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 Artificial Intelligence (AI) agent 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 latency improvement over 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：保险和企业间商务中的自动化协商面临重大挑战。现有系统通过将敏感财务数据路由至中心化服务器，迫使使用者在便利性与隐私之间做出权衡，这不仅增加了安全风险，也削弱了用户信任。本研究提出了一种面向隐私保护协商的设备原生自主人工智能体系统。所提出的系统完全运行于用户硬件之上，能够在本地保持敏感约束的同时实现实时议价。它集成了零知识证明以确保隐私，并采用蒸馏世界模型来支持高级设备端推理。该架构在智能体AI工作流中包含了六个技术组件。智能体能够自主规划协商策略、执行安全多方议价，并生成加密审计追踪，而无需将用户数据暴露给外部服务器。该系统在保险和企业采购场景中，于多种设备配置下进行了评估。结果显示平均成功率高达87%，相较于云端基线延迟降低了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，当决策追踪可用时，信任度评分提高了27%。这些发现为在隐私敏感金融领域构建可信自主智能体奠定了基础。