I need to explore the dataset systematically to find surprising or counterintuitive patterns. Let me start by examining survival rates across different dimensions to identify unexpected relationships. {"action": "tool_call", "reasoning": "First, I'll look at overall survival rates and basic demographic breakdowns to establish baseline expectations, then search for patterns that contradict common assumptions about the Titanic disaster.", "tool": "query_sql", "args": {"sql": "SELECT COUNT(*) as total_passengers, SUM(Survived) as survived, ROUND(100.0 * SUM(Survived) / COUNT(*), 1) as survival_rate_pct, SUM(CASE WHEN Sex = 'male' THEN 1 ELSE 0 END) as males, SUM(CASE WHEN Sex = 'female' THEN 1 ELSE 0 END) as females, SUM(CASE WHEN Sex = 'male' AND Survived = 1 THEN 1 ELSE 0 END) as males_survived, SUM(CASE WHEN Sex = 'female' AND Survived = 1 THEN 1 ELSE 0 END) as females_survived FROM raw.titanic", "purpose": "Get overall survival statistics and gender breakdown to establish baseline expectations", "source": "raw"}}