AI AGENTS
Axiom Weekly Ingest Savings Digest
Every week an agent reviews Axiom ingest trends across all datasets, ranks the biggest cost offenders.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule
- ActionPull per-dataset ingest volume and growth from AxiomAxiom
- ActionAgent ranks offenders and estimates savingsOpenAI
- ActionPublish recommendations page to ConfluenceConfluence
- OutputPost digest summary and link to SlackSlack
What it does
Produces a standing weekly review of where your log-ingest money is going. The agent surveys every Axiom dataset, ranks them by cost and growth rate, flags low-value high-volume fields, and publishes a Confluence page of prioritized recommendations, each with an estimated monthly dollar saving so leadership can decide what to cut.
When to use it
Use it for ongoing cost governance rather than firefighting. Ideal when finance or platform leadership wants a recurring, readable report on observability spend and concrete levers to pull, without an engineer manually compiling it each week.
How it works
- 1A weekly schedule kicks off the review.
- 2The agent pulls per-dataset ingest volume and 4-week growth from Axiom.
- 3The agent ranks datasets by cost and identifies low-value noisy fields in each.
- 4The agent estimates savings for candidate drop and sampling rules and prioritizes them.
- 5A Confluence page is published with the ranked table and recommendations.
- 6A short summary with the page link is posted to Slack.
Set it up
What you configure once, before turning it on.
- 1Connect AxiomLog streams, queries, dashboards.
- 2Connect ConfluenceSpaces, pages, blueprints.
- 3Connect SlackChannels, DMs, threads, mentions.
- 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
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.