Build AI Agents From Scratch Series
Hands-on series building AI Agents from first principles (Reasoning → Planning → Tools → Memory → Multi-Agent Systems). Not a 'use LangChain in 10 minutes' series - this is real engineering for devs, builders, founders, and AI practitioners. Covers the 5-stage definition, ReAct framework, tool-use patterns, planning agents, multi-agent systems, and production deployment.
Key Learnings
AI Agents are the next major leap in applied AI - goal-driven systems that observe, reason, act, and improve. I learned how ReAct (Reasoning + Acting) fundamentally changed how LLMs interact with the world. The TAO loop (Thought → Action → Observation) is the backbone of autonomous workflows. Agents reduce hallucinations through verification loops - they think, act, verify, and continue. This turns LLMs into interactive problem-solvers. I explored when to use different frameworks: coding-first (SmolAgents, LangChain, LangGraph, LlamaIndex), low-code (Flowise, Dust, Dify), or no-code (Lovable, Vercel AI SDK). Key research papers studied: CoT, ReAct, Tool-use LLMs, and Generative Agents (Stanford).