Why does my background agent starve the user-facing one sharing the same infrastructure?
機会
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.
重要な理由
An agent-aware workload scheduler is the missing infrastructure layer that makes multi-tenant LLM deployments as predictable as any other multi-process system.
機会をどう評価するか
Opportunity Scoreは測定値ではなく、私自身の見解です。どれほど痛みを伴うか、どれほど頻繁に影響を与えるか、そして今日時点で解決策がいかに少ないか。スコアが高いほど、構築する価値が高いと私は考えています。
それが現れたときにどれほどの痛みをもたらすか。
実際にどれほど頻繁に人々がそれに直面するか。
今日時点で、それに対する優れたツールがいかに少ないか。