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Example 1: Appointment Booking (Flow Agent)

Goal: Collect details in a conversation, call a booking API, then confirm. Variables:
  • Custom: business_name, business_phone
  • AI extracted (Conversation): customer_name, appointment_date, appointment_time
  • Toolpath (Tool – Booking API): After Test Tool, use path selector for booking_id ($.data.booking.id), confirmation_code ($.data.booking.confirmation_code)
  • Static (Confirmation node): confirmation_message = "Your appointment at {{business_name}} is confirmed for {{appointment_date}} at {{appointment_time}}. Confirmation code: {{confirmation_code}}"
Flow: Start → Conversation (collect) → Tool (book) → Conversation (confirm) → End. Each step sees system + custom + variables from previous steps.

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Example 2: Customer Support Routing (Flow Agent)

Goal: Classify issue and urgency, look up account, then route. Variables:
  • Custom: support_hours_start, support_hours_end, emergency_number
  • AI extracted: issue_type (enum: billing, technical, general), urgency_level (low, medium, high, critical)
  • Toolpath (CRM): account_status, is_premium, customer_id
  • Static: routing_decision = "{{issue_type}}_{{urgency_level}}_{{account_status}}"
Use these in router conditions and transfer nodes.

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Example 3: DTMF Account Verification (Flow Agent)

Goal: Capture account number via keypad, call account API, then speak balance or failure. Variables:
  • DTMF (Start): account_number
  • Toolpath (Account API): account_verified, account_name, account_balance
  • Static: verification_success_message and verification_failure_message using {{account_name}}, {{account_balance}}, {{account_number}}

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Example 4: API Tool Testing & Extraction (Flow Agent)

Goal: Call a weather API and use response in the next message. Steps:
  1. Configure tool (URL, params e.g. {{user_location}}, headers).
  2. Click Test Tool with e.g. user_location = "New York".
  3. In path selector: pick $.current.temperature → current_temperature, $.current.conditions → weather_conditions, $.location.city → city_name, $.forecast[0].high → tomorrow_high.
  4. In the next conversation node: "The current temperature in {{city_name}} is {{current_temperature}}°C with {{weather_conditions}}. Tomorrow's high will be {{tomorrow_high}}°C."
Takeaways: Test first, use path selector, test success and error responses, handle optional fields.

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Example 5: Web Tool Variable Extraction (Flow Agent)

Goal: Add to cart via web tool and confirm in a message. Steps:
  1. Define expected response (object or string). Example object: {"cart": {"total": 149.99, "item_count": 2, "items": [...], "discount_code": "SAVE10"}}.
  2. In path selector (against that structure): $.cart.total → cart_total, $.cart.item_count → cart_item_count, $.cart.items[0].name → first_item_name, $.cart.discount_code → discount_code.
  3. For string response, use $ for the full string (e.g. cart_summary).
Message: "I've added {{first_item_name}} to your cart. Total: ${{cart_total}}, {{cart_item_count}} items. Code: {{discount_code}}." Web vs API: Web tools run in the browser; you define expected response and optionally test manually. API tools use Test Tool and real response for path selection.

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Example 6: Single Prompt Agent with Variables

Goal: Create a simple customer support agent for a restaurant using a single prompt agent (no flow builder). Agent type: Single Prompt Agent Step 1: Define custom variables In the Variables Panel, create custom variables that act as configuration parameters: 
restaurant_name: "Bella Italia"
restaurant_phone: "+1 (555) 123-4567"
restaurant_address: "123 Main Street, New York, NY"
opening_time: "11:00 AM"
closing_time: "10:00 PM"
accepts_reservations: true
delivery_available: true
menu_url: "https://bellaitalia.com/menu"
Step 2: Configure the single prompt System prompt: 
You are a friendly customer service assistant for {{restaurant_name}}.

Restaurant Information:
- Phone: {{restaurant_phone}}
- Address: {{restaurant_address}}
- Hours: {{opening_time}} to {{closing_time}}
- Menu: {{menu_url}}

Current Context:
- Current time: {{current_time}}
- Current date: {{current_date}}
- Day of week: {{current_weekday}}
- Customer phone: {{user_number}}

Services:
{{#if accepts_reservations}}
- We accept reservations. You can help customers book tables.
{{/if}}
{{#if delivery_available}}
- We offer delivery service.
{{/if}}

Your Role:
- Answer questions about our menu, hours, and location
- Help with reservations and orders
- Be warm, friendly, and professional
- If you don't know something, offer to have someone call them back
Step 3: Available variables Custom (configuration): restaurant_name, restaurant_phone, restaurant_address, opening_time, closing_time, accepts_reservations, delivery_available, menu_url System: current_time, current_date, current_weekday, user_number, call_id, direction, and all other system variables Not available: Extracted variables, static variables, or variables from conversation history (single prompt agents don’t have flow nodes). Step 4: Example conversation User: “What time do you close?” Agent (using variables): “We’re open from to . Right now it’s , so we’re currently open!” Rendered: “We’re open from 11:00 AM to 10:00 PM. Right now it’s 2:30 PM, so we’re currently open!” Step 5: Updating configuration To change hours, update only the custom variables (e.g. opening_time, closing_time). The agent uses the new values without changing the prompt. Step 6: Multi-environment Use different custom variable values per location (e.g. NYC vs Boston: different restaurant_name, restaurant_phone, restaurant_address). Same prompt, different configuration. Advantages of single prompt agents: Simple setup, quick deployment, easy updates via variables, multi-environment support, no extraction needed for simple Q&A. When to upgrade to Flow Builder: When you need to collect customer info, check availability via API, process orders, transfer by intent, or extract structured data from conversations. Takeaways: Custom variables act as configuration; system variables are available; no extraction; use {{variable_name}} and {{#if}} for conditionals; ideal for straightforward conversational agents and multi-environment configs.
See Extracted Variables for AI/toolpath/DTMF details, Availability & Context for flow position and Single Prompt Agent Variables, and Best Practices for naming and extraction strategy.