AI Agent Engineering: Build Production Ready Agentic Systems

Production-Grade Agentic Systems with LangChain, MCP, RAG & FastAPI

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Problem Statement

Modern AI applications are rapidly evolving from simple prompt-based chatbots to autonomous agentic systems capable of:

• Planning and reasoning
• Using tools and APIs
• Collaborating with other agents
• Accessing enterprise knowledge bases
• Operating securely in production environments

However, most learning resources stop at demos and do not cover:

• Real agent architecture patterns
• Production deployment
• Observability and evaluation
• Security, guardrails, and authentication
• Multi-agent workflows

This project repository provides complete resource material, architecture examples, and implementation guides for building real-world, production-ready AI agent systems.

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Capstone Project

graph TD
    %% Styles
    classDef server fill:#e1f5fe,stroke:#01579b,stroke-width:2px;
    classDef external fill:#fff3e0,stroke:#e65100,stroke-width:2px;
    classDef observ fill:#f3e5f5,stroke:#4a148c,stroke-width:2px,stroke-dasharray: 5 5;

    %% External User
    User([User / Client Request])

    %% Server A: The Agent Host
    subgraph Server_A ["FastAPI Server A (Agent Host)"]
        direction TB
        AgentEndpoint[/"FastAPI Endpoint"/]
        ResearcherAgent[("Researcher Agent")]
        
        AgentEndpoint --> ResearcherAgent
    end

    %% Server B: The MCP Host
    subgraph Server_B ["FastAPI Server B (MCP Host)"]
        direction TB
        MCPEndpoint[/"MCP SSE Endpoint"/]
        ArxivTool[("arXiv MCP Server")]
        
        MCPEndpoint --> ArxivTool
    end

    %% External Services
    LLM{{"External LLM Provider"}}
    ArxivWeb(("arXiv.org"))
    
    %% Observability
    Langfuse[("Langfuse Observability")]

    %% Connections
    User -- "1. Trigger Task" --> AgentEndpoint
    
    ResearcherAgent -- "2. Reasoning & Generation" <--> LLM
    
    ResearcherAgent -- "3. MCP Protocol (SSE)" --> MCPEndpoint
    
    ArxivTool -- "4. Scrape/API Request" --> ArxivWeb
    
    %% Logging Connections
    ResearcherAgent -.->|"Log Traces/Spans"| Langfuse
    ArxivTool -.->|"Log Tool Inputs/Outputs"| Langfuse

    %% Class Assignments
    class Server_A,Server_B server;
    class LLM,ArxivWeb external;
    class Langfuse observ;

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About the Course

This repository supports the Udemy course:

AI Agent Engineering: Build Production Ready Agentic Systems

Build modern AI agents using:

• LangChain
• LangGraph
• Deep Agents
• MCP (Model Context Protocol)
• RAG Pipelines
• FastAPI
• Observability with Langfuse

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🛠️ Tech Stack Covered

Layer	Technologies
LLM Orchestration	LangChain, LangGraph
Agent Framework	Deep Agents
Context Protocol	MCP (Model Context Protocol)
Backend API	FastAPI
Observability	Langfuse
Deployment	Docker
Retrieval	RAG (Vector Databases)

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Getting Started

1️⃣ Clone the Repository

git clone https://github.com/raj713335/AI_Agent_Engineering_Build_Production_Ready_Agentic_Systems
cd AI_Agent_Engineering_Build_Production_Ready_Agentic_Systems

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2️⃣ Create Virtual Environment

python -m venv venv
source venv/bin/activate   # Mac/Linux
venv\Scripts\activate      # Windows

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3️⃣ Install Dependencies

pip install -r requirements.txt

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4️⃣ Run FastAPI Agent

cd AI_Agent_Client
python main.py

Open Swagger UI:

http://127.0.0.1:9100/api/docs

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5️⃣ Run MCP Server

cd AI_Agent_MCP_Server
python main.py

Open Swagger UI:

http://127.0.0.1:8000/api/docs

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🔗 Course Link

👉 GitHub Repository: https://github.com/raj713335/AI_Agent_Engineering_Build_Production_Ready_Agentic_Systems

Thank You.