WooCommerce Support Agent

Challenge

A WooCommerce store was handling hundreds of support messages per week, the majority asking the same three things: where is my order, how do I return it, and does this product fit my use case. Each reply required a support rep to log into WooCommerce, look up the order, and copy-paste the tracking information. The work was mechanical and time-consuming; the answers were never wrong, but the team's time was being spent on tasks with zero creative or judgement value.

Options Considered

1. Static FAQ page — reduced some ticket volume but didn't help customers who couldn't find the relevant answer or needed order-specific data.
2. Helpdesk with canned responses (Zendesk, Freshdesk) — improved agent speed but still required a human to trigger the reply. Didn't solve after-hours coverage.
3. AI agent with live WooCommerce API access — chosen. The agent retrieves real order data, checks the return policy against the specific product, and escalates only non-standard cases.

Decision

The agent connects to WooCommerce REST API to look up orders by email or order number in real time. It answers questions about order status, estimated delivery, return eligibility, and product specifications. When a case requires a human — a damaged item, a payment dispute, or a question outside the knowledge base — it escalates with a full conversation summary.

Implementation

A Python backend wraps the WooCommerce REST API with a tool-calling interface. The LLM calls the appropriate tool (get_order, check_return_policy, search_catalog) and includes the result in its response. The knowledge base is stored as vector embeddings and queried via RAG for FAQ and policy questions. The agent is embedded as a chat widget on the store and connected to the existing email support channel via webhook.

Human handoff preserves the full conversation history and pre-fills the helpdesk ticket with the order number, issue category, and the agent's assessment — so the support rep starts with context, not a blank ticket.

Outcome

Approximately 75% of support conversations resolved without human involvement. After-hours coverage went from zero to full. Average first-response time dropped from 3 hours to under 10 seconds. Support team shifted focus to complex cases and relationship-building.