Decoupling Readiness from Release for Tail-Aware Scheduling of Agentic LLM Workflows
Agentic LLM workflows consist of sequences of model turns interleaved with tool interactions, so their end-to-end completion time depends not only on inference speed but also on when ready turns are released. Most runtimes release each turn immediately upon readiness. Under contention, this eager release policy can accumulate released but unfinished work; once submitted, those turns can no longer be reordered by the workflow-level policy, increasing tail latency. We present a tail-risk-aware turn release scheduling method that jointly decides which ready turn to release next and how much released but unfinished work to maintain. The method uses a mean--Conditional Value-at-Risk (CVaR) obj...
| Authors | Bochao Feng, Jianjiang Li, Haojie Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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