Cross-Agent Governance Alignment (CAGA): A Zero-Knowledge Formalization for Cross-Organizational AI Governance

As autonomous AI agents increasingly coordinate across institutional boundaries in regulated industries, existing governance frameworks prove insufficient. Current models assume either a single organizational authority or full policy transparency between participants—assumptions that do not hold in multi-stakeholder environments where governance constraints encode proprietary risk tolerances, regulatory interpretations, and competitive strategy. This paper formalizes the Cross-Agent Governance Alignment (CAGA) problem: verifying mutual governance compatibility between agents operating under distinct policy regimes without disclosing those policies. We define governance domains and cross-domain interactions, introduce the governance alignment predicate Φ(Dᵢ, Dⱼ, τ), and formalize the problem under an honest-but-curious threat model appropriate for regulated settings. We identify the essential properties any valid solution must satisfy, including alignment correctness, policy confidentiality, governance determinism, evidentiary sufficiency, composable security, and forward secrecy of governance state. We then demonstrate that CAGA is irreducible to existing paradigms, including agent communication protocols, federated learning, secure multi-party computation, single-organization governance architectures, and blockchain-based transparency systems. CAGA is a zero-knowledge coordination problem at the intersection of AI governance, cryptographic protocol design, and multi-agent systems. By precisely defining the problem space and evaluation criteria, this work establishes the foundation for rigorous protocol development while deliberately stopping short of implementation disclosure. This preprint is licensed under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).