# AgentRouter: Heterogeneous Model Routing for Cost-Optimal Multi-Step Agentic Workflows

arXiv: https://arxiv.org/abs/2609.22951 · HTML: https://mlsanity.com/paper/2609.22951/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 14 references that are in the map.
- First posted on arXiv 19 Sep 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 12 of 12)

- [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://mlsanity.com/paper/2305.05176/) (2023)
- [RouteLLM: Learning to Route LLMs with Preference Data](https://mlsanity.com/paper/2406.18665/) (2024)
- [Optimizing Model Selection for Compound AI Systems](https://mlsanity.com/paper/2502.14815/) (2025)
- [Difficulty-Aware Agentic Orchestration for Query-Specific Multi-Agent Workflows](https://mlsanity.com/paper/2509.11079/) (2025)
- [Adaptive LLM Routing under Budget Constraints](https://mlsanity.com/paper/2508.21141/) (2025)
- [Doing More with Less: A Survey on Routing Strategies for Resource Optimisation in Large Language Model-Based Systems](https://mlsanity.com/paper/2502.00409/) (2025)
- [Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey](https://mlsanity.com/paper/2603.04445/) (2026)
- [Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning](https://mlsanity.com/paper/2511.02755/) (2025)
- [Small Language Models for Agentic Systems: A Survey of Architectures, Capabilities, and Deployment Trade offs](https://mlsanity.com/paper/2510.03847/) (2025)
- [Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering](https://mlsanity.com/paper/2601.14470/) (2026)
- [Budget-Aware Agentic Routing via Boundary-Guided Training](https://mlsanity.com/paper/2602.21227/) (2026)
- [One Head, Many Models: Cross-Attention Routing for Cost-Aware LLM Selection](https://mlsanity.com/paper/2509.09782/) (2025)

## Built on by (most cited first, 1 of 1)

- [What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents](https://mlsanity.com/paper/2609.39989/) (2026)

## Abstract (arXiv)

Enterprise agentic systems that route every trajectory step to a frontier model waste 60-80% of their inference budget on subtasks that smaller models handle equally well. Existing routing solutions optimize single-turn query assignment but ignore a property unique to agentic workflows: subtask complexity varies widely within a single trajectory. A planning step may require frontier-class reasoning while a subsequent formatting step needs only a 7B model. We formalize step-level model routing as a sequential assignment problem over agent trajectories and propose AgentRouter, a lightweight classifier (12M parameters, <5ms overhead per step on an A100 GPU) that maps each trajectory step to one of four model tiers using five features extractable at routing time. Trained on 50,000 annotated agent trajectory steps spanning planning, coding, research, and data analysis tasks, AgentRouter achieves 72% cost reduction relative to frontier-only baselines, retaining 97.3% of frontier-only quality (less than 3% degradation in end-to-end task completion); per-step routing accuracy reaches 91% on minimal-complexity steps and 85% on efficient-tier steps, with 76-82% on the harder mid-range and frontier tiers. On the same benchmarks, RouteLLM and FrugalGPT (applied per-step) achieve only 31% and 44% cost reduction respectively, because their single-turn training signal misses trajectory-level quality dependencies.