Abstract
In modern agentic AI environments, autonomous agents dynamically exchange prompts, embeddings, intermediate reasoning states, tool outputs, and memory artifacts, often across geographic regions and infrastructure owned by different providers. Existing solutions govern data primarily at storage boundaries or API entry points, leaving significant gaps during agent reasoning, delegation, and cross-provider execution.
The proposed solution addresses this challenge by binding privacy and governance policies directly to AI artifacts and enforcing those policies at runtime, before any agent action is executed. Each AI artifact carries immutable policy metadata that specifies permitted regions, providers, agent roles, purposes of use, and retention constraints. Autonomous agents and execution environments validate these policies locally, without reliance on a centralized controller, ensuring continuous compliance throughout the AI reasoning lifecycle.
The framework supports decentralized, agent-centric enforcement, enabling scalable and fault-tolerant governance consistent with autonomous multi-agent systems. It ensures that policy constraints propagate automatically to derived artifacts and remain enforceable during agent-to-agent interaction, tool invocation, memory persistence, and cross-provider workflows. Comprehensive auditability is achieved through policy-aware logging of AI actions without exposing sensitive data.
By enabling continuous, artifact-centric, and provider-agnostic privacy enforcement, the proposal allows organizations to safely deploy advanced multi-agent AI systems while maintaining strict control over data privacy, sovereignty, and regulatory compliance. The approach is future-proof, adaptable to evolving AI architectures, and applicable across regulated industries such as healthcare, finance, government, and enterprise AI platforms.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
M M, Niranjan, "Policy-Bound Governance for Privacy-Preserving Multi-Agent AI Systems Across Multiple Service Providers", Technical Disclosure Commons, (March 16, 2026)
https://www.tdcommons.org/dpubs_series/9530