Build a Multi-Agent AI Stock Analyzer with Phidata and Grok

Description

What happens when multiple AI agents collaborate to solve a real-world problem? In this tutorial, we build a complete end-to-end Agentic AI application from scratch — a financial stock analyzer powered by multiple AI agents. Instead of relying on a single model, we design a multi-agent architecture where different AI agents handle specialized tasks and collaborate to produce the final output. This project demonstrates how modern AI systems are moving toward agent-based workflows, where independent agents coordinate to solve complex problems. 🚀 What you'll learn in this project: ✅ How Agentic AI systems work ✅ Creating independent AI agents with specific responsibilities ✅ Designing a multi-agent architecture for real-world applications ✅ Building a web search agent to fetch live information ✅ Building a financial analysis agent for stock fundamentals and insights✅ Combining both agents into a single AI system ✅ Using an open-source LLM for real-time analysis ✅ Generating structured financial insights with sources In this example, the system analyzes Tesla stock by combining:• Latest news from the web• Analyst recommendations• Financial indicators The final output is a structured financial summary with sources, demonstrating how multiple AI agents collaborate in a real application. ⚠️ This project is for educational purposes only and should not be used as financial advice. If you're interested in AI engineering, agentic systems, and building real AI applications, this project is a great place to start. ⏱️ TIMESTAMPS / CHAPTERS 00:00 Intro — Building a Multi-Agent AI System 00:09 Project overview: AI stock analyzer 00:20 Why multi-agent systems matter 00:45 Understanding the architecture 00:48 Web search agent (live information) 00:54 Financial analysis agent (stock fundamentals) 20:00 Combining multiple agents into one system 20:17 How the financial AI system works 21:28 Writing the final AI prompt 21:51 Running the Python program 22:24 Installing required packages (DDGS update) 22:35 AI analysis output (Tesla example) 23:16 Interpreting analyst recommendations 23:39 Sources used for financial insights 24:02 Real-time data explanation 24:05 Final system recap 24:18 Future improvements for the system 24:28 Next AI engineering projects 📣 Learn more about AI Engineering with NIIT:👉 https://www.niit.com/india/course/pgp-in-machine-learning-artificial-intelligence/?utm_source=yt&utm_medium=video&utm_campaign=agentic_ai_stock_analyzer&utm_content=description_link 👇 Comment below:What AI project should we build next — AI research assistant, autonomous trading agent, multi-agent coding system, or AI business analyst? #NIIT#UnlockWithNIIT#AgenticAI#AIAgents#AIEngineering#MultiAgentAI#ArtificialIntelligence#PythonAI#LLM#AIProjects#MachineLearning