🎫 Ticket Agent

An intelligent multi-role support ticket agent powered by LangGraph and Snowflake Cortex and Streamlit. This system automates the lifecycle of a support ticket—from initial AI-driven triage and priority classification to human-in-the-loop administrative resolution.

• Smart New-Ticket Evaluation – Evaluates new issues (priority + metadata) before ticket creation and response generation
• Snowflake Cortex Search – Retrieves relevant past tickets and knowledge base context to inform responses
• AI + Human Support Loop – Combines AI-generated replies with admin human-in-the-loop intervention in the same chat threads
• Lifecycle Tracking & Resolution – Stores all agent messages and runs resolution checks to keep ticket status up to date

The system features a unified routing architecture in frontend/app.py that allows seamless switching between two distinct user experiences, managed via a global sidebar toggle.

• Support Desk Landing: A dedicated home page to raise new problems or check the status of existing ones.
• Live Status Tracking: Real-time ticket lookups with direct "Open Chat" links to resume active conversations.
• Ticket History: A searchable, tabular overview of all personal past tickets for easy reference.
• AI-Driven Chat: Direct interaction with the Mistral-powered agent for immediate troubleshooting and automated triage.

• Queue Management: A comprehensive view of all system tickets with visual color indicators for priority (URGENT to LOW) and status.
• Advanced Triage: Built-in tools to filter the queue by status (OPEN/CLOSED) and sort by urgency, subject, or update recency.
• Human-in-the-Loop Override: Admins can enter any active chat thread to provide manual assistance alongside the AI agent.
• One-Click Resolution: Streamlined controls to close resolved tickets directly from the dashboard or within the chat view.

• Shared Interface: Both roles utilize the same user_chat.py component, ensuring a consistent history of both AI and human responses.
• Visual Role Cues: The chat interface identifies messages from "Users," "AI Agents," and "Human Agents" (Admins) using distinct avatars and alignments to maintain clarity.

The agent uses a LangGraph state machine with the following workflow:

stateDiagram-v2
 [*] --> load_history
 load_history --> retrieve
 retrieve --> evaluate_ticket: new_ticket
 evaluate_ticket --> create_ticket
 create_ticket --> generate
 retrieve --> update_ticket: existing_ticket
 update_ticket --> generate
 generate --> store_agent_message
 store_agent_message --> check_for_resolution
 check_for_resolution --> [*]

ticket_agent/
├── backend/
│ ├── agent.py # Main agent entry point
│ ├── db/
│ │ ├── snowflake_utils.py # Snowflake session management utils
│ │ ├── sql_utils.py # SQL execution helpers
│ │ ├── zen_repo.py # Ticket repository operations
│ │ ├── csv/ # CSV files for sample data
│ │ ├── tables/ # SQL table definitions
│ │ ├── views/ # SQL view definitions
│ │ └── cortex_search_services/ # SQL cortex definitions
│ ├── graph/
│ │ ├── graph.py # LangGraph workflow definition
│ │ ├── router.py # Conditional routing logic
│ │ ├── state.py # State schema definition
│ │ └── nodes/ # Individual graph nodes
│ └── llm/
│ └── model.py # LLM configuration
├── scripts/
│ ├── setup_db.py # Database setup script
│ └── chat_with_agent.py # CLI chat interface
├── frontend/
│ ├── app.py # Streamlit app entry point
│ └── views/ # Streamlit app pages
├── requirements.txt
├── Makefile
└── .env.example

• Python 3.10+
• Snowflake account with Cortex enabled
• Mistral API key (used in backend/llm/model.py, you can change the code to use another LLM)
• Environment: Ensure your .env contains your Snowflake credentials and MISTRAL_API_KEY

1. Clone the repository

   git clone <repository-url>
   cd ticket_agent

2. Create and activate a virtual environment

   make venv
   source venv/bin/activate

3. Configure environment variables

   Copy the example file and fill in your credentials:

   cp .env.example .env

   Required variables:

   Variable	Description
   SNOWFLAKE_ACCOUNT	Your Snowflake account identifier
   SNOWFLAKE_USER	Snowflake username
   SNOWFLAKE_TOKEN	Snowflake password/token
   MISTRAL_API_KEY	API key
   SNOWFLAKE_WAREHOUSE	Compute warehouse name
   SNOWFLAKE_DATABASE	Database containing your data
   SNOWFLAKE_SCHEMA	Schema name
   SNOWFLAKE_CORTEX_SEARCH_SERVICE	Cortex Search service name

To test the agent through command line, run:

make run_agent
# or
PYTHONPATH=.:backend python scripts/chat_with_agent.py

Start the ticketing system UI:

make app
# or
streamlit run frontend/app.py

• LangGraph – Stateful agent orchestration
• LangChain – LLM integration
• Snowflake – Data storage and processing
• Snowflake Cortex – Semantic search & LLM inference
• Streamlit - App frontend