Jan 17
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Build a real-world AI Engineering Team using CrewAI In this tutorial, you’ll learn how to design and run a multi-agent AI system that mirrors a real software engineering workflow — from architecture design to backend development, frontend UI, and automated testing. We’ll create an EngineeringTeam using CrewAI where each AI agent has a clearly defined role: * Engineering Lead * Backend Engineer * Frontend Engineer (Gradio UI) * QA Test Engineer This is a zero-to-production workflow showing how multiple LLM-powered agents collaborate to build a complete Python application — fully automated. If you want to master CrewAI multi-agent system design, this video is for you. ## 🧠 What You’ll Learn * How CrewAI multi-agent systems work * Designing AI agents with real engineering roles * Automating system design, coding, UI creation, and testing * YAML-based agent & task orchestration * Running CrewAI locally with multiple LLM providers * Building an end-to-end AI engineering pipeline ## 🛠 Tech Stack Used * CrewAI * Python * Gradio (Frontend UI) * OpenAI / Anthropic / Google GenAI * LiteLLM * UV package manager ## 🔗 Source Code & Video 📂 Code Repository 👉 [https://github.com/matinict/MyCrewAi/tree/main/engineering_team](https://github.com/matinict/MyCrewAi/tree/main/engineering_team) 🎥 Video Link 👉 [https://youtu.be/H0Xnk2KYeDM](https://youtu.be/H0Xnk2KYeDM) ## ⏱ Chapters (12-Min Video) 00:00 – Intro Welcome to PlayOwnAI & overview of the AI Engineering Team 00:30 – What We’re Building Real-world AI engineering workflow using CrewAI 01:15 – Engineering Team Roles Explained Engineering Lead, Backend Engineer, Frontend Engineer, QA Engineer 02:10 – Creating a CrewAI Project Using `crewai create crew EngineeringTeam` 03:20 – Project Structure Overview Folders, configs, and outputs 04:10 – Environment Variables Setup Setting API keys with `.env` 05:10 – Agents Configuration (agents.yaml) Roles, goals, backstories, and LLM selection 06:30 – Task Pipeline (tasks.yaml) Design → Code → Frontend → Tests 07:50 – EngineeringTeam Crew Class How agents and tasks are orchestrated 09:00 – Running the AI Engineering Pipeline Installing dependencies & running `crewai run` 10:20 – Output Review Backend code, Gradio UI, and unit tests 11:20 – Final Thoughts & Use Cases Extending this system for real-world products ## 🔥 Why This Matters This tutorial demonstrates how AI agents can collaborate like a real engineering team, dramatically speeding up development while maintaining structure and quality. Perfect for: * AI Engineers * Automation Builders * Startup Founders * CrewAI Learners * Multi-Agent System Designers 👍 If you found this helpful, like, subscribe, and turn on notifications for more advanced AI engineering content from PlayOwnAI. ## 🔍 SEO Keywords CrewAI tutorial, AI engineering team, multi-agent AI system, CrewAI Python, AI automation workflow, Gradio UI, LLM agents, PlayOwnAI, AI software development