Multi-agent AI enhancement platform that intelligently escalates chatbot interactions to human experts, integrates their knowledge and feedback, and creates a continuous learning loop between AI and human intelligence.
Executive Summary
Introduction
This product was initially conceived, designed, and developed as part of the 8 week Dallas AI Summer Program 2025 under the mentorship of Eric Poon. Chris Munch was the Product Designer/Manager and Lead Backend/AI engineer.
The Problem
Customer frustration with AI chatbots is a growing issue:
- 77% of customers get frustrated with chatbots
- Poor escalation timing wastes human resources
- No feedback loop for AI improvement
- Customer service agents handle irate customers
VIA's Solution
Intelligent human-AI collaboration system:
- Real-time frustration detection and quality monitoring
- Smart routing to optimal human experts
- Continuous learning from human resolutions
- Proactive escalation before customer irritation
Business Impact
Improvement for both customers and support teams
Faster Resolution Times
Proactive, intelligent escalation reduces average resolution times
Customer Sentiment
Customer sentiment improves with timely human intervention and more context-aware responses
Empowered Employees
Employees help improve the AI and also benefit from AI powered context and knowledge retrieval
Balanced Workloads
Balanced, optimized workloads lead to higher employee satisfaction
Continuous Learning
System learns from daily human interactions to improve AI performance
Cost Reduction
Quicker resolutions and improved AI handling reduce overall support costs
Multi-Agent Architecture
Four specialized AI agents working together to enhance customer interactions
Frustration Agent
Analyzes customer messages in real-time to detect mounting frustration using sentiment analysis and linguistic patterns. Triggers escalation when frustration thresholds are reached.
Quality Agent
Reviews chatbot responses before delivery, determining adequacy and managing adjustments. Prevents poor responses from reaching customers and enables proactive escalation.
Routing Agent
Intelligently selects optimal humans for escalation based on expertise, workload, resolution times, and employee wellbeing factors. Ensures balanced workload distribution.
Context Manager
Retrieves and delivers relevant context from interactions and knowledge base to agents and humans. Maintains conversation continuity and provides historical insights.
Demo
The protoype is not fully wired-up yet, but this demo shows the basic functionality of the multi-agent system in action.
View Live Demo on Hugging Face
Our Frontend is still under development, but below is the conception of our Frontend Engineer Nithin Dodla.
Technology Stack
Built with cutting-edge AI orchestration and enterprise-ready infrastructure
🤖 AI Framework
⚙️ Backend
🌐 Frontend
🚀 Deployment
Our Team
VIA was developed by a multidisciplinary team as part of the Dallas AI Summer Program 2025
Each team member brought unique expertise that was essential to VIA's development, from technical implementation to user experience design to market analysis.
More Details
Dive deeper into VIA's technical architecture and explore the complete codebase
Technical Discussion Series
More in-depth technical and design choices discussion including future roadmap with specialized fine-tuned models, comprehensive evaluation framework, and RAG knowledge base implementation.
Open Source Repository
Complete source code for VIA's multi-agent architecture built with LangGraph, including all four specialized agents, database schema, and deployment configurations.
View Repository