The Context Window Trap in Enterprise AI: Designing Memory, Reset, and Retrieval Boundaries

This article examines the 'context window trap' in enterprise AI, arguing that context windows are temporary working sets, not durable memory. It highlights risks like context overload, stale assumptions, and cross-task contamination, which can lead to confident but poorly bounded outputs. The author proposes a context authority model with explicit rules for what enters context, what is retrieved, what is remembered, and when to reset. Using a troubleshooting scenario, it shows how context boundary failures can cause unsafe recommendations. The piece emphasizes the need for deliberate architecture over implicit context decisions for auditability and governance.