When Does Context Management Improve Agent Efficiency? An Empirical Study on Speculative Prefetching and Context Isolation

LLM-based agent systems increasingly rely on long sequences of tool calls, making execution latency and context management important runtime challenges. We investigate whether operating-system-inspired runtime optimizations, including speculative tool prefetching and task-level context isolation, can improve execution efficiency in black-box LLM agents. Contrary to our initial hypothesis, these mechanisms do not consistently improve efficiency across all workloads. Experiments on five representative task structures show that context isolation can reduce token consumption by removing redundant accumulated history, but may increase execution latency when removed context contains reasoning-dependent information. Neither natural-language summaries nor structured execution-state representations recover this degradation, suggesting that the missing information cannot be explained solely by information compression and may instead reflect execution-relevant information embedded in the model's generated history. Across all evaluated scenarios, speculative prefetching provides no consistent improvement beyond context isolation alone. These findings suggest that runtime context management should adapt to task dependency rather than adopt context isolation as a universal optimization strategy.