Why does my background agent starve the user-facing one sharing the same infrastructure?

When multiple AI agents run concurrently, some user-facing and some background, they compete for LLM inference capacity, context windows, and API rate limits with no priority ordering. Standard infrastructure schedulers are CPU and memory-aware but do not understand LLM workload semantics: which requests are mid-flight tool chains, which have irreversible side effects, and which can safely be preempted and resumed. The result is that a runaway background job starves a latency-sensitive user query and the only fix today is manual rate-limit tuning per deployment. The HiveMind paper proposes an OS-inspired approach but it is a research prototype with no production adoption and no standard interface for agent frameworks to build against.