AI AGENTS
Axiom Ingest-Spike Drop-Rule Responder
When Axiom ingest volume spikes past a threshold, an agent identifies the single noisiest new log pattern driving it and opens an emergency GitHub PR adding a targeted drop rule…
How it runs
The automated pipeline, trigger to output.
- TriggerWebhook fires on ingest-volume spikeHTTP webhook
- ActionDiff Axiom pattern frequency vs baseline for spike windowAxiom
- LogicConfirm top pattern is low-value and drives the spike
- ActionOpen urgent GitHub PR with targeted drop ruleGitHub
- OutputPage on-call in Slack with PR and offending patternSlack
What it does
This is a reactive agent that responds to sudden log-volume spikes. When a monitor signals that ingest has jumped past your threshold, it pinpoints the specific log pattern responsible for the surge and opens a fast, narrowly scoped GitHub PR adding a drop rule for just that pattern, so a deploy that started spewing noise does not blow your budget overnight.
When to use it
Use it when a bad deploy or misconfigured logger can flood Axiom between weekly reviews. It is the emergency brake to complement scheduled cleanup: fast, surgical, and reviewable.
How it works
- 1A webhook from your ingest-volume monitor triggers the run with the spike window.
- 2The agent queries Axiom for that window and diffs pattern frequency against the prior baseline to isolate the surging template.
- 3A logic gate confirms the top pattern is low-value and accounts for a meaningful share of the spike, otherwise it exits without changes.
- 4It drafts a tightly scoped drop rule matching only that pattern.
- 5It opens a GitHub PR adding the rule, labeled urgent, with the spike chart and attribution.
- 6It pages the on-call channel in Slack with the PR and the offending pattern.
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 HTTP webhookTrigger any URL on agent actions.
- 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.
More AI Agents workflows
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
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.