01 / 05
Receive
A customer message arrives over WhatsApp or email. The agent picks it up on its next run and opens a trace for that query.
← All work
Case study · AI systems · 02
Shopify Commerce Agent
Identifies intent, fetches live Shopify data, drafts a reply — and waits for a human to approve.
A production-shaped e-commerce agent built on the same LangGraph foundation as the Email Automation Agent. It reads customer queries over WhatsApp and email, classifies intent — order status, product info, returns — calls the Shopify API for live data, drafts a reply, and holds everything for human review before anything sends.
The pipeline, not a promise
How it works
Step by step
Five stages, one gate
02 / 05
Classify
An LLM call over Groq identifies intent — order status, product info, return, or general inquiry — and routes each type differently downstream.
03 / 05
Query Shopify
A tool call hits the Shopify API for live data — order details, shipping status, product availability. Every call is wrapped in retry logic with exponential backoff.
04 / 05
Approve
The draft lands in a Streamlit review queue. A person approves, edits, or rejects it — rejected drafts go back for a rewrite.
05 / 05
Send & log
Approved replies go out through WhatsApp or email. Every stage — classify, query, draft, send — is logged with its cost and latency.
Agent in action
A real query, end to end
Customer sends
"Where is my order #1234? It has been 5 days."
Agent classifies
Intent: order_status — routes to Shopify order tool call
Shopify returns
Order #1234 shipped Sep 18, arriving Sep 21 via DHL · tracking #DE4912
LLM drafts reply
"Hi! Your order #1234 shipped on Sep 18 and is arriving Sep 21 via DHL. Track here: [link]. Let me know if you need anything else."
HITL guardrail → approved → sent
Human reviewed and approved. Reply delivered in under 4 seconds from approval.
Why the gate matters
A commerce agent that sends a wrong shipping date or incorrect price to a customer is a customer-service problem, not just a code bug. So nothing leaves this agent without a person seeing it first.
Every LLM call carries a typed output schema, retry logic, and cost tracking. A failed tool call or a low-confidence classification holds the message for human review instead of guessing at an answer.
The Shopify tool calls are idempotent reads — no writes, no order mutations. Even if something fails inside the agent, it cannot corrupt customer data or trigger any order changes.
Built with
Tech used on this project
Want a commerce agent for your store?
Open to freelance and contract work on agentic AI systems — Shopify automation, omnichannel agents, or something custom. If you can describe the problem, I can tell you honestly whether it is a good fit.
- Project
- Shopify Commerce Agent
- Status
- Built & tested
- Role
- Design & engineering
- Contact
- affanned399@gmail.com