Scepsy: תפעול זרמי עבודה של סוכנים באמצעות פיפליינים של LLM

תקציר מקורי באנגליתarXiv:2604.15186v3 Announce Type: replace-cross Abstract: Agentic workflows carry out complex tasks by orchestrating multiple large language models (LLMs) and tools. Serving them at a target throughput with low latency is hard because they are written in arbitrary agentic frameworks and their execution times are unpredictable: execution branches, fans out, or recurs in data-dependent ways. Since their LLMs often outnumber the available GPUs, they also oversubscribe GPUs. We describe Scepsy, a serving system that schedules arbitrary multi-LLM agentic workflows onto a GPU cluster. Scepsy exploits the insight that, while the end-to-end latency of an agentic workflow is unpredictable, each LLM's fraction of execution time is comparatively stable across requests. Scepsy profiles each LLM under