A context engineer is building a Databricks agent for a retail inventory management system. The system prompt and tool descriptions consume 1,800 tokens of a 4,096-token context window, and the engineer reserves 1,000 tokens for the user turn and model response. The remaining tokens are used for few-shot examples, each costing approximately 250 tokens, so the engineer includes exactly five examples. The agent uses three tools: inventory_lookup, reorder_check, and supplier_lead_time. Six candidate examples are available: Example 1: A warehouse manager asks whether a depleted SKU needs reordering; the agent calls inventory_lookup, confirms depletion, calls reorder_check, and returns a binary reorder decision. Example 2: A procurement officer asks when a low-stock item can be replenished; the agent calls reorder_check, calls supplier_lead_time, and returns a replenishment recommendation. Example 3: A store associate asks for the current stock count of a single product; the agent calls inventory_lookup and returns a scalar quantity. Example 4: A store associate asks for the current stock count of a single product; the agent calls inventory_lookup and returns a structured status object containing the quantity and a restocking flag. Example 5: A store associate asks whether a forecasted shortfall requires immediate action; the agent calls inventory_lookup, compares against minimums, calls reorder_check, and returns a binary reorder decision. Example 6: A logistics coordinator asks how long a specific supplier takes to fulfill an order; the agent calls supplier_lead_time and returns a scalar lead-time value. Which example should the engineer exclude to preserve the token budget while maximizing behavioral coverage?
