CustomerServiceRefundAgent - A Full Stack Agentic Workflow

This project implements a production-style refund agent designed as an AI-assisted deep-dive to understand how real agents are built, debugged, evaluated, and deployed. End-to-end refund agent comprises of deterministic policy engine, RAG with FAISS + Pinecone, NVIDIA Nemotron LLM explanations, LangSmith tracing, eval harnesses, and MCP tool interface. Hands-on architecture exploration using AI-assisted development.

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Key Components

• Deterministic Refund Decision Engine: Policy-accurate and auditable logic for refund decisions.
• RAG Pipeline: Uses FAISS + Pinecone with NVIDIA Embeddings for robust context retrieval.
• LLM Explanation Layer: Powered by NVIDIA Nemotron for generating clear, natural-language explanations of the agent's decisions.
• Observability: Instrumented with LangSmith for tracing, latency monitoring, and evaluation.
• Tooling: MCP tool interface to expose the agent's capabilities to external LLMs or autonomous agents.

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Architecture Overview

The following diagram illustrates the flow of the agentic workflow:

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There are two ways you can run this project

A. Run the Entire Agent in Google Colab (Recommended)

This notebook was developed in Google Colaboratory for easy execution, experimentation, and debugging.

• 1. Open the Notebook: The fastest way to get started is to click the "Open in Colab" badge below. This will open the notebook in a runnable, temporary environment.

  

  (Remember to replace the placeholders in the link above with your repository details!)

• 2. Get Your Keys: To run the entire workflow, you will need to acquire and configure your own API keys for the following services:

  • Pinecone
  • NVIDIA API (for Nemotron and Embeddings)
  • LangSmith
  • Note: These should be stored in your Colab Secrets manager (the "key" icon in the left sidebar) to prevent them from being exposed in the notebook or saved to GitHub.

• 3. Execution: Once the notebook is open and your keys are configured, you can execute the cells sequentially to initialize the RAG pipeline, deploy the agent, and run traces.

B. Using MCP server through your agentic workflow

This project also includes an optional Model Context Protocol (MCP) server that exposes the refund agent as a standalone, interoperable tool. This allows any MCP-compatible client (e.g., ChatGPT, Claude, custom agents) to call the refund logic directly.

Why MCP?

MCP provides a standardized way for LLMs and agents to call external tools with below and turns the refund agent from a notebook-only workflow into a reusable microservice:

• deterministic inputs
• structured JSON outputs
• clean separation between tool logic and model reasoning
• cross-model interoperability

📁 Server File

• The server implementation lives in: refund_agent_mcp_server.py

▶️ Running the MCP Server

• 1. Install the MCP Python package:

  pip install mcp
  

• 2. Start the server: python refund_agent_mcp_server.py

• 3. Add the following code to the ChatGPT client.json (or equivalent tool config)

  { "tools": { "refund-agent": { "type": "python", "entrypoint": "refund_agent_mcp_server.py" } } }

• 4. Once configured, the agent becomes callable

  tool(refund-agent).refund_agent_api(order_id="ORD-1001", claimed_defective=False)

What the MCP Tool Returns

The MCP tool responds with a structured JSON payload containing:

• deterministic policy decision
• refund eligibility details
• amount + status fields
• policy window calculations
• full LLM-generated explanation

This allows other agents or orchestration frameworks to integrate the refund workflow as a building block.