Apr 17
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🚀 Welcome to this complete guide on building an AI Multi-Agent System using Azure OpenAI In this video, I explain how to design and implement a powerful AI system that can: ✔ Understand requirements ✔ Generate backend code ✔ Perform QA testing ✔ Automate the entire development workflow 🎯 What You’ll Learn in This Video: 🔹 What is an AI Agent? 🔹 Difference between Single-Agent vs Multi-Agent systems 🔹 Real-world architecture using FastAPI & Azure OpenAI 🔹 How Agent Pipeline works (Requirement → Code → QA) 🔹 Orchestrator (GPT-4o) with tool calling & RAG 🔹 Automated code generation & testing 🔹 Feedback & refinement loop 🧠 Core Concepts Covered: LLM = Brain Agent = Role Prompt = Instructions Multi-Agent collaboration model ⚙️ Technology Stack Used: ✔ Python ✔ FastAPI ✔ Azure OpenAI (GPT-4o) ✔ Uvicorn ✔ REST APIs ✔ RAG (Retrieval-Augmented Generation) 🧩 Agents in This System: 👨💻 Requirement Agent → Converts input to structured JSON 💻 Code Agent → Generates backend APIs 🧪 QA Agent → Reviews, tests & suggests improvements 🧠 Orchestrator → Controls entire workflow 🔥 Use Case Example: 👉 Create Login API with JWT & Role-Based Access ✔ Auto-generated code ✔ QA validation ✔ Structured output 🔐 Security Features: ✔ JWT Authentication ✔ Role-Based Access Control ✔ Password Hashing ✔ HTTPS Enforcement 💡 This project is perfect for: Developers QA Engineers AI Enthusiasts Automation Engineers 👍 If you like this content: ✔ Like ✔ Share ✔ Subscribe 👨🏫 Presented by: Ajay Dhandare 🏢 EduNova Tech ⚠ Disclaimer: This project is for educational purposes. #AIAgents #AzureOpenAI #FastAPI #MultiAgentSystem #Automation #AIProjects #SoftwareEngineering