Title: Tiered Agentic Oversight: A Hierarchical Multi-Agent System for Healthcare Safety
Primary area: foundation or frontier models, including LLMs
Keywords: Agentic Oversight, AI Safety, Multi-Agent System, Healthcare

Abstract: Large language models (LLMs) deployed as agents introduce significant safety risks in clinical settings due to their potential for error and single points of failure. We introduce **Tiered Agentic Oversight (TAO)**, a hierarchical multi-agent system that enhances AI safety through layered, automated supervision. Inspired by clinical hierarchies (e.g., nurse-physician-specialist) in hospital, TAO routes tasks to specialized agents based on complexity, creating a robust safety framework through automated inter- and intra-tier communication and role-playing. Crucially, this hierarchical structure functions as an effective error-correction mechanism, absorbing up to 24\% of individual agent erros before they can compound. Our experiments reveal TAO outperforms single-agent and other multi-agent systems on 4 out of 5 healthcare safety benchmarks, with up to an 8.2\% improvement. Ablation studies confirm key design principles of the system: (i) its adaptive architecture is over 3\% safer than static, single-tier configurations, and (ii) its lower tiers are indispensable, as their removal causes the most significant degradation in overall safety. Finally, we validated the system's synergy with human doctors in a user study where a physician, acting as the highest tier agent, provided corrective feedback that improved medical triage accuracy from 40\% to 60\%. Project Page: https://tiered-agentic-oversight.github.io/

TL;DR: We introduce Tiered Agentic Oversight (TAO), a hierarchical multi-agent system that enhances AI safety through layered, automated supervision.