SwarmX: Agentic Scheduling for Low-Latency Agentic Systems

SwarmX is a new scheduling system designed to optimize GPU-CPU cluster performance for agentic AI applications that make multiple model calls and tool executions. The system uses neural predictors to reduce tail latency by up to 61.5% and sustain 2x higher throughput than production schedulers, addressing a critical infrastructure gap as AI agents become more complex.

SwarmX tackles a fundamental infrastructure problem emerging as AI applications grow from single-model inference to multi-step agentic workflows. Traditional scheduling systems assume predictable compute patterns, but agentic systems execute variable numbers of model calls based on prompt semantics, creating unpredictable latency and resource utilization. This mismatch between workload characteristics and scheduling assumptions has become a bottleneck for deploying sophisticated AI agents at scale.

The system's innovation lies in its scheduling-specific neural predictors that learn relationships between prompt features, device characteristics, and runtime behavior. By exposing distributional predictions rather than point estimates, SwarmX enables routers and scalers to make tail-aware decisions that prioritize worst-case performance—critical for production SLOs. The framework's integration with existing infrastructure means it can be adopted without requiring complete system redesigns.

For the infrastructure and model-serving community, SwarmX represents a maturing recognition that agentic AI requires purpose-built systems rather than adaptations of batch-processing schedulers. The 61.5% tail latency reduction and 2x throughput gains are substantial improvements that directly impact user experience and operational costs. As enterprises deploy more complex agent systems, efficient scheduling becomes as important as model optimization.

The practical validation across multi-agent code generation, research workflows, and multimodal applications demonstrates broad applicability. Future development likely includes further predictor refinement, adaptation to new model architectures, and integration with emerging inference optimization techniques. This work establishes scheduling as a distinct optimization frontier within agentic systems.

• →SwarmX reduces tail latency up to 61.5% through scheduling-specific neural predictors trained on agentic workload patterns.
• →Distributional prediction exposure enables routers and scalers to optimize for worst-case scenarios rather than average performance.
• →The system sustains 2x higher throughput than production schedulers under identical SLO constraints across diverse agentic workflows.
• →Neural predictors capture prompt semantics, device characteristics, and runtime features to handle variable model-call structures.
• →Production deployment across nearly 1,000 GPUs validates practical scalability and real-world effectiveness.