{
    "action": "tool_call",
    "reasoning": "The evidence shows llm_calls has a created_at timestamp (step-0) and a gold table llm_calls_by_created_at__yyyy_mm_dd_hh exists (step-2). Step-5 already queried it and returned hourly aggregates with row_count, token usage, cost, and latency. I can see the data spans June 7-17, 2026, with varying activity levels. To identify notable spikes, dips, and breaks, I need to look at the full time series more systematically — the step-5 result shows 50 rows but is truncated. I'll query the gold table to get the complete hourly series with key metrics (row_count, cost, latency) to spot anomalies.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT created_at__yyyy_mm_dd_hh, row_count, cost_usd__sum, latency_ms__avg FROM gold.llm_calls_by_created_at__yyyy_mm_dd_hh ORDER BY created_at__yyyy_mm_dd_hh",
        "purpose": "Get complete hourly time series of LLM call volume, cost, and latency to identify spikes, dips, and breaks",
        "source": "gold"
    }
}