The Challenge of Tail Latency in Agentic Workflows

Agentic LLM workflows often involve complex, multi-step chains where the total execution time is highly sensitive to the slowest component (the tail). Traditional scheduling models often treat task readiness and execution release as a single event, which leads to resource underutilization and significant latency spikes when individual LLM calls experience variance in response times. This paper argues that by decoupling these two concepts, systems can achieve more predictable performance and better throughput in production environments.

Decoupling Readiness from Release

The core proposal is a scheduling framework that distinguishes between when a task is logically ready to be processed and when it is actually released to the compute resource.

• Readiness: The state where all dependencies for a specific agentic step are met (e.g., previous tool outputs are available).
• Release: The act of dispatching the task to an LLM inference engine based on real-time system load, model availability, and historical tail-latency data.

By introducing this buffer, the scheduler can implement "tail-aware" policies. Instead of immediate execution, the scheduler can prioritize tasks that are likely to fall into the tail of the latency distribution or batch requests to optimize GPU utilization without violating user-facing SLAs. This approach effectively treats agentic workflows as distributed systems problems rather than simple sequential API calls, allowing for dynamic load balancing across heterogeneous model endpoints.