TICKET MANAGEMENT
Agent-driven duplicate sweep that triages and proposes merges per ClickUp space
An agent walks each ClickUp space, reasons about which open tasks are true duplicates versus related-but-distinct work.
How it runs
The automated pipeline, trigger to output.
- TriggerOperator starts the sweep for chosen spaces
- ActionPull open tasks and dependency graphs per spaceClickUp
- ActionAgent reasons over clusters and drafts rewire plansOpenAI
- LogicRoute by confidence to review vs. fast-track channels
- OutputDeliver merge proposals with controls in SlackSlack
What it does
Runs a reasoning agent over your ClickUp workspace to do dedupe triage the way a lead would. Rather than threshold matching alone, the agent reads task context, distinguishes genuine duplicates from merely similar tickets, picks a defensible keeper, and drafts a per-cluster merge proposal that includes which dependencies would need rewiring — then routes each proposal to the right channel for review.
When to use it
Use it when naive similarity scoring produces too many false positives and you want judgment about what is actually the same work. Good for large, messy backlogs spanning many spaces.
How it works
- 1A manual run kicks off the sweep for a chosen set of spaces.
- 2The agent pulls open tasks per space from ClickUp, including descriptions and dependency graphs.
- 3It reasons through clusters, separating true duplicates from related work and selecting a keeper with written rationale.
- 4For each duplicate cluster it drafts a rewire plan describing which links move to the keeper.
- 5A logic step routes high-confidence clusters and uncertain ones to different Slack channels for review.
- 6Slack delivers the proposals with approve/reject controls.
Set it up
What you configure once, before turning it on.
- 1Connect ClickUpDocs + tasks + chats in one workspace.
- 2Connect OpenAIModels, embeddings, files.
- 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.
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