AI AGENTS
SLO-Backed Remediation Verifier
For each closed incident action item, checks Honeycomb to confirm the targeted SLO or error rate actually recovered.
How it runs
The automated pipeline, trigger to output.
- TriggerMonthly verification schedule fires
- ActionFetch closed remediation items from LinearLinear
- ActionRun each item's SLO/error query in HoneycombHoneycomb
- LogicClassify metric as recovered, flat, or regressed
- ActionRe-open flat/regressed items with metric deltasLinear
- OutputSummarize unverified fixes in TeamsMicrosoft Teams
What it does
A remediation isn't done because a ticket is closed — it's done when the metric it was supposed to fix actually moved. This agent reads closed incident action items from Linear, looks up each item's target SLO or error-rate query in Honeycomb, and compares the post-fix metric window against the incident baseline. If the metric never recovered or has regressed since the fix, it re-opens the item and flags it for re-investigation.
When to use it
Use it monthly to catch remediations that were technically merged but failed to deliver the reliability outcome — the silent regressions that resurface as repeat incidents.
How it works
A monthly schedule triggers the run. The agent fetches closed `incident-remediation` items from Linear, each carrying a Honeycomb query reference. For every item it runs the Honeycomb query over the post-fix window and compares to the recorded baseline. A logic step classifies each as recovered, flat, or regressed. Items that are flat or regressed are re-opened in Linear with the metric deltas attached, and an ms-teams alert summarizes which fixes did not hold.
Set it up
What you configure once, before turning it on.
- 1Connect LinearIssues, projects, cycles, triage.
- 2Connect HoneycombDistributed traces and queries.
- 3Connect Microsoft TeamsChannels, chats, files.
- 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
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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.

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