UDA-Hub Support System

UDA-Hub is a production-ready, LangGraph-powered multi-agent customer support system developed for CultPass, a fitness membership platform. The system features four specialized AI agents that intelligently handle support tickets through sophisticated workflow orchestration, dual-database architecture, and comprehensive memory management. The system achieves intelligent classification, personalized responses, and multi-factor escalation logic.

Problem Statement

CultPass faced challenges with customer support scalability:

• High volume of support tickets requiring diverse expertise
• Need for consistent, high-quality responses across all interactions
• Difficulty routing tickets to appropriate resolution paths
• Requirement for context-aware support maintaining conversation history
• Need for intelligent escalation when issues exceed agent capabilities

Solution

Built a comprehensive multi-agent support system featuring:

• Four Specialized Agents: Classifier, Resolver, Supervisor, and Escalation agents
• Intelligent Routing: Confidence-based routing ensures tickets reach appropriate agents
• Three-Tier Memory System: Short-term, medium-term, and long-term memory for context awareness
• Dual-Database Architecture: Separate databases for customer and application data
• Workflow Orchestration: LangGraph manages complex multi-agent interactions
• Robust Error Handling: Graceful failure management with fallback mechanisms

Architecture

The system implements a sophisticated multi-agent architecture:

1. Classifier Agent:

   • Analyzes incoming support tickets
   • Categorizes issues by type and complexity
   • Determines routing to appropriate resolver
   • Provides confidence scores for classification

2. Resolver Agent:

   • Handles ticket resolution based on classification
   • Queries customer and application databases
   • Generates personalized responses
   • Maintains conversation context

3. Supervisor Agent:

   • Monitors resolver agent performance
   • Validates response quality
   • Ensures rubric compliance
   • Triggers escalation when needed

4. Escalation Agent:

   • Handles complex issues requiring human intervention
   • Manages escalation workflows
   • Provides context summaries for human agents
   • Tracks escalation reasons

Key Technologies

• LangGraph: Orchestrates multi-agent workflows with state management
• Multi-Agent Collaboration: Specialized agents working in concert
• Dual-Database Architecture:
  • Customer database: User profiles, membership status, history
  • Application database: Service details, policies, procedures
• Three-Tier Memory System:
  • Short-term: Current conversation context
  • Medium-term: Session-level information
  • Long-term: Historical customer interactions

Memory Management

Short-Term Memory: Maintains context within a single conversation turn, enabling natural dialogue flow.

Medium-Term Memory: Tracks information across a support session, allowing agents to reference earlier parts of the conversation.

Long-Term Memory: Stores historical customer interactions, enabling personalized responses based on past experiences.

Confidence-Based Routing

The system uses confidence scores from the Classifier Agent to route tickets:

90%
High confidence (>): Direct routing to Resolver Agent
90%
Medium confidence (70-): Supervised routing with Supervisor validation
70%
Low confidence (<): Escalation Agent handles with human oversight

Challenges & Resolutions

Challenge: Maintaining context across multiple agent interactions
Resolution: Implemented three-tier memory system with structured context passing

Challenge: Ensuring rubric compliance in agent responses
Resolution: Supervisor Agent validates responses against compliance metrics (achieved 85.7%)

Challenge: Handling complex queries requiring multi-step reasoning
Resolution: LangGraph workflow orchestration enables sequential agent processing

Challenge: Balancing automation with quality
Resolution: Multi-factor escalation logic ensures human intervention when needed

• 85.7% rubric compliance rate in agent-generated responses
• 75% first-contact resolution rate without escalation
92%
[object Object], achieving accuracy
• Personalized responses based on customer history and preferences
40%
[object Object], logic reducing unnecessary escalations by
• Successfully deployed to production handling CultPass support tickets

This project demonstrated the power of LangGraph for orchestrating complex multi-agent workflows. The three-tier memory system proved essential for maintaining context across agent interactions, enabling natural conversation flows. The dual-database architecture highlighted the importance of separating customer and application data for both performance and security. The confidence-based routing system showed how intelligent automation can balance efficiency with quality, ensuring appropriate human intervention when needed.