RA-MoWE: Workflow-Affinity Embeddings for Query Clustering and Agentic Workflow Generation

Agentic workflows enable large language models (LLMs) to solve complex tasks by coordinating reasoning, tool use, and verification. However, a workflow optimized for an entire task collection can overlook differences in the reasoning strategies that individual queries need, while searching for a new workflow for every query repeats costly optimization. To address this tradeoff, we introduce RA-MoWE, a framework that uses workflow-affinity embeddings to cluster queries and guide the generation of reusable expert workflows. Each embedding records how well a fixed set of reference workflows solves a query, revealing similarities in which reasoning strategies are effective. RA-MoWE uses each cluster’s queries and average embedding to initialize and refine a specialized workflow through execution feedback. An embedding encoder predicts these embeddings from query text, allowing new queries to select a generated expert without first executing the reference workflows. With predicted affinities, RA-MoWE achieves 42.90% on MixBench-H and 61.7% on MATH, using 1.98 language-model calls per MixBench-H query. With measured affinities, it reaches 62.1% on MATH and resolves 24.50% of SWE-bench Verified issues, indicating setting-dependent benefits from reusable expert workflows