Cryptographic Governance for Autonomous AI Agents in Decentralized Systems: A Policy-Enforced Identity and Accountability Framework

Abstract

Autonomous artificial intelligence agents increasingly act across decentralized systems, yet existing authorization models provide limited mechanisms for constraining delegated authority, proving policy compliance, and assigning accountability for machine-initiated actions. This paper presents a policy-enforced identity and accountability framework for cryptographic governance of autonomous AI agents. The proposed architecture binds each agent to a verifiable decentralized identity, machine-readable authorization policies, delegated capability constraints, and tamper-evident action records. Before an action is executed, the framework evaluates identity validity, policy scope, contextual conditions, delegation depth, expiration, and revocation status. Approved actions generate cryptographically verifiable receipts that link the agent, authorizing principal, applicable policy, execution context, and resulting state transition without requiring disclosure of unnecessary sensitive information. The framework also supports attenuated delegation, enabling subordinate agents to receive narrower permissions than their parent agents while preventing privilege amplification. A formal threat model evaluates impersonation, policy substitution, replay attacks, unauthorized delegation, audit-log manipulation, and compromised agent behavior. Security analysis indicates that the architecture strengthens provenance, non-repudiation, least-privilege enforcement, and post-execution auditability across heterogeneous decentralized environments. The proposed approach provides a foundation for governing autonomous agents in blockchain networks, distributed applications, machine-to-machine systems, and multi-agent infrastructures where conventional access control is insufficient. It shifts AI governance from trust-based supervision toward verifiable, policy-bound, and cryptographically accountable execution.