DATA OPS

Agent-written morning warehouse health narrative to Notion

Every morning an agent pulls freshness, volume, and recent failure signals across BigQuery and Datadog, investigates anything off.

CategoryData Ops
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMorning cron starts agent run
  • ActionPull freshness and volume from BigQueryGoogle BigQueryBigQuery
  • ActionPull recent events and monitors from DatadogDatadogDatadog
  • LogicReason over signals and rank issues
  • OutputWrite health narrative to NotionNotionNotion

What it does

Produces a daily, human-readable state-of-the-warehouse report. An agent gathers table freshness and load-volume signals from BigQuery and recent monitor/event history from Datadog, reasons about which issues actually matter versus noise, and writes a Notion page with an overall verdict (Healthy / Degraded / Incident), the top issues ranked by impact, and suggested next steps.

When to use it

Use it when stakeholders want a morning briefing they can actually read, not a wall of threshold alerts. It complements the deterministic checkers by adding judgment and narrative across multiple signals.

How it works

  1. 1A morning cron starts the agent run.
  2. 2The agent queries BigQuery for current freshness and volume metrics on tracked tables.
  3. 3It pulls recent Datadog events and monitor states for the data services.
  4. 4It reasons over the combined signals, separating material issues from transient noise and ranking them.
  5. 5It writes a formatted health report with a verdict and prioritized actions to a Notion page for the team.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect DatadogMetrics, traces, log search.
  3. 3
    Connect NotionPages, databases, comments.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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