analysis_claude_code

[English](./README.md) | [中文](./README-zh.md) | [日本語](./README-ja.md)

Learn Claude Code -- Harness Engineering for Real Agents

The Model IS the Agent

Before we talk about code, let's get one thing absolutely straight.

**An agent is a model. Not a framework. Not a prompt chain. Not a drag-and-drop workflow.**

What an Agent IS

An agent is a neural network -- a Transformer, an RNN, a learned function -- that has been trained, through billions of gradient updates on action-sequence data, to perceive an environment, reason about goals, and take actions to achieve them. The word "agent" in AI has always meant this. Always.

A human is an agent. A biological neural network, shaped by millions of years of evolutionary training, perceiving the world through senses, reasoning through a brain, acting through a body. When DeepMind, OpenAI, or Anthropic say "agent," they mean the same thing the field has meant since its inception: **a model that has learned to act.**

The proof is written in history:

• 2013 -- DeepMind DQN plays Atari. A single neural network, receiving only raw pixels and game scores, learned to play 7 Atari 2600 games -- surpassing all prior algorithms and beating human experts on 3 of them. By 2015, the same architecture scaled to 49 games and matched professional human testers, published in Nature. No game-specific rules. No decision trees. One model, learning from experience. That model was the agent.

...