DATA OPS
Axiom Spike Root-Cause Agent Investigation
An agent-driven investigation that, on a flagged Axiom ingestion spike, correlates the offending service against recent GitHub deploys, drafts a root-cause narrative.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily agent investigation schedule
- ActionConfirm spike and pin top service in AxiomAxiom
- ActionPull recent commits/deploys for that serviceGitHub
- LogicCorrelate timing, rank suspect commit
- ActionDraft root cause, open Linear ticketLinear
- OutputPost root-cause narrative to SlackSlack
What it does
Goes past raw attribution: when a spike is detected, an agent pulls the top offending service from Axiom, looks at what shipped to that service around the spike's start in GitHub, and reasons about which change most likely caused the log explosion. It writes a plain-language root-cause draft and links the suspect commit in the ticket.
When to use it
Use it when knowing which service spiked isn't enough and you want a first-pass why, complete with a deploy correlation, so the owning engineer starts from a hypothesis rather than a blank page.
How it works
- 1A schedule triggers the agent after the prior day's ingestion lands.
- 2The agent queries Axiom to confirm a spike and pin the top offending service.
- 3It pulls recent GitHub commits and deploys touching that service near the spike onset.
- 4The agent correlates timing and change content to rank the most likely culprit commit.
- 5It drafts a root-cause narrative and opens a Linear ticket linking the suspect commit and Axiom evidence.
- 6It posts the narrative to Slack for the owning team to confirm or refute.
Set it up
What you configure once, before turning it on.
- 1Connect AxiomLog streams, queries, dashboards.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Connect SlackChannels, DMs, threads, mentions.
- 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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