AI AGENTS
On-Call Slack Command: Diagnose and Propose Runbook Action
An engineer types a symptom in Slack; the agent runs read-only diagnostics, picks the matching runbook.
How it runs
The automated pipeline, trigger to output.
- TriggerSlack command/mention from on-callSlack
- ActionRun read-only diagnostic checksShell
- ActionSelect matching runbook from NotionNotion
- LogicGate on applicability confidence
- OutputPropose steps in-thread, await approvalSlack
- ActionOn approval, execute via shellShell
What it does
Gives on-call a conversational entry point to the runbook library. An engineer describes a symptom in a Slack channel, the agent gathers read-only diagnostics, selects the best-matching runbook, and replies with a recommended action plan. The engineer approves in the same thread before anything runs.
When to use it
Use it when incidents are reported by humans rather than monitors, or when you want a guided assistant during a war-room. It keeps the operator in the driver's seat while removing the scramble to find and interpret the right runbook under pressure.
How it works
- 1A Slack slash command or mention triggers the flow with the engineer's description.
- 2The agent runs read-only diagnostic shell checks to gather current system state.
- 3It selects the matching runbook from Notion and grades its applicability.
- 4A logic step decides whether confidence is high enough to propose action or to ask for more detail.
- 5The agent posts the proposed steps in-thread with an Approve control.
- 6On approval, the shell action runs the steps and posts results back to the thread.
Set it up
What you configure once, before turning it on.
- 1Connect SlackChannels, DMs, threads, mentions.
- 2Connect ShellRun sandboxed commands inside the workspace.
- 3Connect NotionPages, databases, comments.
- 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.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.