AI AGENTS
Datadog Anomaly to Proposed Rollback (Approval-Gated)
On a Datadog error-rate or latency alert, an agent correlates the spike to the most recent deploy and proposes a rollback.
How it runs
The automated pipeline, trigger to output.
- TriggerDatadog monitor alertDatadog
- ActionQuery recent GitHub deploys/mergesGitHub
- LogicCorrelate spike to a suspect deploy
- ActionAgent drafts rollback proposal
- OutputPost proposal to Slack for approvalSlack
- ActionOn approval, revert via VercelVercel
What it does
When a Datadog monitor trips on elevated errors or latency, the agent cross-references recent deployments to identify the likely culprit release. It assembles a rollback proposal naming the suspect commit, the regression evidence, and the exact revert it would perform, then waits for human sign-off before touching production.
When to use it
Ideal for teams that deploy frequently and want a fast "is this the deploy?" answer during an incident, without granting automation the authority to roll back on its own. The operator gets a one-click decision instead of a cold-start investigation.
How it works
- 1A Datadog monitor alert triggers the flow with the metric, service, and timestamp.
- 2The agent queries GitHub for deploys and merges in the window before the spike.
- 3A logic step decides whether a single recent deploy correlates strongly enough to suspect.
- 4The agent drafts a rollback proposal with the suspect commit and evidence.
- 5The proposal is posted to Slack with an Approve button.
- 6On approval, a Vercel action triggers the revert and reports the new deployment status.
Set it up
What you configure once, before turning it on.
- 1Connect DatadogMetrics, traces, log search.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Connect VercelDeploys, runtime logs, analytics.
- 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.