AI AGENTS

SLO-Backed Remediation Verifier

For each closed incident action item, checks Honeycomb to confirm the targeted SLO or error rate actually recovered.

CategoryAI Agents
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMonthly verification schedule fires
  • ActionFetch closed remediation items from LinearLinearLinear
  • ActionRun each item's SLO/error query in HoneycombHoneycomb
  • LogicClassify metric as recovered, flat, or regressed
  • ActionRe-open flat/regressed items with metric deltasLinearLinear
  • 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.

  1. 1
    Connect LinearIssues, projects, cycles, triage.
  2. 2
    Connect HoneycombDistributed traces and queries.
  3. 3
    Connect Microsoft TeamsChannels, chats, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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