Privacy-Preserving Agentic AI in Edge Networks With Content and Trace Protection

Agentic Artificial Intelligence (AI) enables task execution by coordinating calls to service agents, which makes it attractive for edge services under diverse user requests in edge networks. However, this workflow introduces new privacy risks. First, transmitted call messages may expose sensitive content through explicit fields and values. Second, the observable calling sequence over the network may reveal user intent through the selection and ordering of called service agents. Existing edge agentic AI methods mainly improve efficiency through multi-agent coordination, model compression, or cloud offloading, while privacy defenses are still limited and often focus on input filtering, interface masking, or action constraints. To address these issues, we propose a two-layer privacy-preserving agentic framework with a device controller agent and a service agent pool. The device agent integrates three privacy modules: (i) The disclosure control module marks each field value pair as 'keep', 'hide', or 'drop' to limit plaintext exposure. (ii) The variational information bottleneck encoding module replaces 'hide' values with protected semantic hints to preserve utility. (iii) The decoy insertion module adds safe decoy calls under a per-subtask budget to reduce trace leakage. Experiments across diverse tasks and settings show that the proposed framework reduces both content leakage and trace leakage while maintaining task utility.