AI AGENTS
Chat-Driven Incident Commander with Confirm-Before-Act
Responders type plain-English requests in the incident Slack channel; an agent maps each to a runbook step, restates the exact action it will take.
How it runs
The automated pipeline, trigger to output.
- TriggerMessage posted in incident Slack channelSlack
- ActionMatch request to runbook actionConfluence
- LogicRestate command and await 'confirm' replySlack
- ActionExecute confirmed actionShell
- OutputPost output and timestamped log entrySlack
What it does
Gives the incident channel a conversational operator. A responder types "restart the payments worker" and the agent translates it into the precise runbook action, echoes back exactly what it will run, and acts only after the person confirms. It keeps a running log of every action in the channel.
When to use it
Use this during active incidents when the team is moving fast in Slack and wants to delegate mechanical steps without leaving the conversation — while guaranteeing every change is read back and confirmed before it happens.
How it works
- 1A message in the designated incident Slack channel triggers the agent.
- 2The agent interprets the request and matches it to a known runbook action.
- 3A logic gate restates the resolved command and asks the responder to reply "confirm".
- 4On confirmation, the agent executes the action via shell.
- 5If the request is ambiguous or has no matching runbook, it asks a clarifying question instead.
- 6It posts the command output and a timestamped entry to the channel log.
Set it up
What you configure once, before turning it on.
- 1Connect SlackChannels, DMs, threads, mentions.
- 2Connect ConfluenceSpaces, pages, blueprints.
- 3Connect ShellRun sandboxed commands inside the workspace.
- 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.
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