Tiered Agentic Oversight: A Hierarchical Multi-Agent System for Healthcare Safety

Hierarchical oversight catches errors before they spread: TAO's layered system of specialized AI agents, modeled after hospital hierarchies (nurses, physicians, specialists), successfully catches and corrects up to 24% of individual agent errors before they compound into patient safety risks—a critical safeguard for clinical AI deployment.

Specialized agents outperform generalist AI: The system achieved up to 8.2% improvement over single-agent and other multi-agent approaches on healthcare safety benchmarks by routing tasks to agents specialized for different complexity levels, suggesting that task-appropriate expertise matters for AI safety in medicine.

Lower-tier agents are essential, not optional: Ablation studies showed that removing lower-tier agents caused the most significant safety degradation (over 3% decline), indicating that the entire hierarchical structure is necessary—cutting corners on oversight layers undermines the system's protective function.

Human-AI collaboration significantly improves outcomes: When a physician acted as the highest oversight tier and provided corrective feedback, medical triage accuracy improved from 40% to 60%, demonstrating that human experts remain essential for validating and refining AI agent decisions in healthcare.

Automated supervision can reduce single points of failure: By distributing decision-making across multiple specialized agents with built-in oversight mechanisms, TAO addresses a fundamental risk of deploying LLMs in healthcare—the vulnerability of relying on a single AI system that could fail catastrophically.