arxiv arXiv cs.CL · 1d ago · src: 4d ago · research

AgentRouter reduces agentic workflow costs by 72% via step-level model routing

from English

Researchers propose AgentRouter, a lightweight classifier that optimizes multi-step agentic workflows by routing individual trajectory steps to appropriate model tiers rather than using a single frontier model for all tasks. The system addresses the inefficiency of enterprise systems that waste 60-80% of their inference budget on subtasks that smaller models can handle equally well.

AgentRouter uses a 12M parameter classifier with less than 5ms overhead per step on an A100 GPU to map steps to four model tiers based on five extractable features.
Trained on 50,000 annotated agent trajectory steps, it achieves a 72% cost reduction relative to frontier-only baselines while retaining 97.3% of the quality.
The system demonstrates per-step routing accuracy of 91% on minimal-complexity steps and 85% on efficient-tier steps.
It outperforms RouteLLM and FrugalGPT, which achieve only 31% and 44% cost reduction respectively, by accounting for trajectory-level quality dependencies that single-turn solutions miss.

Importance 2/3 New harness with differentiators arXiv cs.CL AI agents Inference efficiency

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