AI AGENTS

Route low-confidence epic splits to a human before filing

An agent splits oversized Linear epics, but when its estimate confidence is low it holds the breakdown and asks an engineering lead in Slack to approve or revise before any child…

CategoryAI Agents
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerOversized Linear epic createdLinearLinear
  • ActionOpenAI splits epic with confidence scoresOpenAI
  • LogicBranch on estimate confidence threshold
  • ActionRequest lead approval in Slack if uncertainSlack
  • OutputCreate approved child issues in LinearLinearLinear

What it does

The agent decomposes oversized Linear epics into estimated child issues, but it scores its own confidence. High-confidence breakdowns are filed automatically; low-confidence ones are posted to an engineering lead in Slack with approve and revise buttons, and only file once a human signs off.

When to use it

Use when you want automation speed on routine epics but a human gate on ambiguous or high-stakes ones. Good for teams that trust the agent for well-specified work but insist on review when scope is fuzzy.

How it works

  1. 1A Linear webhook fires when an oversized epic is created.
  2. 2OpenAI produces a child-issue breakdown plus a confidence score per estimate.
  3. 3A logic step branches on the lowest confidence score against a threshold.
  4. 4High-confidence path: child issues are created in Linear immediately.
  5. 5Low-confidence path: the breakdown is posted to Slack for an engineering lead to approve or revise.
  6. 6On approval, the (possibly edited) child issues are created in Linear under the epic.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect LinearIssues, projects, cycles, triage.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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