Module 1 · AI
📖 4 min read · By ['[“HungHM15”]']

Assignment: FPT Customer Chatbot - Multi-Agent System#

Assignment Metadata#

Field

Description

Assignment Name

FPT Customer Chatbot - Multi-Agent System

Course

LangGraph and Agentic AI

Project Name

fpt-chatbot-multi-agent

Estimated Time

240 minutes

Framework

Python 3.10+, LangGraph, LangChain, SQLite/PostgreSQL, Tavily API, OpenAI

Learning Objectives#

By completing this assignment, you will be able to:

  • Design hierarchical multi-agent architectures with Primary Assistant and specialized agents

  • Implement dialog stack management for agent transitions

  • Apply context injection patterns for user information

  • Build CompleteOrEscalate pattern for agent decision making

  • Create production-ready tool schemas with Pydantic validation

Problem Description#

Build a customer service chatbot for FPT with the following capabilities:

  1. FAQ Agent: Answer questions about FPT policies using RAG

  2. Ticket Support Agent: Create, track, update, cancel support tickets

  3. IT Support Agent: Troubleshoot technical issues using Tavily search

  4. Booking Agent: Manage meeting room reservations

The Primary Assistant coordinates routing between specialized agents.

Technical Requirements#

Environment Setup#

  • Python 3.10 or higher

  • Required packages:

    • langgraph >= 0.2.0

    • langchain >= 0.1.0

    • sqlalchemy >= 2.0.0

    • pydantic >= 2.0.0

    • tavily-python >= 0.3.0

Database Schema#

Implement the following tables:

  • Ticket: ticket_id, content, description, customer_name, customer_phone, email (optional), status, time

  • Booking: booking_id, reason, time, customer_name, customer_phone, email (optional), note, status

Tasks#

Task 1: State & Context Management (20 points)#

  1. Define AgenticState with:

    • messages: Conversation history with add_messages

    • dialog_state: Stack tracking agent hierarchy

    • user_id, email (optional): Context injection fields

    • conversation_id: Session tracking

  2. Implement dialog stack functions:

    • update_dialog_stack: Push/pop agent transitions

    • pop_dialog_state: Return to Primary Assistant

  3. Create context injection that auto-populates user info into tool calls

Task 2: Ticket Support Agent (25 points)#

  1. Define Pydantic schemas for:

    • CreateTicket: content (required), description, customer_name, phone, email (optional)

    • TrackTicket: ticket_id

    • UpdateTicket: ticket_id (required), optional fields for updates

  2. Implement tools:

    • create_ticket: Creates ticket with “Pending” status

    • track_ticket: Returns ticket information

    • update_ticket: Updates provided fields only

    • cancel_ticket: Sets status to “Canceled”

  3. Add CompleteOrEscalate tool for returning to Primary Assistant

Task 3: Booking Agent (20 points)#

  1. Define Pydantic schemas with validation for:

    • BookRoom: reason (required), time (required, must be future)

    • TrackBooking, UpdateBooking, CancelBooking

  2. Implement booking tools with status management:

    • Status flow: Scheduled → Finished (or Canceled)

Task 4: IT Support & FAQ Agents (15 points)#

  1. IT Support Agent:

    • Uses Tavily search for troubleshooting information

    • Returns practical guides from reliable sources

  2. FAQ Agent:

    • Implements RAG for FPT policy questions

    • Returns answers with source references

Task 5: Primary Assistant & Routing (20 points)#

  1. Define routing tools for Primary Assistant:

    • ToTicketAssistant, ToITAssistant, ToBookingAssistant

    • Include user context injection in tool calls

  2. Implement entry nodes for agent transitions:

    • Create create_entry_node factory function

    • Push new agent to dialog_state stack

  3. Build complete graph with:

    • All agent nodes and their tool nodes

    • Conditional routing from Primary Assistant

    • Tool fallback handling for errors

  4. Create tool_node_with_fallback for graceful error handling

Submission Requirements#

Required Deliverables#

  • Source code with all agents implemented

  • Database schema (SQLite or PostgreSQL)

  • README.md with setup and usage instructions

  • Test conversations demonstrating each agent flow

Submission Checklist#

  • All 4 specialized agents working correctly

  • Context injection auto-populates email when available

  • CompleteOrEscalate enables return to Primary Assistant

  • Tool schemas validate inputs properly

  • Dialog stack correctly tracks agent hierarchy

  • Code runs without errors

Evaluation Criteria#

Criteria

Points

State & context management

20

Ticket Support Agent

25

Booking Agent

20

IT Support & FAQ Agents

15

Primary Assistant & routing

15

Code quality and documentation

5

Total

100

Hints#

  • Use ToolNode(tools).with_fallbacks([...]) for error handling

  • The dialog_state stack enables proper agent hierarchy tracking

  • Optional email fields should use Optional[EmailStr] in Pydantic schemas

  • Reference the LangGraph customer support tutorial for architecture patterns

References#