Teams grow their always-loaded instruction file (system prompt, CLAUDE.md-style rules) by reflex: whenever an agent slips, append one more rule. On a long session that whole file sits in the cached prefix and is re-read on every single turn, and past a point each extra rule measurably lowers the model's compliance with the others - so a rule with positive standalone value can be net-negative. Treat the file as a capacity-constrained assortment: there is a provable cap on its optimal size, adding a rule needs to clear an evidence threshold, and you cannot safely delete 'ignored' rules because the feedback is censored.
Curate Always-Loaded Context Like an Assortment: One More Rule Can Make the Whole File Worse
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