OTHER
Warranty return investigation agent
An agent-driven workflow that takes a warranty return, assembles the full case — order history, prior RMAs, and a photo-condition assessment.
How it runs
The automated pipeline, trigger to output.
- TriggerWarranty-tagged return arrives in FrontFront
- ActionClassify condition and failure mode via Hugging FaceHugging Face
- ActionLook up order and prior-RMA history in AirtableAirtable
- LogicReason over evidence to form coverage recommendation
- ActionWrite recommendation and rationale to RMA recordAirtable
- OutputPost decision-ready dossier to adjudication Slack channelSlack
What it does
When a warranty return lands in Front, this agent workflow builds a complete investigation file. It reads the claim, classifies the product's condition and likely failure mode from the photos with a Hugging Face model, pulls the customer's order and prior-return history from Airtable, and reasons about whether the damage pattern fits warranty coverage or looks like misuse. It writes a structured recommendation — covered, denied, or needs-inspection with rationale — back to the RMA record and notifies the adjudication team.
When to use it
Use it for warranty returns where coverage isn't obvious and an agent needs to weigh photos, failure mode, and customer history together before a human signs off.
How it works
- 1A warranty-tagged return in Front triggers the agent.
- 2The agent classifies condition and failure mode from photos via Hugging Face.
- 3It looks up order history and prior RMAs in Airtable.
- 4It reasons over the evidence to form a coverage recommendation with rationale.
- 5The recommendation and supporting notes are written to the Airtable RMA record.
- 6A decision-ready summary is posted to the adjudication Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect FrontShared inbox, conversations.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 3Connect AirtableBases, tables, views, automations.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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