AI AGENTS
Axiom Log-Noise Clusters to BigQuery Tracking
Daily, a deterministic pipeline pulls Axiom log volume, clusters it by message template.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule triggers the pipeline
- ActionQuery prior-day event volume from Axiom by templateAxiom
- LogicNormalize and merge near-identical patterns
- LogicScore clusters by volume, cost, projected savings
- ActionAppend ranked rows to BigQuery noise tableBigQuery
- OutputPost top-offenders summary to SlackSlack
What it does
This is a deterministic data pipeline that turns raw Axiom log volume into a queryable noise-tracking table. Each day it pulls event counts, clusters them by message template, computes volume, cost, and a noise score per cluster, and appends the ranked results plus a projected-savings field to a BigQuery table so you can chart which patterns are growing and prioritize cleanup.
When to use it
Use it when you want data, not actions: a historical record of log-noise offenders to drive dashboards, quarterly cost reviews, or to feed a separate remediation workflow. No PRs are opened here.
How it works
- 1A daily schedule triggers the pipeline.
- 2It queries Axiom for the prior day's event volume aggregated by message template.
- 3A clustering step normalizes templates and merges near-identical patterns.
- 4A scoring step computes volume, estimated cost, and a noise rank per cluster with projected savings if dropped.
- 5It appends the ranked rows to a BigQuery table keyed by date.
- 6It posts a short top-offenders summary to Slack with a link to the warehouse table.
Set it up
What you configure once, before turning it on.
- 1Connect AxiomLog streams, queries, dashboards.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Connect SlackChannels, DMs, threads, mentions.
- 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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Run this workflow in your colony.
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