ENGINEERING
Quarterly Logging Hygiene Audit Agent
An agent-driven quarterly sweep that surveys all Axiom datasets, builds a logging-hygiene scorecard per service.
How it runs
The automated pipeline, trigger to output.
- TriggerQuarterly schedule launches agent
- ActionEnumerate and sample Axiom datasetsAxiom
- LogicAgent scores hygiene and groups fixes
- ActionOpen grouped per-service GitHub PRsGitHub
- OutputPublish Confluence audit scorecardConfluence
What it does
Runs a deep, reasoning-driven audit of the entire logging estate rather than reacting to one spike. The agent profiles every dataset, judges which streams are over-logging by intent (not just volume), and proposes a coordinated set of fixes across services with a written rationale.
When to use it
Use it quarterly or before a cost review when you want a thorough, defensible cleanup plan instead of incremental tweaks. Suited to orgs with many services and no single owner for logging standards.
How it works
- 1A quarterly schedule launches the audit agent.
- 2The agent enumerates Axiom datasets and samples message templates, volume, and retention for each.
- 3It reasons about each service's logging hygiene, scoring signal-to-noise and flagging anti-patterns like logging in hot loops.
- 4It groups related fixes and opens GitHub PRs per service that adjust levels and sampling.
- 5It publishes a Confluence scorecard summarizing findings, proposed retention changes, and links to every PR.
Set it up
What you configure once, before turning it on.
- 1Connect AxiomLog streams, queries, dashboards.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Connect ConfluenceSpaces, pages, blueprints.
- 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.
More Engineering workflows
Agent reviews model-license fit and suggests compliant swaps on the PR
When a PR adds a Hugging Face model, an agent reads the model card and license, judges fit against your commercial-use policy.
Block PRs that add incompatible Hugging Face model licenses
When a pull request adds or bumps a Hugging Face model dependency, it fetches the model card license, checks it against your org's allowed-license policy.
Post-Merge Log Volume Recheck After Downsampling PR
After a log-level PR merges, waits a day then re-queries Axiom to confirm the targeted stream's volume actually dropped.
Axiom Ingest Cost Spike to Linear Triage Ticket
When Axiom ingest volume spikes beyond its baseline, identifies which service caused it and files a Linear ticket with the offending log stream, sample lines, and a downsampling…
File a Linear license-review ticket for risky model adds
When a PR introduces a Hugging Face model with a non-permissive or unknown license, it opens a Linear issue assigned to the legal-review team with the model, license.
Warn the engineer in Slack when a model license is non-commercial
On a PR that adds a Hugging Face model, it checks the license and, if it is non-commercial or research-only, sends the PR author a direct Slack message explaining the restriction…
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.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.