Zero-knowledge proofs (ZKPs) are emerging as a solution for verifying the identity of AI agents, addressing concerns of impersonation and trust in autonomous systems. Unlike traditional methods that risk data breaches, ZKPs allow entities to prove claims without disclosing sensitive information. This technology can ensure AI training data integrity and ethical compliance while maintaining proprietary confidentiality. ZKPs have the potential to revolutionize the agent economy by facilitating secure, trustless interactions between AI systems and meeting regulatory standards. Despite these advantages, the adoption of ZKPs is hindered by their technical complexity and reluctance from data-centric businesses to embrace the change.