Jan 16
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⚠️ TRANSPARENCY NOTE: This video uses AI-generated visuals and voice for efficiency. However, the python code, engineering logic, and architectural breakdown are 100% human-curated based on actual development. We focus on VALUE over VANITY. Manual prompting is dead. If in 2026 you are still chatting with ChatGPT for every single task, you are wasting your time. The future belongs to Agentic Engineering. In this Masterclass, I will show you how to build a fully autonomous multi-agent system using CrewAI and Python. We will move beyond "Chatbots" and build a Digital Workforce: a Senior Researcher and a Lead Writer that talk to each other to automate your content creation 24/7. 🛠️ THE TECH STACK: Framework: CrewAI (Open Source) Language: Python 3.10+ Tools: SerperDev (Google Search API) LLM: OpenAI GPT-4o / Claude 3.5 Sonnet 📂 CODE STRUCTURE main.py: Python import os from crewai import Agent, Task, Crew, Process from crewai_tools import SerperDevTool from dotenv import load_dotenv # 1. Load Environment Variables load_dotenv() # 2. Define Tools search_tool = SerperDevTool() # 3. Define Agents researcher = Agent( role='Senior Tech Researcher', goal='Uncover groundbreaking developments in {topic}', verbose=True, memory=True, backstory=( "You are a veteran tech analyst with 10 years of experience. " "You dig deep into news, GitHub repos, and academic papers." ), tools=[search_tool], allow_delegation=False ) writer = Agent( role='Tech Content Strategist', goal='Craft compelling content on {topic}', verbose=True, memory=True, backstory=( "You are a witty, engaging writer who explains complex tech to beginners. " "You simplify concepts without losing technical accuracy." ), tools=[], # Writer doesn't need search tool, they rely on Researcher allow_delegation=False ) # 4. Define Tasks task1 = Task( description=( "Conduct a comprehensive research about {topic}. " "Identify key trends, pros and cons, and market sentiment." ), expected_output='A detailed report with 5 bullet points and 3 statistical facts.', agent=researcher, ) task2 = Task( description=( "Using the insights provided, write an engaging blog post about {topic}. " "Focus on the future implications." ), expected_output='A 4-paragraph blog post formatted in Markdown.', agent=writer, context=[task1] # Passing the output from task1 ) # 5. Instantiate Crew my_crew = Crew( agents=[researcher, writer], tasks=[task1, task2], process=Process.sequential ) # 6. Kickoff result = my_crew.kickoff(inputs={'topic': 'Agentic AI Trends 2025'}) print(result) TIMESTAMPS: 0:00 - The Prompt Engineering Trap (Intro) 2:30 - The Setup (Virtual Env & Security) 5:00 - Defining "The Brains" (Agents & Backstories) 12:15 - The Mission (Tasks & Context Handoff) 18:45 - Giving AI "Eyes" (SerperDev Tools) 23:00 - The Execution (Running the Crew) 28:00 - Conclusion: From Chatbot to Commander #AgenticAI #CrewAI #PythonTutorial #AIAgents #Automation #Coding #DigitalAsean #2026Trends #PromptEngineering #BuildPublic #PythonDev #Engineering #TechEducation #NoCode #SaaS