Context engineering is the practice of deliberately managing what enters an AI agent's context window to keep it reliable, cost-efficient, and accurate in production. Key practices include treating the context window like RAM with finite budget, separating static system instructions from dynamic per-turn content to enable prefix caching, managing conversation history through rolling summarization or anchored state documents instead of naive accumulation, and designing retrieval as a budgeted operation with post-retrieval filtering and agent-controlled triggering. Token budgeting should target 60–80% utilization across full agent loops, prioritizing trimming of tool outputs. Production evaluation should use probe-based tests (recall, artifact, and continuation probes) and track metrics like context utilization rate, compression ratio, retrieval precision, and context drift in long-running sessions.
Table of contents
IntroductionTreating the Context Window as a Constrained ResourceMapping What Fills the Context WindowSeparating Static from Dynamic ContextManaging Conversation HistoryDesigning Retrieval as a Budget DecisionBudgeting Tokens Across the Full Agent LoopEvaluating Context Quality in ProductionWrapping UpSort: