AI AGENTS
Axiom Spike to Linear Remediation Ticket
On an Axiom budget-alert webhook, an agent diagnoses the offending service and opens a Linear issue containing the root cause, the exact log lines to suppress.
How it runs
The automated pipeline, trigger to output.
- TriggerAxiom budget-alert webhookHTTP webhook
- ActionQuery Axiom to isolate offending service and patternsAxiom
- LogicSkip if an open Linear issue already covers it
- ActionAgent drafts root cause and sampling configOpenAI
- OutputCreate Linear issue for the owning teamLinear
What it does
Turns a noisy Axiom budget alert into an actionable engineering ticket. When ingest crosses your budget threshold, an agent investigates which service is flooding the pipeline, identifies the specific log patterns responsible, and files a Linear issue that includes a proposed sampling configuration the owning team can review and merge.
When to use it
Use it when cost alerts currently die in a Slack channel and never become work. This routes the spike directly into your team's backlog with enough detail that the fix is mostly written for them.
How it works
- 1An Axiom monitor fires a webhook when ingest exceeds the budget threshold.
- 2The agent queries Axiom to isolate the top contributing service and log patterns.
- 3A logic step checks whether a recent open Linear issue already covers this dataset to avoid duplicates.
- 4The agent composes a root-cause summary plus a concrete drop/sampling config snippet.
- 5A Linear issue is created, tagged to the owning team, with the diagnosis and proposed config in the body.
Set it up
What you configure once, before turning it on.
- 1Connect AxiomLog streams, queries, dashboards.
- 2Connect LinearIssues, projects, cycles, triage.
- 3Connect HTTP webhookTrigger any URL on agent actions.
- 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.
More AI Agents workflows
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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.
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