vLLM publishes AgentX-based optimization study for real-world agent traffic

https://x.com/vllm_project/status/2097427310513426721

vLLM published a full-stack optimization pass for real-world agent traffic benchmarked on AgentX. Findings include: pipeline parallelism helps cold long prompts but loses on warm short turns; decode context parallelism depends strongly on the model attention stack; and session-sticky routing can beat naive load balancing because warm KV caches matter more than queue distribution in fast-turn agent settings.

Source: Latent Space $2026-09-09$.