DEVOPS
Weekly Noisy-Alert Digest from Axiom Log Trends
Each week, queries Axiom for the alert and error events from the past seven nights, ranks the loudest and most repetitive sources.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires
- ActionQuery Axiom for overnight alert eventsAxiom
- LogicAggregate and rank noisiest sources
- ActionWrite tuning digest with LLMOpenAI
- OutputFile ranked triage issue in LinearLinear
What it does
This workflow turns a week of overnight alert spam into an actionable cleanup list. It queries Axiom for alert-related log events during off-hours, counts which monitors and services fired most often, identifies repeat offenders that auto-resolved without action, and ranks them by interruption cost. The result is filed as a Linear issue for the team to actually tune the thresholds.
When to use it
Use it when on-call engineers are getting paged repeatedly by the same low-value alerts and that noise never makes it onto the backlog. It converts alert fatigue into a tracked, prioritized engineering task each week.
How it works
- 1A weekly schedule fires (e.g. Monday morning).
- 2Query Axiom for alert and error events across the previous seven overnight windows.
- 3A logic step aggregates by source, computes frequency and self-resolve rate, and keeps only the top offenders.
- 4An LLM writes a digest explaining which alerts are noisiest and suggests tuning actions.
- 5Create a Linear issue with the ranked list and recommendations assigned to the on-call rotation lead.
Set it up
What you configure once, before turning it on.
- 1Connect AxiomLog streams, queries, dashboards.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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