ENGINEERING

Axiom Noisy Log Stream Detector with Auto-PR

Each morning scans Axiom for log streams with abnormally high event volume and low diagnostic value, then opens a GitHub PR that raises their log level from debug/info to warn.

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule fires
  • ActionQuery Axiom for per-stream event volumeAxiom
  • LogicKeep high-volume low-signal streams
  • ActionLocate emitting log call sites in repoGitHubGitHub
  • OutputOpen PR raising log levelsGitHubGitHub

What it does

Finds the log lines that are drowning out everything useful and quiets them at the source. It ranks Axiom datasets by event count, flags the chatty ones whose messages are repetitive boilerplate (heartbeats, polling ticks, cache hits), and drafts a code change that bumps those loggers to a higher level.

When to use it

Run it when your Axiom bill is climbing, dashboards are sluggish, or on-call keeps scrolling past walls of identical lines. Best as a scheduled daily hygiene pass so noise never compounds.

How it works

  1. 1A daily schedule fires the workflow.
  2. 2It queries Axiom for per-dataset and per-message-template event counts over the last 24 hours.
  3. 3A logic step keeps only streams above the volume threshold whose top message templates carry low signal (low cardinality, no error keywords).
  4. 4For each flagged logger it locates the emitting call site in the GitHub repo and rewrites the level (e.g. `logger.info` to `logger.debug`).
  5. 5It opens a GitHub pull request with the before/after volume estimate and a per-logger diff for review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AxiomLog streams, queries, dashboards.
  2. 2
    Connect GitHubRepos, issues, pull requests, actions.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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