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Enterprise Customer Support Multi-Agent System
This project builds an enterprise-grade customer support automation system using four specialised AI agents coordinated through the Band platform. The problem: Customer support teams waste hours manually triaging tickets, searching knowledge bases, drafting responses, and reviewing quality. This system automates the entire pipeline. How it works: A customer submits a support ticket. The Triage Agent classifies urgency and topic. The Knowledge Agent searches a hybrid RAG knowledge base combining BM25 sparse retrieval with dense embeddings using optimal fusion weight alpha equals 0.70, validated in MSc thesis research achieving 93 percent Recall at 10 on 8.84 million MS MARCO passages. The Resolution Agent drafts a professional response with confidence scoring. The Review Agent approves or escalates to human review if confidence falls below threshold. All four agents communicate and coordinate through Band's multi-agent platform in real time. The system demonstrates practical enterprise workflow automation where AI agents exchange context, hand off tasks, and complete work together without human intervention. Built solo in under two days using Band SDK, LangGraph, Groq LLM, FAISS vector index, BM25 retrieval, and Python. The hybrid RAG retrieval is based on statistically validated research achieving plus 11.4 percent improvement over baseline retrieval systems. Track: Track 1 Internal Enterprise Workflows.
19 Jun 2026
