{
    "action": "tool_call",
    "reasoning": "The evidence from earlier steps isn't visible to me, so I'll recompute the correlations in one query. Text columns are cleaned by extracting the numeric part, and the result then backs the final answer.",
    "tool": "query_sql",
    "args": {
        "sql": "WITH d AS (SELECT TRY_CAST(NULLIF(regexp_extract(Age,'-?[0-9]+\\.?[0-9]*',0),'') AS DOUBLE) AS age, TRY_CAST(NULLIF(regexp_extract(Monthly_Income,'-?[0-9]+\\.?[0-9]*',0),'') AS DOUBLE) AS income, TRY_CAST(NULLIF(regexp_extract(AI_Usage_Hours_Per_Day,'-?[0-9]+\\.?[0-9]*',0),'') AS DOUBLE) AS usage_hrs, TRY_CAST(NULLIF(regexp_extract(Monthly_AI_Cost,'-?[0-9]+\\.?[0-9]*',0),'') AS DOUBLE) AS cost, Productivity_Score AS prod, Accuracy_Rating AS acc, Satisfaction_Score AS sat, TRY_CAST(NULLIF(regexp_extract(Time_Saved_Hours_Per_Week,'-?[0-9]+\\.?[0-9]*',0),'') AS DOUBLE) AS saved, TRY_CAST(NULLIF(regexp_extract(Work_or_Study_Hours_Per_Day,'-?[0-9]+\\.?[0-9]*',0),'') AS DOUBLE) AS work FROM raw.AI_Usage_and_Impact_on_Students_and_Professionals) SELECT COUNT(*) AS n, corr(usage_hrs,saved) AS usage_saved, corr(prod,saved) AS prod_saved, corr(usage_hrs,prod) AS usage_prod, corr(prod,sat) AS prod_sat, corr(acc,sat) AS acc_sat, corr(sat,saved) AS sat_saved, corr(usage_hrs,sat) AS usage_sat, corr(age,work) AS age_work, corr(age,saved) AS age_saved, corr(age,sat) AS age_sat, corr(cost,prod) AS cost_prod, corr(cost,sat) AS cost_sat, corr(cost,saved) AS cost_saved, corr(cost,usage_hrs) AS cost_usage, corr(income,prod) AS income_prod, corr(income,sat) AS income_sat, corr(income,cost) AS income_cost FROM d",
        "purpose": "Recompute key Pearson correlations across numeric columns after cleaning text values",
        "source": "raw"
    }
}