Root Intent and Chain-Level Safety in Multi-Agent Healthcare AI: A Conceptual Framework

As AI systems evolve into multi-agent architectures, new governance challenges emerge that single-agent safety frameworks cannot address. This paper introduces two conceptual principles for ensuring safe and bounded behavior in healthcare AI: the Root Intent Principle and the Chain-Level Hazard Principle. Together they provide a theoretical foundation for understanding how multi-agent systems can maintain alignment with human intent, preserve role integrity, and prevent the propagation of unsafe inferences across agent chains. A third section articulates the governance fabric framing — a conceptual model that treats governance as a distributed semantic layer rather than a perimeter around individual agents. The focus throughout is conceptual rather than operational, offering a governance-layer framing suitable for public discourse, academic citation, and standards development.