The Anatomy of an AI Agent: Memory, Tools, Planning, and Execution Explained

<p><em>Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. <a

<h1> The Agent Loop </h1> <p><em>The entire AI agent stack in 60 lines of Python.</em></p> <p><a class="article-body-image-wrapper"

<h3> For a more in-depth guide </h3> <p>Medium: <a

<p>Most agent frameworks treat memory as an afterthought. They give your agent tools, prompts, and orchestration patterns — but when you restart the conversation, everything

<p>Claude's tool use (function calling) API is what separates toy chatbots from actual agents. I've built production agents with it — here's what reliable tool use looks like when

<p>Most developers who build AI agents make the same mistake: they spend weeks designing the orchestration layer, tuning the system prompt, and picking the right model — then hand

<h2> From Theory to Reality </h2> <p>So far, you’ve learned <strong>what agents are, how they think, and when to use them.</strong></p> <p>Now comes the turning point:</p>

<p>On March 31, 2026, Anthropic accidentally shipped the entire source code of Claude Code — its flagship AI coding agent — inside a routine npm update. No hack. No reverse