arXiv:2606.21401v2 Announce Type: replace-cross Abstract: Agentic AI applications compose multiple model calls and tool executions, creating new scheduling challenges for GPU-CPU clusters. Their inference time and model-call structure often depend on prompt semantics, making conventional scheduling approaches ineffective for low-latency serving. This paper presents SwarmX, a system that implements agentic scheduling for low-latency agentic applications. SwarmX uses scheduling-specific neural predictors to capture prompt, device, runtime, and target-model features; exposes distributional predic
The proliferation of agentic AI applications is creating significant challenges for existing GPU-CPU cluster scheduling, making new, specialized solutions like SwarmX necessary for low-latency performance.
Efficient scheduling of agentic AI is critical for their widespread adoption and performance, directly impacting the viability of new AI-driven workflows and the capabilities of autonomous systems.
Traditional scheduling approaches are being superseded by agentic-specific systems using neural predictors, fundamentally altering how AI inference and tool execution are managed in complex applications.
- · AI application developers
- · GPU manufacturers
- · Cloud providers
- · Enterprises adopting agentic AI
- · Companies reliant on conventional scheduling
- · AI applications with high latency tolerance
Improved performance and decreased latency for complex agentic AI systems.
Accelerated development and deployment of sophisticated autonomous AI agents across various industries.
Enhanced competition in the AI agent market, favoring systems that can efficiently manage complex, real-time tasks.
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Read at arXiv cs.AI