AI AGENTS
Axiom Drop-Rule Merge Savings Verifier
When a logging-config drop-rule PR merges in GitHub, an agent waits a set window, measures the actual Axiom volume drop for the affected patterns.
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub webhook fires on logging-config PR mergeGitHub
- LogicParse PR for changed patterns and projected savings
- ActionQuery Axiom before/after volume for affected patternsAxiom
- LogicCompute realized savings, flag underperformers
- OutputPost realized-vs-projected report to SlackSlack
What it does
This agent closes the loop on log-noise cleanup by verifying that merged rules actually worked. When a logging-config PR carrying drop or sampling rules merges, it waits a measurement window, then compares the affected patterns' Axiom volume before and after to compute realized savings, and reports them against the projection the PR claimed.
When to use it
Use it to keep your noise-reduction program honest: confirm rules deployed correctly, catch rules that silently did nothing, and build trust in projected-savings numbers from your proposal workflows.
How it works
- 1A GitHub webhook fires when a PR labeled as a logging-config change merges.
- 2The agent parses the PR to extract which patterns and rules changed and the projected savings.
- 3It schedules a wait so the change has time to deploy and accumulate post-change data.
- 4It queries Axiom for the affected patterns across matched before and after windows.
- 5A logic step computes realized volume reduction and flags any rule whose drop is far below projection.
- 6It posts a realized-versus-projected savings report to Slack, calling out any underperforming rules to revisit.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect AxiomLog streams, queries, dashboards.
- 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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