```json { "action": "tool_call", "reasoning": "The evidence shows this dataset has a mixed structure with 150 individual call records and various summary/metadata rows. I've seen that the 'Day' column contains dates (2026-07-17 through 2026-07-22), the 'Total Calls' column contains caller phone numbers for individual calls, and 'AI Handled' contains call intents. To identify natural segmentation opportunities, I need to examine the actual intent categories (which appear to be in the 'AI Handled' column based on the call records) and outcome types (which appear to be in the 'Human Handled' column based on the sample showing 'AI Resolved', 'Transferred', 'Voicemail'). Let me query the distinct values in these key columns to understand the natural groupings.", "tool": "query_sql", "args": { "sql": "SELECT \"AI Handled\" as intent, COUNT(*) as call_count FROM raw.callyeah_analytics_2026_07_23 WHERE \"Day\" LIKE 'CY-%' GROUP BY \"AI Handled\" ORDER BY call_count DESC", "purpose": "Get the distribution of call intents from individual call records to identify natural segmentation by intent type" } } ```