AI 中文总结
针对AI智能体工作流运行时依赖调用的资源分配挑战，提出带可修改预留的ASGE-RR控制器，在两类测试床实验中较现有控制器提升了工作流价值完成量。
AI 中文摘要
AI智能体工作流通常涉及对网络中分布式模型、存储器和工具的远程调用，执行过程中这些依赖调用共同构成智能体服务图（ASG）。与传统服务请求不同，许多依赖调用仅在运行时才会被揭示，因此为当前可见的调用分配资源可能会消耗后续高价值工作流调用所需的容量。我们将此挑战形式化为智能体服务图嵌入（ASGE），这是一个在线网络控制问题，需在容量、成本和截止期限约束下，将运行时揭示的工作流调用映射到服务副本和网络路径。我们提出ASGE-RR，一种带可修改预留的在线ASGE控制器，该控制器在执行约束的同时为可能的未来调用预留容量。ASGE-RR根据预测的工作流后续情况评估候选副本与路径的映射，并在新执行信息可用时更新预留。我们使用OpenHands和GPT Researcher工作流，结合gpt-5.6-luna模型，在两个互补的实验环境（受控Docker测试床和广域网（WAN）测试床）中重放进行评估。研究表明，所有被评估的AI智能体任务均暴露至少一个可在连接建立前引导的运行时揭示依赖调用。利用该控制点，尽管实验环境为小规模，ASGE-RR已展现显著潜力：在WAN测试床上，它比同等信息的滚动时域控制器和当前调用引导控制器多完成高达10%的工作流价值。结果表明，运行时揭示的工作流结构创造了新的网络控制机会：为可能的未来调用保护资源可让更多AI智能体工作流及时完成。
英文摘要
AI-agent workflows often involve remote calls to models, memory stores, and tools distributed across a network. As execution progresses, these dependency calls collectively form an agentic service graph (ASG). Unlike traditional service requests, many dependency calls are revealed only at runtime. Consequently, allocating resources to a currently visible call may consume capacity later needed by a call from a higher-value workflow. We formulate this challenge as Agentic Service Graph Embedding (ASGE), an online network-control problem that maps runtime-revealed workflow calls to service replicas and network paths under capacity, cost and deadline constraints. We present ASGE-RR, an online ASGE controller with revisable reservations. ASGE-RR protects capacity for likely future calls while enforcing the constraints. ASGE-RR evaluates candidate replica-and-path mappings against predicted workflow continuations and updates reservations as new execution information becomes available. We evaluate ASGE-RR using OpenHands and GPT Researcher workflows executed with gpt-5.6-luna and replayed over in two complementary experimental environments, a controlled Docker testbed and a WAN testbed. The investigation shows that all the evaluated AI-agent tasks expose at least one runtime-revealed dependency call that can be steered before connection establishment. Exploiting this control point, even though the experimental environments are small-scale, ASGE-RR already demonstrates noticeable potential: It completes (up to) 10% more workflow value than a same-information rolling-horizon controller and a current-call steering controller on the WAN testbed. The results suggest that runtime-revealed workflow structure creates a new network control opportunity: protecting resources for likely future calls allows more AI-agent workflows to finish in time.
