# AI Agents Tutorial

A growing series of blog posts and videos teaching AI agent concepts step by step, from function calling and agent loops to multi-agent orchestration and evaluation.

AI Agents Tutorial is a tutorial series, in blog and video form, written and maintained by the founder of Outcome School, teaching AI agent concepts from the ground up. It starts with foundational AI engineering topics, including LLMs, retrieval-augmented generation, MCP, fine-tuning, and quantization, before moving into agent-specific material.

Individual posts cover what an AI agent is and how it differs from a plain LLM or chatbot, the five core parts of an agent, a concrete research-agent example, common failure modes, how function calling works under the hood and why the model itself never runs the function, and the think-act-observe loop that keeps an agent going, including how the loop knows when to stop and how it commonly fails. The series states it will keep growing as further posts and videos are published.

Website: https://github.com/amitshekhariitbhu/ai-agents-tutorial