Decoupling Readiness from Release for Tail-Aware Scheduling of Agentic LLM Workflows
A new scheduling strategy reduces P95 tail latency for agentic LLM workflows by 3.5x under high contention.
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 conte...
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This research addresses a critical bottleneck in agentic workflows by demonstrating that eager task submission actually degrades performance under heavy system contention. By strategically controlling when turns are released, the system minimizes P95 latency and enables more efficient execution of complex, multi-step LLM tasks.
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