A Secure Sandbox Environment for Orchestrating Medical AI Agents Using Model Context Protocols and Role-Based Access...

The evolution of Large Language Models into autonomous agents presents significant opportunities for healthcare, yet no secure environment exists to experiment with these tools in clinical settings. Current agentic systems are unsuitable due to insufficient security mechanisms and the absence of visual components necessary for effective use. This paper introduces Astral, a secure sandbox environment utilizing an Orchestrator-Specialist model for the controlled, secure orchestration, experimentation, and evaluation of medical AI agents. Astral enforces safety and accountability through an auditable identity layer using OAuth 2.0 Token Exchange, which creates verifiable actor claims to log every agent action. To support clinical workflows, it introduces a "generative visual layer" built on persistent WebSockets and Model Context Protocol to enable low-latency, multimodal interaction. Controlled simulations validated that this architecture neutralizes Tool Poisoning Attacks and provides improved transport performance, establishing a pragmatic foundation for trustworthy clinical AI.