AgentAsk: Multi-Agent Systems Need to Ask

• URL: http://arxiv.org/abs/2510.07593v1
• Date: Wed, 08 Oct 2025 22:36:05 GMT
• Title: AgentAsk: Multi-Agent Systems Need to Ask
• Authors: Bohan Lin, Kuo Yang, Yingchuan Lai, Yudong Zhang, Chen Zhang, Guibin Zhang, Xinlei Yu, Miao Yu, Xu Wang, Yang Wang,
• Abstract summary: Multi-agent systems built on large language models (LLMs) promise enhanced problem-solving capabilities through collaborative division of labor.<n>We propose AgentAsk, a lightweight and plug-and-play clarification module that treats every inter-agent message as a potential failure point and inserts minimally necessary questions to arrest error propagation.<n>AgentAsk consistently improves accuracy and robustness over public multi-agent implementations while keeping overhead minimal, with latency and extra cost all less than 5%.
• Score: 26.13279490836716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent systems built on large language models (LLMs) promise enhanced problem-solving capabilities through collaborative division of labor. However, they frequently underperform single-agent baselines due to edge-level error cascades: minor inaccuracies at one message handoff propagate across the entire chain. We propose AgentAsk, a lightweight and plug-and-play clarification module that treats every inter-agent message as a potential failure point and inserts minimally necessary questions to arrest error propagation. AgentAsk follows a three-stage pipeline: (i) distilling edge-level judgments from curated failure traces into a compact policy, (ii) supervising the policy to determine when/what/whom/how to ask, and (iii) optimizing online with E-GRPO, a reinforcement learning objective that balances accuracy, latency, and cost. The module is architecture-agnostic and easy to integrate into existing orchestration. Across math, reasoning, and coding benchmarks, AgentAsk consistently improves accuracy and robustness over public multi-agent implementations while keeping overhead minimal, with latency and extra cost all less than 5%, approaching the performance of a strong evaluator. Beyond empirical improvements, we contribute a principled taxonomy of edge-level errors and a practical recipe for link-local intervention, offering a scalable pathway toward more reliable LLM-based multi-agent systems.

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