Difficulty-Aware Agent Orchestration in LLM-Powered Workflows

• URL: http://arxiv.org/abs/2509.11079v1
• Date: Sun, 14 Sep 2025 03:57:43 GMT
• Title: Difficulty-Aware Agent Orchestration in LLM-Powered Workflows
• Authors: Jinwei Su, Yinghui Xia, Qizhen Lan, Xinyuan Song, Yang Jingsong, Lewei He, Tianyu Shi,
• Abstract summary: Large Language Model (LLM)-based agentic systems have shown strong capabilities across various tasks.<n>Existing multi-agent frameworks often rely on static or task-level, which either over-process simple queries or underperform on complex ones.<n>We propose Difficulty-Aware Agentic Orchestration (DAAO), a dynamic framework that adapts workflow depth, operator selection, and LLM assignment based on the difficulty of each input query.
• Score: 5.413626686277941
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Model (LLM)-based agentic systems have shown strong capabilities across various tasks. However, existing multi-agent frameworks often rely on static or task-level workflows, which either over-process simple queries or underperform on complex ones, while also neglecting the efficiency-performance trade-offs across heterogeneous LLMs. To address these limitations, we propose Difficulty-Aware Agentic Orchestration (DAAO), a dynamic framework that adapts workflow depth, operator selection, and LLM assignment based on the difficulty of each input query. DAAO comprises three interdependent modules: a variational autoencoder (VAE) for difficulty estimation, a modular operator allocator, and a cost- and performance-aware LLM router. By leveraging heterogeneous LLMs and dynamically tailoring workflows, DAAO enables fine-grained, query-specific reasoning strategies. DAAO outperforms prior multi-agent systems in both accuracy and inference efficiency across six benchmarks. We will release our code and implementation details upon publication.

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