Enterprise adoption of cloud-based AI agents faces a fundamental privacy dilemma: leveraging powerful cloud models requires sharing sensitive data, while local processing limits capability. Current agent frameworks like MCP and A2A assume complete data sharing, making them unsuitable for enterprise environments with confidential information. We present SplitAgent, a novel distributed architecture that enables privacy-preserving collaboration between enterprise-side privacy agents and cloud-side reasoning agents. Our key innovation is context-aware dynamic sanitization that adapts privacy protection based on task semantics -- contract review requires different sanitization than code review or financial analysis. SplitAgent extends existing agent protocols with differential privacy guarantees, zero-knowledge tool verification, and privacy budget management. Through comprehensive experiments on enterprise scenarios, we demonstrate that SplitAgent achieves 83.8\% task accuracy while maintaining 90.1\% privacy protection, significantly outperforming static approaches (73.2\% accuracy, 79.7\% privacy). Context-aware sanitization improves task utility by 24.1\% over static methods while reducing privacy leakage by 67\%. Our architecture provides a practical path for enterprise AI adoption without compromising sensitive data.

翻译：企业采用基于云端的AI智能体面临一个根本性的隐私困境：利用强大的云端模型需要共享敏感数据，而本地处理则限制了能力。当前的智能体框架（如MCP和A2A）假设数据完全共享，这使得它们不适合处理机密信息的企业环境。我们提出了SplitAgent，一种新颖的分布式架构，能够实现企业侧隐私智能体与云端侧推理智能体之间的隐私保护协作。我们的核心创新是上下文感知的动态脱敏技术，该技术根据任务语义自适应调整隐私保护强度——合同审查所需的脱敏策略与代码审查或财务分析不同。SplitAgent扩展了现有的智能体协议，提供了差分隐私保证、零知识工具验证和隐私预算管理。通过对企业场景的全面实验，我们证明SplitAgent在保持90.1%隐私保护水平的同时，实现了83.8%的任务准确率，显著优于静态方法（73.2%准确率，79.7%隐私保护水平）。上下文感知脱敏技术相比静态方法将任务效用提升了24.1%，同时将隐私泄露降低了67%。我们的架构为企业采用AI技术提供了一条切实可行的路径，且无需牺牲敏感数据。