🎫 Customer Support Ticketing Agent with Structured Output

A tutorial demonstrating how to implement a structured customer support ticketing system using Google's ADK (Agent Development Kit) framework. This example shows how to create type-safe, structured support tickets with priority levels, categories, and resolution estimates using Pydantic schemas and Gemini 3 Flash model.

Tutorial Features

• 🎫 Structured Support Tickets:

  • Learn how to create comprehensive support ticket schemas
  • Understand priority levels and categorization
  • See how to estimate resolution times

• 🔧 Advanced Schema Design:

  • Complex Pydantic models with enums and optional fields
  • Proper field validation and descriptions
  • Type-safe structured responses

• 🎯 Real-World Application:

  • Practical customer support use case
  • Shows how to handle different types of support requests
  • Demonstrates structured output for business processes

• 📊 Priority Management:

  • Four-tier priority system (Low, Medium, High, Critical)
  • Automatic priority assignment based on issue description
  • Category-based routing for different departments

🚀 Getting Started

1. Set up your environment:

   cd 3_1_customer_support_ticket_agent
   
   # Copy the environment template
   cp env.example .env
   
   # Edit .env and add your Google AI API key
   # Get your API key from: https://aistudio.google.com/
   

2. Install dependencies:

   # Navigate back to the directory
   cd ..
   
   # Install required packages
   pip install -r requirements.txt
   

3. Run the Agent

   # Start the ADK web interface
   adk web
   

   Then:

   1. Open the web interface in your browser
   2. Select the "support_ticket_creator" agent
   3. Enter your support request (e.g., "I can't log into my account and I have an important meeting in 2 hours")
   4. The response will be a structured JSON with all ticket details

Tutorial Overview

This tutorial demonstrates advanced structured output implementation in Google ADK:

1. Complex Schema Design: Learn how to create sophisticated Pydantic models
2. Enum Usage: Understand how to use enums for constrained values
3. Optional Fields: See how to handle optional data with proper defaults
4. Business Logic: Learn how to implement real-world business processes

Code Structure

• customer_support_agent/agent.py: Contains the main agent definition and SupportTicket schema
• customer_support_agent/__init__.py: Module initialization for easy imports

Support Ticket Schema

The agent creates structured tickets with the following fields:

• title: Concise summary of the issue
• description: Detailed problem description
• priority: Priority level (low, medium, high, critical)
• category: Department (Technical, Billing, Account, Product)
• steps_to_reproduce: Optional list of steps for technical issues
• estimated_resolution_time: Estimated time to resolve

Example Usage

Input: "My payment failed and I'm getting charged twice for the same service"

Output:

{
  "title": "Duplicate payment charge issue",
  "description": "Customer reports payment failure followed by duplicate charges for the same service",
  "priority": "high",
  "category": "Billing",
  "steps_to_reproduce": null,
  "estimated_resolution_time": "4-6 hours"
}

Dependencies

• google-adk: Google's Agent Development Kit
• pydantic: Data validation and settings management

How Structured Output Works

This tutorial shows how Google ADK handles complex structured output:

1. Input Processing: Takes natural language support requests
2. Context Analysis: Analyzes the issue severity and type
3. Structured Generation: Creates comprehensive tickets with all required fields
4. Validation: Ensures output matches the defined schema and business rules

This approach demonstrates how to create reliable, business-ready structured responses in Google ADK applications.