Agent Library · By platform · D2C · Orders · Returns
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A thousand orders a day. Twelve of them are wrong.
D2C operations are high-volume and low-margin, which means the cost of handling exceptions manually is the whole margin. The routine 98% should never need a person. The 2% should reach one immediately, with context.
Exceptions are found by customers
The failed address, the split shipment, the out-of-stock line — the customer usually notices first.
Returns triage is repetitive and judgement-heavy
Most are routine, a few are fraud or a product problem, and they arrive mixed together.
Catalogue quality decays
Descriptions, attributes and images drift as SKUs multiply, and search suffers first.
The agents
5 agents. Each one earns its autonomy.
Agent
What it does
Autonomy
Human gate
Order Exception Handler
Address failures, payment holds, split shipments and stock-outs detected and routed with the fix proposed.
Notify
—
Returns Triage
Return requests classified by reason, value and risk, with the routine approved and the unusual escalated.
Review
Refund approval stays human
Post-Purchase Support
Where-is-my-order and similar high-volume queries answered from real fulfilment data.
Review
Human approves customer-facing replies
Catalogue Hygienist
Missing attributes, thin descriptions and inconsistent variants flagged and drafted.
Notify
—
Fraud Signal Watch
Order and return patterns that suggest abuse flagged for human review with the evidence.
Flag only
—
Autonomy key — Auto: executes above 95% confidence. Notify: executes at 85–95% with notification and undo. Review: always queued for a human. Flag only: never acts, alerts a person.
What changes
Three shifts, not a feature list.
Exceptions get caught before the customer notices
The order that would have generated a ticket gets fixed while it is still an internal event.
Returns stop consuming the support queue
The routine majority clears; the judgement cases arrive with a summary.
Catalogue quality stops decaying with scale
Hygiene runs continuously rather than in occasional clean-up projects.
The eval bench
What these agents have to pass before they touch real work.
- Exception recall against a week of orders your team has already worked manually
- Returns classification accuracy against 200 historic decisions
- Zero autonomous refunds — verified structurally
Written against your data, not ours. The bench is built during Blueprint and runs on every deploy from then on.
Related
Where this connects.
See it on your Shopify.
A Blueprint maps which of these agents pays off first in your exact setup — and which to leave alone.
Apply for a Blueprint