I need to identify the time dimension and analyze it for spikes, dips, or breaks. From the evidence, I can see multiple temporal columns across different tables. Let me start by examining the most promising candidates - the `batches` table which appears to track processing runs. {"action": "tool_call", "reasoning": "The batches table has both started_at and completed_at temporal columns and appears to be a core operational table. I'll analyze the distribution of batches over time to identify spikes, dips, or breaks in activity.", "tool": "query_sql", "args": {"sql": "SELECT DATE_TRUNC('day', CAST(started_at AS TIMESTAMP)) as day, COUNT(*) as batch_count, COUNT(DISTINCT tenant_id) as tenant_count FROM raw.batches WHERE started_at IS NOT NULL GROUP BY day ORDER BY day", "purpose": "Analyze daily batch activity over time to identify temporal patterns, spikes, and dips", "source": "raw"}}