arXiv:2606.09613v1 Announce Type: cross Abstract: Multi-turn LLM agents interleave model calls with external tool invocations, shifting serving from stateless request processing to stateful program execution. Serving these workloads requires scheduling, KV-cache management, and routing policies that use program-level context, including turn dependencies, tool-induced gaps, and reusable KV state. Evaluating such policies directly on real systems is costly, since each design point may require dedicated accelerator time across arrival rates, model scales, serving-instance counts, and memory hiera
The rapid development and adoption of multi-turn LLM agents necessitate new methods for efficient serving and evaluation, especially as computational demands grow.
Efficient serving of LLM agents is a key bottleneck for their widespread deployment and economic viability, impacting the scalability and cost-effectiveness of AI applications.
The focus is shifting from stateless LLM serving to stateful, program-execution-based serving, requiring new hardware-aware simulators and optimizing strategies.
- · AI infrastructure providers
- · Cloud computing platforms
- · LLM agent developers
- · Inefficient AI serving architectures
- · Companies with high LLM inference costs
Improved simulation tools will enable faster iteration and optimization of LLM agent serving policies.
More efficient serving will reduce the operational costs of AI agents, accelerating their deployment across industries.
The proliferation of cost-effective AI agents could trigger new business models and disrupt existing service sectors.
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Read at arXiv cs.AI