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Agentic AIintermediate

AI-Powered Customer Support Agent with Memory and Tool Calling

Support agents lose over 60% of their time switching between multiple tools to gather customer context before drafting a reply. This project solves that problem by building an AI-powered Customer Support Copilot that automatically collects relevant context using RAG for knowledge base search, Mem0 for persistent customer memory, and LangChain tool calling for CRM and billing lookups, then uses an LLM to generate a ready-to-review response in one click. The system is built on a modular FastAPI backend with SQLite, ChromaDB, and Mem0, includes a Streamlit dashboard for ticket handling, and is production-ready with Docker, CI/CD pipelines, and AWS EC2 deployment.

20 lectures

What You Will Learn

Mastering LangChain for building intelligent agents
Implementing memory management techniques for long-term context retention using Mem0
Utilizing tool calling to enable agents to perform actions
Building modular and maintainable code using Pydantic schemas
Deploying applications to AWS using CI/CD pipelines
Implementing RAG for integrating database content with Agents

System Architecture

AI-Powered Customer Support Agent with Memory and Tool Calling Architecture Diagram

High-level architecture overview of the AI-Powered Customer Support Agent with Memory and Tool Calling .

What You'll Build

  • A customer support agent with memory and tool calling capabilities
  • A modular codebase with well-defined modules and interfaces
  • An end-to-end CI/CD pipeline for automated deployment to AWS
  • A Dockerized application for easy deployment and scaling
AI-Powered Customer Support Agent with Memory and Tool Calling
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