LLM agents routinely spend orders of magnitude more tokens on context than on generation, re-sending interaction history, tool schemas, and system prompts at every turn. We introduce **SCOPE**, a unified framework for *context compilation* that compresses these three strata jointly under a fixed token budget while preserving agent behavior. SCOPE combines: 1. A learned *minimal sufficient context* reranker for memory, trained via leave-one-out supervision; 2. A plan-conditioned tool compressor that speculatively loads likely tools while retaining lightweight signatures for the rest; 3. A constraint-aware prompt compressor that preserves instructions. A lightweight planner coordinates these components via a contextual bandit, allocating budget across memory, tools, and prompt to maximize fidelity to full-context execution. On 3142 chat trajectories, SCOPE achieves **96.8%** decision fidelity at **4.46×** compression (60K tokens/turn), matching full-context task success within **0.9%** while using only **22%** of the tokens. Across operating regimes, SCOPE Pareto-dominates strong single-stratum baselines and naive compositions, with gains exceeding **11%** at matched cost. Our results show that optimizing context in isolation is insufficient: coordinated, budget-aware compression is key to scaling LLM agents without sacrificing reliability.