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Paper · arXiv

HiveMind: OS-Inspired Scheduling for Concurrent LLM Agent Workloads

When multiple LLM coding agents share a rate-limited API endpoint, they exhibit resource contention patterns analogous to unscheduled OS processes competing for CPU, memory, and I/O. In a motivating incident, 3 of 11 parallel agents died from connection resets and HTTP 502 errors - a 27% failure rate - despite the API having sufficient aggregate capacity to serve all 11 sequentially. We present HIVEMIND, a transparent HTTP proxy that applies five OS-inspired scheduling primitives - admission control, rate-limit tracking, AIMD backpressure with circuit breaking, token budget management, and priority queuing - to eliminate the failure modes caused by uncoordinated parallel execution. The pr...

AuthorsJustice Owusu Agyemang, Jerry John Kponyo, Obed Kwasi Somuah et al.
Primary categorycs.DC
Published18 April 2026
Last updated at source18 April 2026
cs.DCcs.AI
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