{
    "action": "tool_call",
    "reasoning": "The categorical dimensions are all artificially balanced (company_size, experience_level, remote_ratio all ~25-33% each; industry and job_title evenly distributed 4.7-7.1%). To find natural groupings, I should examine the continuous measures. The salary distribution shows a wide range ($32k-$399k) with clear quartiles that could reveal natural tiers. Let me create salary bands and see if they correlate with other meaningful dimensions like experience level, job title, or industry to identify natural segments.",
    "tool": "query_sql",
    "args": {
        "sql": "WITH salary_bands AS (SELECT CASE WHEN salary_usd < 70000 THEN '1_Entry ($32k-$70k)' WHEN salary_usd < 100000 THEN '2_Mid ($70k-$100k)' WHEN salary_usd < 150000 THEN '3_Senior ($100k-$150k)' ELSE '4_Executive ($150k+)' END AS salary_band, experience_level, job_title, industry, years_experience, remote_ratio FROM raw.ai_job_dataset) SELECT salary_band, COUNT(*) AS job_count, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) AS pct, ROUND(AVG(years_experience), 1) AS avg_years_exp, COUNT(DISTINCT job_title) AS distinct_titles, COUNT(DISTINCT industry) AS distinct_industries, ROUND(100.0 * SUM(CASE WHEN remote_ratio = 100 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_fully_remote FROM salary_bands GROUP BY salary_band ORDER BY salary_band",
        "purpose": "Create natural salary-based segments and examine their characteristics (experience, role diversity, remote work patterns) to see if they form meaningful clusters",
        "source": "raw"
    }
}