I need to explore this dataset systematically to find surprising patterns. Let me start by understanding the sales pipeline performance and then look for counterintuitive relationships. {"action": "tool_call", "reasoning": "First, I'll examine the overall deal stage distribution and success rates to establish baseline expectations for what 'normal' looks like in this sales pipeline.", "tool": "query_sql", "args": {"sql": "SELECT deal_stage, COUNT(*) as opportunity_count, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) as pct_of_total, ROUND(AVG(close_value), 0) as avg_deal_value, ROUND(SUM(close_value) / 1000000.0, 2) as total_value_millions FROM raw.sales_pipeline GROUP BY deal_stage ORDER BY opportunity_count DESC", "purpose": "Understand the distribution of deals across stages and their values to establish baseline expectations", "source": "raw"}}