Functions and Actions: Connecting AI to External APIs

A Knowledge Base gives your agent access to static content. Functions give it access to live data. When a customer asks “Where is my order?”, a function lets the agent call your order management API, retrieve the current status, and reply with real information — without the conversation leaving Fliqr AI.

Overview

Functions (also called tools or actions) are HTTP endpoints you define inside an AI Agent. When the agent determines that a function is relevant to answering a user’s question, it extracts the required parameters from the conversation, calls your endpoint, and incorporates the response in its reply — all in a single turn. The agent decides when to call a function based on the function’s description and the user’s message. You do not need to build explicit routing logic. The language model handles that decision.

Prerequisites

An AI Agent configured with a system prompt — see Prompt Engineering
An HTTP endpoint that accepts requests and returns JSON responses
(Recommended) Understanding of JSON Schema — functions use it to describe parameters

When to Use Functions

Functions are the right tool when the answer requires data that changes in real time or is specific to the authenticated user:

Check order status from your ERP or order management system
Look up appointment availability in your booking system
Create or update a contact record in your CRM
Send a Slack or email notification when a lead qualifies
Fetch a real-time product price or inventory level
Submit a support ticket on the customer’s behalf

If the data is static and infrequently updated (product descriptions, FAQ answers, policies), a Knowledge Base is simpler and does not require an API.

How Functions Work

You define a function: name, description, HTTP method, URL, headers, and a JSON Schema describing its parameters.
During a conversation, the language model reads the function’s description and decides whether it applies to the user’s current message.
If it applies, the model extracts the parameter values from the conversation and Fliqr AI calls your endpoint.
Your API returns a JSON response. Fliqr AI passes that response back to the model.
The model writes a reply to the user using the returned data.

The user experiences this as a single, coherent response. The function call happens in the background.

Add a Function to an Agent

Open the Functions tab

Go to AI Agents → select your agent → Functions tab.

Add a new function

Click Add Function. A configuration panel opens with fields for name, description, method, URL, headers, and parameters.

Enter the function name and description

The name is used internally (no spaces — use underscores). The description is what the language model reads to decide when to call this function. Write it as a clear, specific explanation of what the function does and what triggers it.

Name: get_order_status
Description: Retrieves the current status of a customer order by order ID.
             Call this when a customer asks about the status, location,
             or delivery date of a specific order.

Set the HTTP method and URL

Select the HTTP method (GET, POST, PUT, PATCH, DELETE) and enter your endpoint URL. Use {{parameter_name}} placeholders in the URL for path parameters.

Method: GET
URL: https://api.yourstore.com/orders/{{order_id}}

Add authentication headers

If your endpoint requires authentication, add the necessary headers. Use {{secret_key}} notation to reference secrets stored in your Fliqr AI environment — do not hard-code credentials.

Authorization: Bearer {{secret_key}}
Content-Type: application/json

Define the parameter schema

Define each parameter the function needs. For each parameter, specify its type, a description, and whether it is required.

Save and test

Click Save, then open the Playground and send a message that should trigger the function (e.g., “What’s the status of order #4821?”). Verify that the agent calls the function and incorporates the response correctly.

Example Function Definition

The following example defines a function that retrieves an order’s status from an external API. You can use this as a starting template.

{
  "name": "get_order_status",
  "description": "Retrieves the current status of a customer order by order ID. Call this when a customer asks about the status, location, or estimated delivery of a specific order.",
  "method": "GET",
  "url": "https://api.yourstore.com/orders/{{order_id}}",
  "headers": {
    "Authorization": "Bearer {{secret_key}}"
  },
  "parameters": {
    "order_id": {
      "type": "string",
      "description": "The order ID provided by the customer. Ask the customer for this if they have not already provided it.",
      "required": true
    }
  }
}

Structuring Function Results as Chat Messages

When a function returns data that should be presented as a structured message — a product card, a list of options, or a set of quick replies — combine function calls with JSON output prompting. Add an instruction to your system prompt:

When get_order_status returns a result, format your response as a JSON message:
[
  {
    "message": {
      "text": "Your order {{order_id}} is currently {{status}}. Estimated delivery: {{estimated_date}}.",
      "quick_replies": [
        { "content_type": "text", "title": "📦 Track Shipment", "payload": "Track Shipment" },
        { "content_type": "text", "title": "↩️ Request Return", "payload": "Request Return" }
      ]
    }
  }
]

This pairs function data retrieval with the structured output format your Flow expects, without requiring separate Flow blocks for each possible response.

Multiple Functions on One Agent

You can add multiple functions to a single agent. The model selects the right function based on each function’s description. To avoid unintended calls:

Keep each function’s description specific about its trigger conditions.
If two functions cover similar topics, differentiate them clearly in the description (e.g., “use this for order status, not for return requests”).
Test each function individually in the Playground before testing combinations.

Functions execute on behalf of the authenticated user. Validate and sanitize all parameter inputs on your server side before acting on them. Do not rely on the language model’s parameter extraction as a security boundary — treat function calls as you would any untrusted API input.

Write the function description as if you are explaining it to a colleague who needs to decide when to reach for it. A description like “Gets data” tells the model nothing. A description like “Retrieves the current inventory count for a specific product SKU — call this when a customer asks whether an item is in stock” produces accurate invocation decisions.

Built-in and Flow-triggered functions

Besides custom HTTP tools, Fliqr AI supports AI Functions that collect parameters and trigger a Flow (for example booking or weather lookup). Create them under AI Center → AI Tools → AI Functions. Naming tips:

Use multi-word names: get_current_weather, book_appointment
Start descriptions with “Allows the user to…”
Use meaningful parameter names (email, not random codes)
Prefer returning the customer-facing result via a custom field output message so the Agent can rewrite it naturally

See Appointment scheduling for a full example. For vendor ecosystems (Shopify, Stripe, Zapier), prefer MCP servers when available.

What’s next

MCP servers

Attach MCP tools without writing every HTTP function.

Prompt engineering

Teach the agent when to call each tool.

Human handover

Built-in path when AI should stop.

Rich responses

Format tool results as buttons and cards.

Core Concepts

Channels

Building blocks

Pipelines

Automation

Ads

E-Commerce

Integrations

Analytics

Account & Admin

Troubleshooting

Functions and Actions: Connecting AI to External APIs

Overview

Prerequisites

When to Use Functions

How Functions Work

Add a Function to an Agent

Example Function Definition

Structuring Function Results as Chat Messages

Multiple Functions on One Agent

Built-in and Flow-triggered functions

What’s next

MCP servers

Prompt engineering

Human handover

Rich responses

​Overview

​Prerequisites

​When to Use Functions

​How Functions Work

​Add a Function to an Agent

​Example Function Definition

​Structuring Function Results as Chat Messages

​Multiple Functions on One Agent

​Built-in and Flow-triggered functions

​What’s next

MCP servers

Prompt engineering

Human handover

Rich responses

Overview

Prerequisites

When to Use Functions

How Functions Work

Add a Function to an Agent

Example Function Definition

Structuring Function Results as Chat Messages

Multiple Functions on One Agent

Built-in and Flow-triggered functions

What’s next