AI AGENTS
Log-Noise Suppression Rule PR Author
On a nightly schedule, an agent finds the highest-volume noisy log patterns in Axiom and opens a GitHub pull request that adds suppression rules or downgrades log levels…
How it runs
The automated pipeline, trigger to output.
- TriggerNightly schedule
- ActionQuery Axiom top log patterns by volumeAxiom
- LogicFilter to high-volume benign patterns
- ActionDraft source edits and open GitHub PRGitHub
- OutputPost PR link to Slack for reviewSlack
What it does
This agent looks at which log patterns dominated Axiom volume over the last day, identifies the ones that carry no operational value, and writes an actual pull request that suppresses them at the source: dropping a log line, lowering its level, or adding an ingestion filter. It turns recurring noise into a reviewable code change rather than a manual chore.
When to use it
Use it when log volume is driving cost or drowning real signal and the fix is in the code, not the dashboard. It keeps the suppression work flowing as small, auditable PRs instead of letting noise accumulate.
How it works
- 1A nightly schedule triggers the run.
- 2The agent queries Axiom for the top log patterns by volume and their source files where available.
- 3A logic step filters to patterns above a volume threshold that match known-benign shapes (health checks, retries, expected 404s).
- 4The agent locates the emitting code, drafts the edits, and opens a GitHub PR with a summary of expected volume reduction.
- 5It posts the PR link to Slack for a reviewer to approve.
Set it up
What you configure once, before turning it on.
- 1Connect AxiomLog streams, queries, dashboards.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Connect OpenAIModels, embeddings, files.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, 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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