-Knowledge Architecture and Crypto-Shredding. Proof-of-concept implementations on Ethereum Sepolia and Bitcoin SV testnets demonstrate chain-agnostic applicability, achieving at worst 1.4&#xa0;s latency per transaction while maintaining 100% auditability of AI transactions.</p>

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Ricardian-TEA: a hybrid framework for assigning legally enforceable identities to autonomous AI agents

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Konstantinos Sgantzos,
  • Massimiliano Ferrara

摘要

As AI agents evolve into autonomous economic actors, verifiable and legally binding identity frameworks become critical. This paper presents Ricardian-TEA, a novel architecture combining Triple-Entry Accounting (TEA), Ricardian Contracts, and Distributed Ledger Technology to assign “Legal-Technical Identities” to AI agents. We provide rigorous mathematical foundations: a Ricardian-TEA Integrity Theorem proving that constraint enforcement, non-disputability, and identity binding hold with overwhelming probability under standard cryptographic assumptions, and a Cyber-Chama Convergence Proposition characterising reputation-based trust dynamics. The framework ensures GDPR compliance via Zero-Knowledge Architecture and Crypto-Shredding. Proof-of-concept implementations on Ethereum Sepolia and Bitcoin SV testnets demonstrate chain-agnostic applicability, achieving at worst 1.4 s latency per transaction while maintaining 100% auditability of AI transactions.