DATA OPS
Agent-Driven Freshness Breach Triage with Root-Cause Note in Linear
When a source breaches its freshness SLA, an agent investigates the likely cause across recent loads and logs, drafts a plain-English root-cause summary.
How it runs
The automated pipeline, trigger to output.
- TriggerWebhook: freshness SLA breach detectedHTTP webhook
- ActionPull source load history + row-count trend from SnowflakeSnowflake
- ActionReview related pipeline error logs in DatadogDatadog
- LogicSynthesize likely root cause + next step
- ActionOpen Linear issue with root-cause noteLinear
- OutputPost triage summary + issue link to SlackSlack
What it does
Instead of just firing an alert, this agent-driven workflow does first-pass triage on a freshness breach. It pulls the source's recent load history and related error logs, reasons about the most likely cause (upstream extract failure, schema drift, volume drop), writes a concise root-cause hypothesis, files a Linear issue with that context attached, and shares the summary in Slack so the on-call starts with a head start rather than a blank page.
When to use it
Use it when freshness breaches recur and you want the investigation grunt work done before a human opens the ticket, especially across multiple sources with different failure modes.
How it works
- 1A webhook signals a detected freshness SLA breach for a source.
- 2The agent queries Snowflake for the source's recent load history and row-count trend.
- 3The agent reviews recent pipeline logs from Datadog for related errors.
- 4The agent synthesizes a likely root cause and a recommended next step.
- 5Create a Linear issue with the source, lateness, and root-cause note.
- 6Post the triage summary and issue link to Slack.
Set it up
What you configure once, before turning it on.
- 1Connect HTTP webhookTrigger any URL on agent actions.
- 2Connect SnowflakeWarehouses, queries, shares.
- 3Connect DatadogMetrics, traces, log search.
- 4Connect LinearIssues, projects, cycles, triage.
- 5Connect SlackChannels, DMs, threads, mentions.
- 6Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 7Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 8Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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