AI AGENTS
Cost-Anomaly Evidence Bundle Builder
Deterministically collects the cost breakdown, deploy log, and infra metrics for any detected spike into a single timestamped evidence record stored in Snowflake for later audit…
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled baseline scan
- LogicFilter services exceeding baselineSnowflake
- ActionPull infra metrics for windowDatadog
- ActionFetch deploys and MRs for windowGitLab
- OutputWrite timestamped evidence record to SnowflakeSnowflake
What it does
Builds a durable, queryable record for every cost anomaly. Rather than reasoning about a cause, this pipeline reliably gathers the three inputs an investigation needs — the cost breakdown, the deploy and MR log, and the relevant infra metrics for the window — normalizes them, and writes one timestamped evidence row back to Snowflake. Later anomalies and audits can query a clean history instead of re-pulling raw APIs.
When to use it
Use this as the data-collection backbone under your cost investigations, or when finance needs an auditable trail of what was happening during each spike. It is deterministic by design, so the same anomaly always produces the same bundle.
How it works
- 1A schedule scans Snowflake for any service exceeding its cost baseline.
- 2For each flagged service, the pipeline pulls infra metrics for the anomaly window from Datadog.
- 3It fetches deploys and merged MRs in that window from GitLab.
- 4It normalizes all three sources into a single structured bundle.
- 5It writes the timestamped evidence record back to a Snowflake table for audit and downstream agents.
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.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, 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.

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