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 Spojit 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 Connector node in Agent Mode that has the relevant tools in scope and decides what to do for each request.

It can run interactively (a Webhook trigger 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). Every tool call the agent makes is recorded in the execution history so you can audit what it did.

Prerequisites

A shopify, woocommerce, bigcommerce, or adobe-commerce-rest connection with read and write scopes for products, orders, inventory, and customers.
An AI model selected for the Agent Mode node (Agent Mode consumes AI credits).
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 external app. The posted JSON body carries the user instruction, available as {{ input }}.
Email - polls a connected Gmail or Outlook mailbox; the agent reads {{ input.subject }} and {{ input.textBody }} as the instruction.
Schedule - for recurring jobs like "rebalance inventory at 02:00", using a 5-field cron expression and a timezone.
Manual - for one-off operations, run from the Run button.

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 agent for our Shopify store.
Use the available tools to fulfill the user's request.
Instruction: {{ input.message }}

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

Pick a capable model - tool-using tasks benefit from stronger reasoning. Agent Mode consumes AI credits, so reserve it for requests that genuinely need judgment or multi-step reasoning.

Step 3: Filter the Tools the Agent Can See

By default the agent can reach every tool on the connector. That's both expensive (token cost scales with tool count) and risky (it could call delete-product). In the Agent Mode node, scope the tool list 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: Response Schema

Give the Agent Mode node a Response Schema so the result is forced into machine-readable JSON. 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 using 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. Spojit's execution history 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.
Keep the tool list tight. Fewer tools in scope means fewer steps the agent can take, lower token cost, and less chance of a wrong tool choice. Add tools only as you confirm you need them.
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. Without a Response Schema, you might miss that the agent gave up and returned plain text. Set the schema so downstream nodes always get structured JSON.

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 history. Once read paths look right, enable write tools one at a time.