Why does my background agent starve the user-facing one sharing the same infrastructure?
Opportunity
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.
Why it matters
An agent-aware workload scheduler is the missing infrastructure layer that makes multi-tenant LLM deployments as predictable as any other multi-process system.
How I score the opportunity
The Opportunity Score is my own read, not a measurement: how much it hurts, how often it bites, and how little exists to solve it today. Higher means I think it is more worth building.
How much pain it causes when it shows up.
How often people actually run into it.
How little good tooling exists for it today.
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