MetaRoute-Bench Framework Evaluates Agentic System Routing Policies

Researchers have unveiled MetaRoute-Bench, an open and inspectable framework designed to systematically evaluate meta-decision policies within agentic AI systems. These policies are crucial for determining how an agent navigates complex tasks, deciding whether to provide a direct answer, break down a problem, use a tool, execute code, delegate, or verify results. Traditionally, such decisions are embedded and only assessed through aggregate task accuracy. The benchmark comprises 180 synthetic task profiles across various domains, eight routing policies, and 30 random seeds, generating 43,200 traces. A task-aware compositional policy demonstrated a 79.4% success rate, outperforming a strong workload-specific static policy (76.7%) and one-shot task routing (67.4%). While achieving higher success, the compositional policy incurred slightly higher mean cost and latency. Ablation studies highlighted the importance of route composition and verification for overall performance. The primary contribution is a reproducible evaluation method and an analysis of routing policy trade-offs, generated via a seeded offline execution model rather than live deployment.