Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-off between convenience and privacy by routing sensitive financial data through centralized servers, increasing security risks, and diminishing user trust. This study introduces a device-native autonomous Agentic AI system for privacy-preserving negotiations. The proposed system operates exclusively on user hardware, enabling real-time bargaining while maintaining sensitive constraints locally. It integrates zero-knowledge proofs to ensure privacy and employs distilled world models to support advanced on-device reasoning. The architecture incorporates six technical components within an Agentic AI workflow. Agents autonomously plan negotiation strategies, conduct secure multi-party bargaining, and generate cryptographic audit trails without exposing user data to external servers. The system is evaluated in insurance and B2B procurement scenarios across diverse device configurations. Results show an average success rate of 87 %, a 2.4x reduction in latency relative to cloud baselines, and strong privacy preservation through zero-knowledge proofs. User studies show 27 % higher trust scores when decision trails are available. These findings establish a foundation for trustworthy autonomous agents in privacy-sensitive financial domains.

翻译：保险和企业对企业（B2B）商务中的自动化谈判面临重大挑战。现有系统迫使在便利性与隐私性之间做出权衡，通过将敏感财务数据路由至集中式服务器，增加安全风险并降低用户信任。本研究提出了一种设备原生自主式Agentic AI系统，用于实现隐私保护的谈判。该系统完全运行于用户硬件上，支持实时议价，同时将敏感约束条件保留在本地。它集成了零知识证明以确保隐私安全，并采用精简世界模型支持先进的设备端推理。该架构在Agentic AI工作流中包含了六个技术组件。智能体能够自主规划谈判策略、进行安全多方议价并生成加密审计轨迹，而不会将用户数据暴露给外部服务器。该系统在保险和B2B采购场景中针对多种设备配置进行了评估。结果显示，平均成功率达87%，相对于云端基准方案延迟降低2.4倍，并通过零知识证明实现了强大的隐私保护。用户研究表明，当决策轨迹可用时，信任评分提高了27%。这些发现为隐私敏感金融领域中可信自主代理的建立奠定了基础。