Shopify AI Customer Support Agent

A workflow for an AI-powered customer service agent that can answer queries about Shopify products, retrieve order status, and access information from internal documentation to assist users in real time.

How the AI Flow works - Shopify AI Customer Support Agent

How the AI Flow works

Receive User Query

Collects customer questions or inquiries via chat input.

Retrieve Product and Order Data

Fetches Shopify product details and order statuses as needed to answer customer questions.

Search Internal Documentation

Looks up information from internal knowledge sources to provide comprehensive responses.

AI Agent Composes Response

An AI agent analyzes the user's request, pulls together product, order, and documentation data, and generates a helpful answer.

Deliver Response to User

Shows the AI-generated response back to the customer in the chat interface.

Prompts used in this flow

Below is a complete list of all prompts used in this flow to achieve its functionality. Prompts are the instructions given to the AI model to generate responses or perform actions. They guide the AI in understanding user intent and generating relevant outputs.

Flow description

Purpose and benefits

Overview

This workflow sets up an AI-powered virtual shop assistant for an online store, integrating product information, order status checking, and knowledge base search. The workflow automates user interactions by combining a conversational AI agent with tools for retrieving document knowledge, checking Shopify order statuses, and fetching product information. All communication is managed via chat input and output nodes, allowing for seamless, scalable, and automated customer support.

Workflow Structure

The workflow consists of the following main components (nodes):

Node NameRole / Functionality
Chat InputReceives user messages as input.
Document RetrieverSearches and retrieves relevant documents from a knowledge base.
Get Order StatusFetches order status information from Shopify given an order number.
Get ProductsRetrieves product information from Shopify, sorted by relevance or other criteria.
AI AgentOrchestrates tools, interprets user queries, and generates intelligent responses.
Chat OutputDisplays the AI agent’s message as the chat response to the user.

Detailed Flow Description

  1. User Message Intake
    The process starts with a Chat Input node, where users can type their queries. This could include questions about products, order statuses, or general inquiries about the store.

  2. AI Agent as Virtual Shop Clerk
    The input message is routed to an AI Agent node. This agent is designed as a seasoned saleswoman (with a backstory of 20 years of experience), acting as a knowledgeable shop clerk. Its goals are:

    • To provide information about products.
    • To read and relay order status updates.
    • To answer any store-related queries using the knowledge base.

    The agent is configured to use multiple tools to achieve these goals.

  3. Integrated Tools (for the Agent):

    • Document Retriever Tool:
      Searches the store’s knowledge base for documents relevant to the user’s query. It is highly configurable, supporting features such as:

      • Filtering by categories, schedules, or URL patterns.
      • Limiting the number of documents and output tokens.
      • Including specific content types (headings, paragraphs) and metadata (e.g., Product).
      • Strategies to balance document inclusion. This tool enables the agent to answer not only factual questions but also provide nuanced information from policies, FAQs, guides, etc.
    • Get Order Status Tool:
      Connects to Shopify to fetch the status of a user’s order based on the provided order number. This supports real-time, automated order tracking for customers.

    • Get Products Tool:
      Allows the agent to fetch product details from Shopify, sorted by criteria such as best-selling, price, or relevance. This supports product recommendation and catalog browsing use cases.

  4. Response Generation and Output
    The AI agent processes the user’s message, determines which tools are needed, invokes them as required, and composes a response. The generated response is sent to the Chat Output node, which presents the answer back to the user in the chat interface.

Example Workflow Scenario

  • Product Inquiry:
    The user asks, “What are your best-selling headphones?”

    • The agent uses the Get Products tool (sorting by best_selling) and responds with relevant product information.
  • Order Status Inquiry:
    The user asks, “Where is my order #1234?”

    • The agent invokes the Get Order Status tool, retrieves the information, and reports back to the user.
  • Policy/Information Query:
    The user asks, “What is your return policy?”

    • The agent utilizes the Document Retriever tool to find and present the relevant policy from the knowledge base.

Automation and Scalability Benefits

  • Unified Support:
    The workflow integrates multiple sources (Shopify, custom knowledge base) into a single conversational interface, providing comprehensive support.

  • Automation:
    By automating responses to common queries, order tracking, and product recommendations, the workflow reduces manual workload and response times.

  • Scalability:
    The modular design allows for easy extension (e.g., adding new tools or knowledge sources) and supports handling many simultaneous user conversations.

  • Consistency:
    The AI agent ensures that customers receive consistent, accurate, and up-to-date information, improving customer satisfaction.

Summary Table: Workflow Steps

StepAction
1. User InputUser sends a message via chat input.
2. AI AgentReceives the message, interprets intent, and selects tool(s) to answer the query.
3. Tool InvocationAgent uses Product, Order, or Document Retriever tools as needed.
4. ResponseAgent composes a response and sends it to the chat output for the user to read.

Conclusion

This workflow enables a powerful, automated virtual shop assistant, leveraging AI and integrations with Shopify and internal knowledge bases. It is ideal for scaling customer support and sales operations in an online retail environment, ensuring fast, accurate, and context-aware responses to a wide array of customer queries.

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