SAGA: Workflow-Atomic Scheduling for AI Agent Inference on GPU Clusters
AI agents execute tens to hundreds of chained LLM calls per task, yet GPU schedulers treat each call as independent, discarding gigabytes of intermediate state between steps and inflating end-to-end latency by 3-8x. We argue that this request-level abstraction is fundamentally mismatched to compound AI workloads, and propose a shift to program-level scheduling: treating the entire agent workflow (not individual inference calls) as the first-class schedulable unit. We present SAGA, a distributed scheduler that implements this abstraction through three mechanisms: (1) Agent Execution Graphs that capture workflow structure to predict KV cache reuse across tool-call boundaries, achieving with...
| Authors | Dongxin Guo, Jikun Wu, Siu Ming Yiu |
|---|---|
| Primary category | cs.DC |
| Published | 1 May 2026 |
| Last updated at source | 19 June 2026 |
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