AI AGENTS
Deploy-Failure Rollback Proposal with Two-Step Guardrail
A deploy-failure webhook triggers an agent that compares the failed release to the last known-good one, proposes a targeted rollback.
How it runs
The automated pipeline, trigger to output.
- TriggerDeploy-failure webhook receivedHTTP webhook
- ActionQuery GitHub for recent release tagsGitHub
- ActionAgent identifies last known-good and drafts rationale
- LogicRequire typed version confirmation in SlackSlack
- ActionTrigger rollback on matching confirmationVercel
- OutputPost confirmation and incident linkSlack
What it does
Reacts to a failed or degraded deploy by proposing a precise rollback rather than executing one blindly. The agent identifies the last healthy release, explains why the current one looks bad, and asks the engineer to confirm by typing the target version — a deliberate guardrail against fat-finger reverts.
When to use it
Use this when a bad deploy needs a fast, *correct* rollback under pressure. The two-step confirmation — propose, then type the version to commit — prevents reverting to the wrong release while still letting you move in seconds.
How it works
- 1A deploy-failure webhook from your CI/CD posts to the workflow.
- 2The agent reads the failing release metadata and queries GitHub for recent release tags.
- 3It identifies the last known-good version and drafts a rollback rationale.
- 4A logic gate posts the proposal to Slack and requires the engineer to reply with the exact version string.
- 5On a matching reply, the agent triggers the rollback via the deploy provider.
- 6It posts confirmation, the reverted version, and a link to the incident record.
Set it up
What you configure once, before turning it on.
- 1Connect HTTP webhookTrigger any URL on agent actions.
- 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.