As LLM agents are deployed in long-horizon sessions, context accumulation drives up inference costs. Existing approaches utilize text pruning or dynamic memory eviction to minimize token footprints; however, their unconstrained sequence mutations alter layouts, introducing prefix mismatches and cache invalidation. This reveals a critical trade-off between text sparsity and prompt cache continuity. To address this, we present **TokenPilot**, a dual-granularity context management framework. Globally, *Ingestion-Aware Compaction* acts as a framework harness to stabilize prompt prefixes and eliminate open-world environmental noise at the ingestion gate. Locally, *Lifecycle-Aware Eviction* monitors the ongoing residual utility of context segments, enforcing a conservative batch-turn schedule to offload content segments only when task relevance expires. Experiments on ‘PinchBench‘ and ‘Claw-Eval‘ under both isolated and continuous modes demonstrate that TokenPilot reduces costs by **61%** and **56%** in isolated mode, and **61%** and **87%** in continuous mode, while maintaining competitive performance compared to prior systems.