Large Language Model (LLM)-based multi-agent systems are increasingly powerful, but current agentic workflow optimization paradigms make an unsatisfying trade-off. Task-level methods spend substantial offline compute yet deploy only a single workflow, leaving complementary candidates unused, while query-level methods synthesize a new workflow per query at substantial inference cost. Our motivating analysis shows these paradigms are more complementary than competing: workflows discovered during offline search often solve different subsets of queries, and many queries handled by expensive query-level generation can already be solved by cheaper precomputed workflows. This suggests a different objective: rather than searching for one universally best workflow or regenerating one per instance, we should build a compact bank of reusable, complementary workflows and select among them adaptively at inference time. Doing so requires solving three coupled problems: generating complementary rather than redundant candidates, compressing them into a small deployable portfolio, and assigning each query to the right workflow under a performance-cost trade-off. To this end, we present FlowBank, a three-stage framework for portfolio-based agentic workflow optimization. Diversifying proposes DiverseFlow to steer search toward under-covered queries and produce a high-coverage candidate pool. Curating proposes CuraFlow to compress this pool into a compact portfolio with minimal redundancy. Matching casts deployment as edge-value prediction on a query-workflow bipartite graph and routes each incoming query to the portfolio member with the best predicted utility. Across five benchmarks, FlowBank achieves the highest average score among the evaluated methods while remaining cost-competitive, improving over the strongest automated and handcrafted baselines by 4.26% and 14.92% relative, respectively.

翻译：基于大语言模型的多智能体系统日益强大，但当前智能体工作流优化范式存在不尽如人意的权衡。任务级方法需要大量离线计算资源却仅部署单一工作流，导致互补候选方案被闲置；而查询级方法虽能为每个查询合成新工作流，却需承担高昂推理成本。我们的动机分析表明：这两种范式实为互补而非竞争——离线搜索发现的工作流常能解决不同子集查询，而昂贵查询级生成处理的许多问题，本可通过更经济的预计算工作流解决。这启示我们应确立不同目标：与其寻找普适最优单一工作流或为每个实例重新生成，不如构建紧凑可复用的互补工作流库，并在推理时实现自适应选择。该目标需解决三个耦合问题：生成互补而非冗余的候选方案、压缩为轻量可部署的资产组合、在性能-成本权衡下为每个查询分配合适工作流。为此我们提出FlowBank三阶段框架：多样化阶段提出DiverseFlow引导搜索覆盖被忽略的查询子集，生成高覆盖率候选池；精炼阶段提出CuraFlow将候选池压缩为最小冗余的紧凑组合；匹配阶段将部署建模为查询-工作流二部图的边值预测，为每个入站查询路由至预测效用最优的组合成员。在五个基准测试中，FlowBank在保持成本竞争力的同时取得最高平均分，相较最强自动基线和人工基线的相对提升分别达4.26%和14.92%。