Practice — Context Engineering Fundamentals (5 questions)

Your support bot has a 12,000-token practical per-turn budget, allocated roughly as: 1,200 system prompt + tool schemas (fixed), 4,000 retrieved policy content (capped, variable), 5,000 conversation history (capped, variable), 1,000 reserved for output, 800 margin. It has worked fine in testing.

In production, a customer with 300+ historical orders asks "what's the status of all my recent orders?" The list_orders tool returns all 300 orders as raw JSON, which alone is around 18,000 tokens — blowing past the entire budget before the model even sees the retrieved policy content or gets to generate an answer. The request fails with a context-length error.

1. Diagnose which region of the context budget was actually unprotected, and explain mechanistically why this wasn't caught by the budget table above.
2. Propose a fix for the list_orders tool integration specifically — not a bigger budget.
3. Separately, is raising the practical budget from 12,000 to, say, 40,000 tokens a reasonable partial mitigation here? Justify with the mechanisms from context engineering, not just "more room is safer."