AI AGENTS
Quarterly Remediation Closure Scorecard
Compiles a quarterly scorecard of how many incident remediations actually closed versus slipped, grades each team.
How it runs
The automated pipeline, trigger to output.
- TriggerQuarter-end schedule fires
- ActionPull all quarterly incident items and history from LinearLinear
- LogicAggregate per-team metrics and assign grades
- ActionPublish scorecard as a Confluence pageConfluence
- OutputPost ranked leadership summary to SlackSlack
What it does
Leadership wants one number: did we actually fix what last quarter's incidents told us to fix? This agent compiles a closure scorecard across all incident action items from the quarter, computes per-team completion rate, median time-to-close, and re-opened-laggard count, then grades each team. It publishes the full scorecard as a Confluence page and sends an executive summary to a leadership Slack channel so the reliability conversation is grounded in data, not anecdote.
When to use it
Run it at quarter close to drive the reliability review meeting and to hold teams accountable for follow-through, not just incident response.
How it works
A quarter-end schedule triggers the run. The agent pulls all incident action items from the quarter out of Linear, including their close dates and any re-open history. A logic step aggregates the metrics per team and assigns a letter grade by closure rate. The agent renders a scorecard and writes it as a new Confluence page under the reliability space, then posts a ranked leadership summary with the lowest-performing teams highlighted to Slack.
Set it up
What you configure once, before turning it on.
- 1Connect LinearIssues, projects, cycles, triage.
- 2Connect ConfluenceSpaces, pages, blueprints.
- 3Connect SlackChannels, DMs, threads, mentions.
- 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.