arXiv:2607.08565v1 Announce Type: cross Abstract: LLM scheduling is critical to serving, yet it remains unclear how well existing designs fit agentic serving--with LLM requests issued by agents instead of humans. This shifts the workload in two ways: (1) agents act only on complete responses, making the cluster's tokens per second (TPS) the primary goal and relaxing--not eliminating--per-token latency requirements; and (2) requests share much of their KV\$-reuse exceeds 80% of request tokens in a production trace from BAILIAN, versus 54-62% in chat. This paper first contributes a systematic st
The proliferation of AI agents is creating new demands on LLM serving infrastructure, necessitating optimized scheduling approaches like SMetric to maintain efficiency and cost-effectiveness.
Efficient LLM scheduling is crucial for scaling AI agent operations, directly impacting operational costs and the performance ceiling of agentic systems.
The focus of LLM scheduling shifts from human-centric latency requirements to throughput optimization for agent interactions, leading to better resource utilization and potentially lower inference costs.
- · AI agent developers
- · Cloud providers offering AI services
- · Companies deploying autonomous AI systems
- · LLM providers with inefficient scheduling
- · Legacy inference infrastructure
Improved efficiency in LLM serving for AI agents.
Reduced operational costs for AI agent deployments, accelerating their adoption across industries.
Deeper integration of AI agents into core business processes, driving further demand for optimized AI compute.
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Read at arXiv cs.AI