AI AGENTS
On-Demand Cost Investigation from Slack
An engineer mentions a service and date in Slack and an agent investigates that exact spend window — correlating deploys and metrics.
How it runs
The automated pipeline, trigger to output.
- TriggerSlack request with service and date rangeSlack
- ActionParse and validate investigation window
- ActionQuery spend for service and windowSnowflake
- ActionPull deploys and infra metricsGitLab
- ActionAgent correlates and drafts summaryDatadog
- OutputReply in Slack thread with root causeSlack
What it does
Lets anyone investigate a cost question on demand without leaving Slack. An engineer triggers the workflow with a service name and a date range; the agent scopes its investigation to exactly that window, pulls the matching spend, deploys, and infra metrics, reasons over the timeline, and replies in the same thread with a concise root-cause summary and evidence links.
When to use it
Use this for the ad-hoc question that doesn't come from an alert — "why did the payments service cost double last Thursday?" It gives every engineer self-serve cost forensics instead of pinging the one person who knows the dashboards.
How it works
- 1A Slack trigger captures the request with the service name and date range.
- 2The agent parses the parameters and validates the window.
- 3It queries Snowflake for that service's spend across the window.
- 4It pulls GitLab deploys and Datadog metrics for the same range.
- 5It correlates the signals and drafts a root-cause summary.
- 6It replies in the original Slack thread with the findings and links.
Set it up
What you configure once, before turning it on.
- 1Connect SlackChannels, DMs, threads, mentions.
- 2Connect SnowflakeWarehouses, queries, shares.
- 3Connect GitLabRepos, MRs, pipelines, registry.
- 4Connect DatadogMetrics, traces, log search.
- 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.