In this full project, I build a Stock Market AI Agent that can analyze multiple stocks in one run and rank them using stock fundamentals. The core idea is simple: instead of manually jumping between tickers, news, and financial metrics, I let an agentic workflow orchestrate the steps—fetch signals, reason over them, and produce a clean ranked output you can actually use. I’m using the AGNO Agentic AI library and Gemini Flash LLM to keep the system fast while still being structured and debuggable. I walk through how an AI agent processes stock data, how you can plug in market news and trends, and how to wrap everything inside agentic design patterns so the system stays modular. The big takeaway is the system-design thinking: tools + orchestration + constraints. If you already understand what an AI agent is (and the common agentic patterns), this project becomes a practical blueprint you can reuse for other domains—screeners, research copilots, and even multi-agent finance workflows.