AI AGENTS
Cloud Cost Spike Root-Cause Memo Agent
When a daily cloud bill jumps past threshold, an agent pulls the spending breakdown, correlates it against deploys and infra changes from the same window.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily scheduled cost check
- ActionQuery yesterday's cost by service vs 7-day baselineSnowflake
- LogicSpike exceeds threshold?
- ActionPull infra metrics for spiking serviceDatadog
- ActionFetch merged MRs and deploys in windowGitLab
- ActionAgent ranks root cause and drafts memo
- OutputPost root-cause memo to SlackSlack
What it does
Watches your cloud spend and, the moment a day-over-day cost spike crosses your threshold, launches an investigation agent. It cross-references the spike against everything that shipped in the same window — code deploys, merged infra changes, and metric shifts — then posts a written root-cause memo naming the most likely culprit and the evidence behind it.
When to use it
Use this when finance or platform teams keep getting surprised by bill spikes and someone has to manually reconstruct "what changed yesterday" across three dashboards. It turns a 45-minute forensic scramble into a memo waiting in Slack before standup.
How it works
- 1A scheduled check queries Snowflake for yesterday's cost by service and compares it to the trailing 7-day baseline.
- 2If the increase clears your percentage threshold, the agent fires; otherwise the run ends quietly.
- 3The agent pulls Datadog infra metrics for the spiking service and the merged GitLab MRs and deploys from the same time window.
- 4It reasons over the correlated timeline to rank the most probable cause and drafts a structured memo with evidence links.
- 5The memo posts to your finance-ops Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect DatadogMetrics, traces, log search.
- 3Connect GitLabRepos, MRs, pipelines, registry.
- 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.
More AI Agents workflows
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Stale Doc-PR Chaser for Runbook Gaps
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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.