TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI

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Computer Science > Artificial Intelligence

arXiv:2607.22465v1 (cs)

[Submitted on 24 Jul 2026 (this version), latest version 27 Jul 2026 ( v2 )]

Title: TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI

Authors: Ritik Raj , Souvik Kundu , Sarbartha Banerjee , Dheemanth Joshi , Ishita Vohra , Tushar Krishna

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Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI. Existing routers, primarily make independent routing decisions for each LLM call. However, agentic applications execute as long-horizon workflows whose quality is determined only by a delayed, task-level outcome. This mismatch prevents per-call routers from correctly attributing feedback to individual routing decisions. Towards mitigating this, we present TRACE-Router, a task-level routing framework that aligns routing with the unit of supervision. TRACE-Router assigns each task to a model once at admission using a contextual bandit, pins all subsequent LLM calls to the selected backend, and updates its policy using the task's terminal reward, jointly accounting for accuracy and latency. By leveraging delayed task feedback,

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