arXiv:2606.27492v2 Announce Type: replace Abstract: LLM-based multi-agent systems show great potential for complex tasks through role specialization and collaborative reasoning. However, existing automated workflow design methods primarily focus on one-off search and candidate optimization, lacking sustained accumulation and reuse of structural design experience to meet evolving coordination demands. We propose QueenBee, an experience-guided framework for multi-agent workflow evolution. QueenBee represents workflows as executable programs that generate temporal communication DAGs and maintains Structural Feedback and Transition Bank (SFT-Bank), a persistent structural memory of execution diagnoses, design hypotheses and validation outcomes, and historical programs. This accumulated experience guides subsequent workflow generation, validation, and optimization, enabling the co-evolution of collaboration programs and design experience. Experiments on Silo-Bench show that QueenBee outperforms fixed communication baselines across team sizes and achieves the highest mean task score among recent automated multi-agent workflow optimization methods. Ablation studies further show that SFT-Bank components provide complementary performance gains, while evolution dynamics reveal a consistently improving average trend in the quality of the best collaboration structures found across independent runs as structural experience accumulates.