Recipe: Order-Status & Product-Support Agent

This recipe builds a single support agent that handles two different kinds of question in the same conversation: general product/policy questions, answered from a knowledge base, and “where’s my order” questions, answered with a live call to an order-lookup API. It combines the RAG pattern from the first cookbook recipe with a router node and a webhook node — this page assumes you’ve read that first recipe and won’t re-explain knowledge base creation or ingestion from scratch.

Every request below needs the x-huat-platform: customer header and a valid dashboard Authorization: Bearer <token> — see Getting Started and Authentication if you haven’t set those up.

Step 1: Create a knowledge base for product/policy questions

Same call as Support Agent with a Knowledge Base — create it, then ingest your product catalog and FAQ content with ingestDocument (inline text) or the presigned-upload flow for larger catalog files. Assume this returns knowledge base id 91.

mutation CreateKB {
  createKnowledgeBase(
    input: {
      name: "Product Catalog & FAQ"
      description: "Product descriptions, sizing, warranty, and returns policy"
    }
  ) {
    id
  }
}

Step 2: Create the agent

mutation CreateOrderSupportAgent {
  createAgent(
    input: {
      name: "Order & Product Support"
      position: "Support Agent"
      personaPrompt: "You are a helpful e-commerce support assistant. You can answer questions about products, sizing, warranty, and returns using your reference material, and you can look up the live status of an order when a customer asks. If you don't have information to answer something, say so plainly rather than guessing."
      useCase: SUPPORT
      voiceTier: STANDARD
    }
  ) {
    id
  }
}

Assume this returns agent id 103.

Step 3: Design the workflow

The workflow classifies each message into one of two intents and dispatches accordingly with a router node — the same classify-then-dispatch shape used throughout Worked Examples & Demos, extended here with a real, live data source on one branch instead of a static response.

Flow: start → classify intent (llm) → dispatch (router) → either:

• order_status branch: extract the order ID (llm, extractFirstInteger) → look up the order (webhook, calls your order API) → reply with the live result (response)
• product_question branch: answer from the knowledge base (llm with knowledgeBaseId: 91) → reply (response)

…then end_session on either path.

mutation CreateWorkflow($input: CreateWorkflowInput!) {
  createWorkflow(input: $input) {
    id
  }
}

{
  "input": {
    "name": "Order & Product Support",
    "description": "Answers product questions from a knowledge base and live order-status questions via webhook."
  }
}

Assume this returns workflow id 67.

mutation UpdateWorkflow($input: UpdateWorkflowInput!) {
  updateWorkflow(input: $input) {
    id
    version
  }
}

{
  "input": {
    "id": 67,
    "nodes": [
      {
        "id": "start",
        "type": "start",
        "label": "Start",
        "config": {},
        "position": { "x": 0, "y": 0 }
      },
      {
        "id": "classify_intent",
        "type": "llm",
        "label": "Classify Intent",
        "config": {
          "outputVar": "intent",
          "promptTemplate": "Classify this message: \"{{userMessage}}\". Respond with ONLY one word: order_status, or product_question.",
          "streamOutput": false
        },
        "position": { "x": 0, "y": 100 }
      },
      {
        "id": "dispatch",
        "type": "router",
        "label": "Intent Dispatcher",
        "config": {
          "variable": "intent",
          "defaultTarget": "answer_from_kb"
        },
        "position": { "x": 0, "y": 180 }
      },
      {
        "id": "extract_order_id",
        "type": "llm",
        "label": "Extract Order ID",
        "config": {
          "outputVar": "orderId",
          "extractFirstInteger": true,
          "promptTemplate": "You are a data extraction step in an automated pipeline, not the assistant talking to the user — you must NOT generate a reply, confirmation, explanation, or any commentary. Extract ONLY the numeric order ID from this message: \"{{userMessage}}\". If no order ID is present, respond with exactly: 0. Respond with ONLY the number, nothing else.",
          "streamOutput": false
        },
        "position": { "x": -250, "y": 260 }
      },
      {
        "id": "lookup_order",
        "type": "webhook",
        "label": "Check Order Status",
        "config": {
          "url": "https://orders.example.com/api/status",
          "method": "POST",
          "headers": { "content-type": "application/json" },
          "bodyTemplate": "{\"orderId\": {{orderId}}}",
          "outputVar": "order_status_result",
          "timeoutMs": 8000
        },
        "position": { "x": -250, "y": 340 }
      },
      {
        "id": "respond_order",
        "type": "response",
        "label": "Respond With Order Status",
        "config": {
          "messageTemplate": "Here's what I found for order #{{orderId}}: {{order_status_result}}",
          "mood": "helpful"
        },
        "position": { "x": -250, "y": 420 }
      },
      {
        "id": "answer_from_kb",
        "type": "llm",
        "label": "Answer From Product Catalog",
        "config": {
          "promptTemplate": "You are a helpful e-commerce support assistant. Answer the customer's question using the provided reference material where relevant: {{userMessage}}",
          "knowledgeBaseId": 91,
          "outputVar": "kb_answer"
        },
        "position": { "x": 250, "y": 260 }
      },
      {
        "id": "respond_kb",
        "type": "response",
        "label": "Respond With Product Info",
        "config": {
          "messageTemplate": "{{kb_answer}}",
          "mood": "helpful"
        },
        "position": { "x": 250, "y": 340 }
      },
      {
        "id": "end",
        "type": "end_session",
        "label": "End",
        "config": { "runEvaluation": true },
        "position": { "x": 0, "y": 500 }
      }
    ],
    "edges": [
      { "id": "e1", "source": "start", "target": "classify_intent" },
      { "id": "e2", "source": "classify_intent", "target": "dispatch" },
      {
        "id": "e3",
        "source": "dispatch",
        "target": "extract_order_id",
        "label": "order_status"
      },
      {
        "id": "e4",
        "source": "dispatch",
        "target": "answer_from_kb",
        "label": "product_question"
      },
      { "id": "e5", "source": "extract_order_id", "target": "lookup_order" },
      { "id": "e6", "source": "lookup_order", "target": "respond_order" },
      { "id": "e7", "source": "respond_order", "target": "end" },
      { "id": "e8", "source": "answer_from_kb", "target": "respond_kb" },
      { "id": "e9", "source": "respond_kb", "target": "end" }
    ]
  }
}

A few choices worth calling out:

• dispatch’s defaultTarget points at answer_from_kb, not a generic fallback response. If the classifier ever outputs something outside the two categories it was given, treating the message as a product question and letting the knowledge base attempt an answer is a more useful default than a dead-end “I didn’t understand” reply — see Router Node for the full defaultTarget/circuit-breaker mechanics if you want to harden this further as usage grows.
• extract_order_id uses extractFirstInteger: true and is explicitly framed as a pipeline step, not the assistant. This is the same pattern from Worked Examples & Demos — it guarantees lookup_order’s bodyTemplate receives a bare number rather than breaking on an off-task reply like “could you clarify the order number?”
• lookup_order is a webhook node, not a tool node, because this recipe assumes you haven’t registered your order API as an MCP connector yet — a webhook node calls a URL directly with no prior registration step. If you later stand up an MCP server for this API (or already have one), a tool node is the better long-term fit; the config shape and argsTemplate/bodyTemplate templating rules are the same either way.
• {{orderId}} is unquoted in bodyTemplate ({"orderId": {{orderId}}}) because extractFirstInteger guarantees it’s already a bare digit string — quoting it would send a string where the endpoint likely expects an integer. This is only safe because of that guarantee; don’t do this with a raw, unvalidated LLM output.
• answer_from_kb reuses the exact RAG pattern from the first recipe — same knowledgeBaseId field, same fail-open retrieval behavior if the vector store is briefly unreachable.

Step 4: Publish and attach

mutation PublishWorkflow($id: Int!) {
  publishWorkflow(id: $id) {
    id
    isPublished
    version
  }
}

{ "id": 67 }

mutation AttachWorkflow {
  updateAgent(id: "103", input: { workflowId: 67 }) {
    id
    workflowId
  }
}

As always, confirm isPublished: true before testing — an unpublished workflow runs zero nodes, with no error anywhere. See Agents.

Before you point this at a real order API

lookup_order.config.url above is a placeholder. Two things to know before pointing it at your real endpoint:

• The URL is checked for SSRF risk at save time, not just execution time — a url pointing at a private/internal IP range is rejected by updateWorkflow before the workflow is even stored. This is the same guard applied to action node URLs, and it means your order-lookup API needs to be reachable from the public internet (or through whatever gateway you expose it behind) for this node to call it at all.
• timeoutMs is clamped to a hard ceiling of 30 seconds, regardless of what you configure. If your real order-lookup endpoint can genuinely take longer than that under load, a webhook node isn’t the right fit for it as-is — see Webhook Node for the full list of constraints.

What happens when this runs

A customer sends: “Where’s my order #48213?”

1. classify_intent reads the message and outputs order_status.
2. dispatch routes to extract_order_id, which pulls 48213 into orderId (as a string, thanks to extractFirstInteger).
3. lookup_order calls https://orders.example.com/api/status with {"orderId": 48213} and waits (blocking, since async is unset) for the response, storing it in order_status_result.
4. respond_order sends the result back: something like “Here’s what I found for order #48213: shipped, arriving Thursday.”

A different customer sends: “Does the men’s jacket run true to size?”

1. classify_intent outputs product_question.
2. dispatch routes to answer_from_kb, which retrieves the relevant sizing-guide chunk from knowledge base 91 and answers grounded in it.
3. respond_kb sends that answer back verbatim.

Both paths converge on the same end_session node, which queues a post-session evaluation (customer-satisfaction-flavored, since useCase: SUPPORT) you can fetch via the evaluation query — see Evaluation & Export.

Next steps

• Cookbook: Support Agent with a Knowledge Base (RAG) — the RAG setup this recipe builds on.
• Webhook Node — full config reference, including blocking vs. async: true fire-and-forget mode.
• Router Node — the defaultTarget/circuit-breaker mechanics for hardening the classifier’s fallback behavior.
• Tool Node — the registered-connector alternative to a webhook node, once you have an MCP server for your order API.
• Troubleshooting — if a webhook call is failing silently or a branch isn’t firing.