LLM agents (AI assistants that perform multi-step tasks) reuse previously computed data from a cache to speed up responses. Under heavy load, a busy period can evict cached data, making the cache almost useless. Even after the busy period ends, the cache may never recover, causing the system to stay slow. The authors show this collapse happened in 9 out of 12 test runs, cutting throughput in half and cache hit ratio to 10%. They found that simply refusing new users did not fix it, but temporarily pausing existing active sessions restored the cache within minutes. This matters because production AI agents must survive brief load spikes without getting permanently stuck.
AI agent servers can stay overloaded after traffic bursts end
The authors find that lost conversation caches can sustain a slowdown after demand returns to normal, while pausing active sessions restores service more reliably than restricting new arrivals.
Independent
Wen Huang · Jack Liu
Research Digest··3 min read
In one line
A transient burst can permanently destroy prefix-cache hit rates and throughput in LLM agent serving.
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Two ideas this paper builds onRead it as
Wen Huang and Jack Liu show that a brief traffic surge can leave an AI agent server persistently degraded, even at an arrival rate it previously handled comfortably.
The authors compared server runs with identical underlying session arrivals, adding a few minutes of extra traffic to one run in each pair.
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