Abstract
Large language models (LLMs) increasingly construct multi-agent workflows that decompose a complex task and assign specialist agents from a pool. However, building such a workflow well remains challenging: how finely to divide the task, which agent to trust with each subtask, and when to create a new specialist are all critical decisions a workflow constructor needs to settle up front. Thus, whether each subtask succeeds remains unknown until the workflow runs. Yet, improving a workflow is costly. Locating a fault usually requires a reference answer, a graded outcome, or a trained assessor, and the fix is applied to the whole workflow through re-execution, re-search, or retraining. We propose InFlowOp, which prices every decision in one label-free cost that weighs how well an agent's competence meets what a subtask demands against how much that agent takes to run. Before execution, InFlowOp bidirectionally determines the granularity of task decomposition and agent assignment following from the cost rather than from a fixed template. During execution, InFlowOp corrects a fault with the cheapest move via the same cost that serves the workflow both as it is built and as it runs. Facing the workflow-level evaluation challenge, we introduce Braid, a benchmark whose tasks require multi-agent coordination beyond single-agent capability. Across various domains and backbones, InFlowOp outperforms single agent baselines by up to $+11.97\%$, achieving $+9.64\%$ with in-flow optimization. Our project page: https://xhguo7.github.io/InFlowOp/.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Guo, X., Wang, H., Chen, S., Chen, Z., Cheng, W., Wang, Q., & Chen, H. (2026). Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization. https://omanscience.com/en/articles/pay-for-the-fault-not-the-flow-label-free-in-flow-multi-agent-workflow-optimization
MLA 9
Guo, Xuehang, et al. "Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization." https://omanscience.com/en/articles/pay-for-the-fault-not-the-flow-label-free-in-flow-multi-agent-workflow-optimization.
Chicago (author–date)
Guo, Xuehang, Haoyu Wang, Shengyu Chen, Zach Chen, Wei Cheng, Qingyun Wang, and Haifeng Chen. 2026. "Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization." https://omanscience.com/en/articles/pay-for-the-fault-not-the-flow-label-free-in-flow-multi-agent-workflow-optimization.
Harvard
Guo, X., Wang, H., Chen, S., Chen, Z., Cheng, W., Wang, Q. and Chen, H. (2026) 'Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization', Available at: https://omanscience.com/en/articles/pay-for-the-fault-not-the-flow-label-free-in-flow-multi-agent-workflow-optimization.
Vancouver
Guo X, Wang H, Chen S, Chen Z, Cheng W, Wang Q, et al. Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization. https://omanscience.com/en/articles/pay-for-the-fault-not-the-flow-label-free-in-flow-multi-agent-workflow-optimization
IEEE
X. Guo, H. Wang, S. Chen, Z. Chen, W. Cheng, Q. Wang, and H. Chen, "Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization," https://omanscience.com/en/articles/pay-for-the-fault-not-the-flow-label-free-in-flow-multi-agent-workflow-optimization.