AGENTSERVESIM: New Simulator for Multi-Turn LLM Agent Serving Policies
Researchers have developed AGENTSERVESIM, a hardware-aware simulator designed to evaluate serving policies for multi-turn LLM agents that interleave model calls with external tool invocations. The simulator addresses limitations in existing LLM serving simulators by modeling program-level dynamics including turn dependencies, tool-induced gaps, and KV-cache management across different memory hierarchies. This tool enables researchers to test agent-serving policies on commodity CPUs with 6% error accuracy compared to real systems, reducing the need for expensive accelerator time during policy exploration.
AGENTSERVESIM is a new simulator that addresses a gap in existing LLM serving infrastructure. While current simulators focus on stateless request-level workloads, multi-turn LLM agents require different handling because they execute as stateful programs that interleave model inference with external tool calls, creating gaps and dependencies that affect performance. The simulator includes four main components: a Program Orchestrator that preserves program identity and turn order, a Tool Simulator that models tool-induced delays, a Session-Aware Router that maintains cache affinity, and a KV Residency Model that tracks key-value cache placement across different memory tiers (HBM, host DRAM/CXL, and eviction). Testing against real serving deployments shows the simulator reproduces actual system behavior within 6% error on key performance metrics while running entirely on commodity CPUs, making it a cost-effective alternative to repeated accelerator-based testing.
What's missing
The paper does not discuss limitations of the simulation approach, such as which aspects of real hardware behavior may not be fully captured, or open questions about scaling to even larger agent programs or more complex tool ecosystems.
What different sources said
- arXiv cs.AICenter
AGENTSERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving
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