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Human compensation behaviors, such as clarifying or redirecting agents, serve as diagnostic indicators of context quality failures. Instrumenting these behaviors can reveal which specific dimensions of context are lacking.

Impact: Tracking human-agent interaction patterns enables data-driven improvements to context quality, reducing reliance on intuition for debugging agent performance.

— from CAFES Framework: Context Quality as AI Bottleneck · Engineering Enablement by DX· Oct 16, 2026