CacheScout Optimizes LLM Serving for Multi-Agent Systems

The deployment of multi-agent Large Language Model (LLM) systems, where user requests are broken down into tasks for specialized agents, presents unique challenges for efficient serving. A key issue is the repeated execution of fixed contexts (system prompts, tool definitions, few-shot examples) by each agent, leading to substantial opportunities for KV-cache reuse. However, existing LLM serving systems typically manage KV-cache reactively, often evicting reusable agent contexts before their next invocation, forcing redundant recomputations. To address this, researchers developed CacheScout, an agent-aware KV-cache runtime layer. CacheScout's core innovation is its ability to learn agent execution semantics online, without requiring predefined workflow graphs or offline training. This learned execution model then guides both cache eviction and proactive prefetching, all while keeping the serving critical path unchanged. Implemented on top of vLLM, CacheScout demonstrated significant performance improvements across real-world multi-agent workloads, boosting KV-cache hit rates by 10-18 percentage points, reducing mean Time-To-First-Token (TTFT) by 18-45%, lowering per-turn latency by 29-38%, and increasing peak throughput by up to 57%. These benefits also scaled to larger models, further reducing TTFT and sustaining higher throughput.