How to Build an AI Agent That Manages Your E-commerce Store

Create an AI agent that can list products, check inventory, process orders, and handle customer queries across your store.

What This Integration Does

This workflow exposes your storefront (Shopify, WooCommerce, BigCommerce, or Adobe Commerce) to a single AI Agent that can take a natural-language instruction and execute the right sequence of tool calls. Instead of writing one workflow per task (one to discount a SKU, one to fulfill an order, one to answer a stock question), you build one Agent-Mode Connector node that has the relevant tools in scope and decides what to do for each request.

It can run interactively (a webhook from a chat UI), on a schedule (e.g. nightly "restock anything under 5 units"), or from email (a manager forwards a request and the agent acts). All side-effecting tool calls are logged so you can audit what the agent did.

Prerequisites

A shopify, woocommerce, bigcommerce, or adobe-commerce-rest connection with read and write scopes for products, orders, inventory, and customers.
A workspace LLM provider configured (Vertex AI, OpenAI, or Anthropic).
Optional: a slack connection for notifications and a Human approval step for high-risk actions.

Step 1: Trigger

Drop a Trigger node onto the canvas. Pick the type that matches how you'll invoke the agent:

Webhook - for a chat UI or Slack slash command. The body carries the user instruction.
Email - the agent reads the subject and body as the instruction.
Schedule - for recurring jobs like "rebalance inventory at 02:00".
Manual - for one-off operations from the canvas.

Step 2: Connector Node in Agent Mode

Add a Connector node and switch it to Agent Mode. Point it at your store connector (for example shopify). Set the prompt to something like:

You are an operations assistant for our Shopify store.
Use the available tools to fulfill the user's request.
Instruction: {{ trigger.body.message }}

Return JSON with: { "actions": [...], "summary": "..." }

Pick a capable model (e.g. Gemini Pro, Claude Sonnet) - tool-using tasks benefit from stronger reasoning.

Step 3: Filter the Tools the Agent Can See

By default the agent gets every tool on the connector. That's both expensive (token cost scales with tool count) and risky (it could call delete-product). Use the node's allowedTools setting to expose only what's needed. A safe starter set on shopify:

list-products, get-product, update-product
get-inventory-levels, adjust-inventory
list-orders, get-order, update-order
list-customers, get-customer
create-discount, list-discounts

Leave destructive tools (delete-product, cancel-order) off the list and gate them behind a separate human-approval flow.

Step 4: Structured Output

Give the Agent Mode node a JSON schema so the result is machine-readable. This lets the next nodes in the workflow react to what happened:

{
  "type": "object",
  "properties": {
    "actions": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "tool": { "type": "string" },
          "args": { "type": "object" },
          "result": { "type": "string" }
        }
      }
    },
    "summary": { "type": "string" }
  }
}

Step 5: Human Approval for High-Risk Actions

For anything irreversible - cancelling orders, deleting products, issuing refunds - route through a Human node before the side-effecting call. The agent's plan is shown to the approver, who clicks approve or reject. Combine with a Condition node on the agent's classification (e.g. "is this a cancel/refund request?") so routine reads skip the human entirely.

Step 6: Notify and Log

End the workflow with a Connector node on slack / send-message that posts the agent's summary plus the list of tool calls to an ops channel. This gives you an audit trail without leaving the canvas. The Spojit execution log also captures every tool call the agent made, with full inputs and outputs.

Tips

Keep prompts short and behavioural. Tell the agent how to behave ("always confirm SKU exists before adjusting inventory"); rely on the tool schemas to describe what each tool does.
Cap the agent's iterations. Spojit's Agent Mode supports a max-steps limit. 6-10 is plenty for store ops; higher just risks runaway costs.
Cache reference data. If you find the agent calling list-products on every run, pre-fetch it in a Direct Mode step and pass it into the prompt.

Common Pitfalls

Too many tools. Exposing all 25+ Shopify tools at once balloons input tokens and increases the chance of wrong tool choice. Filter aggressively.
Ambiguous SKUs. If a user says "discount the blue shirt", the agent may pick the wrong variant. Have the agent confirm the SKU via list-products first, or ask the user to disambiguate.
OAuth scope mismatches. A Shopify connection that's missing write_inventory will make the agent's plan fail at runtime. Verify scopes when you set up the connection.
Silent failures. If Structured Output isn't set, you might miss that the agent gave up and returned plain text. Make the schema required.

Testing

Start in a dev store (Shopify offers free dev stores) or with read-only tools first. Send 10-15 sample instructions covering common cases - "how many units of SKU-1234 do we have", "create a 10% off code for collection Summer", "show me unfulfilled orders from this week" - and inspect the execution log. Once read paths look right, enable write tools one at a time.