AI Product Sync Pipeline Workflow
Product sync between platforms with an AI agent in the middle: field extraction, category mapping and schema normalization before the write-back returns 200 OK.
n8n as the central integration layer: one workflow decides whether an event is a product sync or an order, routes it to the right channel and closes the loop.
Copy and paste the following FlowZap code into a project in your FlowZap account to see this template diagram.
main { # Multi-Platform Integration Layer
n1: circle label:"Start"
n2: diamond label:"Channel?"
n3: rectangle label:"n8n Webhook Receiver"
n4: rectangle label:"AI Agent: Parse & Validate"
n5: diamond label:"Event Type?"
n6: rectangle label:"Product Sync Pipeline"
n7: rectangle label:"Order Orchestrator"
n8: rectangle label:"Inventory Update"
n9: rectangle label:"Unified DB Write"
n10: rectangle label:"Fulfillment Trigger"
n11: circle label:"Done"
n1.handle(right) -> n2.handle(left)
n2.handle(right) -> n3.handle(left) [label="Shopify"]
n3.handle(right) -> n4.handle(left)
n4.handle(right) -> n5.handle(left)
n5.handle(right) -> n6.handle(left) [label="product"]
n6.handle(right) -> n9.handle(left)
n7.handle(right) -> n8.handle(left)
n8.handle(right) -> n9.handle(left)
n9.handle(right) -> n10.handle(left)
n10.handle(right) -> n11.handle(left)
n2.handle(top) -> n3.handle(top) [label="WooCommerce"]
n5.handle(bottom) -> n7.handle(bottom) [label="Order"]
}
Install the MCP packagenpm install -g flowzap-mcpMCP documentation
Product sync between platforms with an AI agent in the middle: field extraction, category mapping and schema normalization before the write-back returns 200 OK.
Order routing where an AI decision engine classifies each order, auto-processes the clean ones and sends the rest to a backorder queue before operations completes it.
Slack as interface, River as agent surface, Aquifer as foundation and a separate harness: the four layers behind an internal agent that treats a session as a durable entity.
Decoupling the brain from the hands: the harness calls the LLM and emits tool intents, while the sandbox executes them, so a new agent is a profile and not a platform.
Three session modes in one agent: interactive in Slack, automated review in the pull request, and ephemeral batch runs triggered by CI — cattle, not pets.
Coinbase AgentKit flow: a human allocates a USDC budget, the agent authenticates its Base wallet, pays an x402 invoice and processes the supplier data, with no fiat in the path.