CAFE(S): Your Agent Is Only as Good as Its Context

In this paper, we propose CAFE(S), a framework for describing the quality of context assembled for an AI system. CAFE(S) is organized around five dimensions: Clarity, Actionability, Fidelity, Efficiency, and Security. It does not prescribe a retrieval architecture or a particular method of context engineering. Instead, it provides a shared language for asking whether the resulting context gives a human-agent system the conditions it needs to work effectively. The parenthetical "S" reflects that Security complements the other four dimensions by asking a different question about the same context. We return to that distinction later.
CAFE(S) is deliberately a definition for high quality context; it is not a measurement system. Our goal is to describe these properties clearly enough that teams can discuss them, review context for them, and intervene when they are missing. A clearer definition may also create the foundation for future work to measure context quality and examine its relationship to agent performance, developer experience, and software outcomes.