AI Support Agent with Self-Learning Loop

Overview

AI support agent automating Tier-1 customer support across chat-based channels (web, mobile, etc.), handling hundreds of thousands of tickets annually. Technical details: Automation metric: AI-handled vs total tickets Agent scope: full conversation handling (classification, resolution, escalation) Embeddings: locally hosted models Summarization: locally hosted models Clustering: HDBSCAN Vector DB: Qdrant

Achievements

AI now handles more tickets than human agents, significantly reducing support workload and enabling human agents to focus on complex issues.

Responsibilities

• Architected a multi-step reasoning agent using LangGraph with specialized chains for complex cases
• Built a RAG system on top of support knowledge sources (transcripts, documentation, communication logs)
• Implemented a lightweight validation model to verify agent responses before delivery
• Created a clustering-based self-learning loop to identify emerging issues and continuously improve the knowledge base
• Developed knowledge ingestion pipelines (chunking, embedding, deduplication, augmentation)
• Defined QA processes with human-in-the-loop validation

Technologies Used