What Actually Makes an AI Agent an Agent: Building One From Zero to See the Machinery

A developer builds an AI agent from scratch to demystify the core components—the loop, ReAct pattern, system prompts, message history, and tool calling—and explains why production agents require more than a naive loop, introducing the Model Context Protocol (MCP) as a standard interface for tool interoperability.

Member-only story AI LLM React AI Agent Artificial Intelligence What Actually Makes an AI Agent an Agent: Building One From Zero to See the Machinery 41 min read Just now A no-framework tour of the loop, the protocol, and the production gap hiding underneath every “agentic” product you’ve used this year Estimated reading time: 24–28 minutes Table of Contents - Introduction: The Question Nobody Answers Cleanly - The One-Shot Call vs. the Loop - ReAct: The Pattern Underneath Almost Every Agent - The System Prompt Is the Steering Wheel - Message History Is the Agent’s Only Memory - Tool Calling: Teaching a Model to Ask for Help - Building the Loop for Real - Swapping the Brain: Running Agents on Local Models - The Model Matters More Than the Code - Mixed Mode: Local Orchestration, Cloud Delegation - Growing the Toolbox — and Hitting a Wall - Model Context Protocol: Why Every Agent Needed a USB-C Port - MCP Client: Borrowing Tools You Didn’t Build - MCP Server: Lending Your Tools to Everyone Else - Where the Naive Loop Breaks: Errors, Retries, and Trust