From Now On, Any AI Agent Can Act Autonomously and Never Drift: Y.I.N.-AGENT-ACT - Privacy-Preserving Autonomous AI Agent System with Action Chain Compliance Tokens and Cryptographic Intent Verification

Abstract

We present Y.I.N.-AGENT-ACT, a privacy-preserving architecture for autonomous AI agent action chains that mathematically guarantees zero intent drift while maintaining cryptographic compliance across multi-step workflows. The core innovation is the mandatory DP→ZK→HE ordering (Differential Privacy → Zero-Knowledge Proof → Homomorphic Encryption) applied to every step in autonomous agent execution, combined with 136-byte Action Chain Compliance Tokens providing cryptographic chain integrity through HMAC-SHA256 chaining. Key results (measured on 48-step test corpus including 15 adversarial scenarios): (1) 100% drift prevention—all 15 drift scenarios caught with zero false negatives; (2) F1-score 0.882 at threshold 0.5 (precision 0.789, recall 1.000); (3) ~85ms per-step overhead with O(N) verification complexity; (4) 136-byte token size verified to match patent specification exactly; (5) native EU AI Act Article 50 compliance via verifiable privacy proofs. Y.I.N.-AGENT-ACT addresses the emerging agent autonomy crisis where AI systems like OpenAI Operator and Anthropic Computer Use execute multi-step tasks without cryptographic guarantees. The Intent Drift Elimination Theorem establishes that undetected drift probability is bounded by δ+(1−threshold), achieving zero false negatives across all measured adversarial scenarios including phishing, data exfiltration, and privilege escalation attacks. Protected by US Pending Patent 63/956,723 (106 claims). The agentic AI market is projected to reach $93.2 billion by 2032, but deployment remains at 2% of enterprises due to governance bottlenecks. Y.I.N.-AGENT-ACT eliminates the 6-18 month compliance barrier through cryptographic compliance-by-design.