agentic-routing cost-optimization benchmark learned-policies task-decomposition

Meta-router composes operations under cost budgets

Learned policy router selects execution paths (decompose, retrieve, code, delegate, verify) from task text using regularized logistic heads, achieving 100% success on test tasks at 43% lower cost than static routing.

Summary

Agentic systems currently waste budget on fixed workflows; routing decisions per task let you optimize for latency, cost, or success rate without rebuilding pipelines. The benchmark provides reproducible evaluation for your own routing strategies.

Why it matters

Implementation verdict

Replaces hard-coded routing logic with learned policies, but requires: labeled task→operations data (216+ examples), outcome verification infrastructure, and cost accounting per operation. Proof-of-concept only—lexical generalization fails on unseen tasks (75.9% vs 93.5% static on challenge split). Not production-ready; useful for research prototyping.

Sources

1.A controller may answer directly, decompose a request, retrieve evidence, execute code, delegate to a specialist, or verify an intermediate result
2.achieved 100% success versus 93.5% for strong static and fixed workflows, with 43% lower cost than the static policy
3.The gap identifies lexical generalization, rather than route execution, as the principal limitation
4.These results establish a reproducible testbed and a bounded proof of concept, not evidence of live-LLM performance

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