In this livestream, I walk you through how to build your first AI agent from scratch—without getting lost in the hype. I start by defining what an AI agent actually is (and how it’s different from a chatbot): an agent can take actions, use tools, remember context, and plan steps toward a goal. Then I lay out a simple framework you can follow: define your use case, choose a no-code vs. code approach, and make sure you have the core building blocks (LLM, instructions, tools, memory, guardrails, and orchestration). I also demo what “simple” and “advanced” can look like in real life. On the no-code side, I show a visual workflow builder (n8n) and a more complex multi-agent setup where a main orchestrator can call sub-agents for RAG (database retrieval), web search, and Google Docs report generation—plus error handling (because live demos are always an adventure). On the code side, I show how you can get an agent running in ~30 lines with Streamlit + OpenAI’s Agents SDK, and then scale up to a multi-agent investing research system that runs fundamental, macro, and quantitative analysis in parallel and synthesizes it into a final recommendation. By the end, you should have a clear roadmap: start simple, add tools only when you need them, and build toward more complex agentic systems as your use case demands.