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The paper proposes InFlowOp, a label-free method for constructing and optimizing multi-agent LLM workflows by pricing each agent assignment with a unified cost that balances agent competence against execution cost, and introduces Braid, a benchmark for evaluating workflow-level multi-agent coordination. Across domains and backbones, InFlowOp outperforms single-agent baselines by up to 11.97% and achieves 9.64% gains in in-flow optimization.