AI RESEARCH
HiveMind: OS-Inspired Scheduling for Concurrent LLM Agent Workloads
arXiv CS.AI
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ArXi:2604.17111v1 Announce Type: cross 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.