Build a Simple, Effective AI Agent Inspired by Claude Code

What this is, in plain English

An “AI agent” is a program that works in a loop: it reads what you want, decides on an action (sometimes using a tool, like editing a file or searching the web), does it, and repeats until the job is done. This workflow takes the design lessons behind Claude Code (a very good AI coding agent) and turns them into principles you can copy when building your own agent.

Be honest about the level: this is not a copy-paste recipe and not a beginner task. Building an agent means writing code that talks to an AI model, and the original source is an article of principles rather than a finished project. So this page gives you the design rules that actually matter (the headline one being “keep it simple”), plus a starter prompt, so you build something that works instead of an over-engineered mess.

What you can use it for

The same simple-agent design works for any task where the AI needs to take steps, not just answer once:

• A code helper. An agent that reads your files, spots a bug, and proposes an edit.
• An email drafter. Turn meeting notes into follow-up emails ready to review.
• A research assistant. Gather sources on a topic and summarize them.
• A content engine. Generate post ideas and draft captions in your voice.
• A support bot. Answer common questions and pull up the right FAQ.

Tools you need

• Claude, or any capable LLM (freemium): the model that powers your agent’s thinking. (“LLM” means large language model, the AI behind chat tools.) The free tier is fine to experiment.
• A code editor (free): such as VS Code, to write the agent’s code (its loop and its tools).

How it actually works

These are the design principles from the article, in the order you would apply them. Treat each as a rule to build by:

1. Keep it simple, on purpose. Resist multiple agents, fancy hand-offs, and elaborate search. Simpler agents are easier to debug and improve. This is the single most important rule.

2. Use one control loop. The “control loop” is the core cycle: read input, decide, act, respond, repeat. Keep it one straightforward loop you can trace top to bottom, not a maze of branches.

3. Keep one message history. Log everything (your inputs, the agent’s replies, its tool calls and reasoning) in a single continuous record. When something goes wrong, you can see the whole story in one place.

4. Write structured prompts with examples. Give the model clear instructions using headings or tags and at least one worked example. A starting point:

   You are an AI assistant that helps with [specific task, e.g. code editing].
   Follow these steps:
   1. Understand the user's request.
   2. If a tool is needed, use the appropriate tool.
   3. Give a clear, concise response.
   
   <example>
   User: Fix the typo in 'def myfuntion():'
   Thought: The user wants to fix a typo. I should use the edit tool.
   Tool: edit_code("def myfunction():")
   Assistant: I have fixed the typo.
   </example>
   
   User: [the user's request]

5. Give it a few simple tools. A “tool” is one ability the agent can call, like read a file, edit code, or search. Keep each tool doing one thing well (Claude Code leans on small tools like Edit, Read, and a to-do writer).

6. Let the agent keep a to-do list. For multi-step jobs, have it break the work into items and tick them off. That keeps it on track across long tasks.

7. Be explicit to stay in control. Spell out tone, style, and rules of thumb. Blunt instructions (“this is important: always do X”) genuinely help steer the model.

Words you’ll see, explained

• AI agent: a program that loops, deciding and acting with an AI, until a task is done.
• LLM: large language model, the AI behind tools like Claude and ChatGPT.
• Control loop: the core read-decide-act-repeat cycle at the heart of the agent.
• Message history: the single running record of everything said and done.
• Tool: one specific ability the agent can call (read, edit, search).
• Steerability: how well you can guide the agent’s behavior through your instructions.
• RAG: Retrieval-Augmented Generation, fetching relevant info before answering; powerful, but the article warns against adding it too early.

Original source

Based on samuelstros’s article “What makes Claude Code so damn good (and how to recreate that magic in your agent)”, shared on Hacker News, which explains the design philosophy behind Claude Code and how to apply it to your own agent.

Notes & variations

• Do you even need to build one? For a simple task, a well-written prompt or an existing agent (like Claude Code) is enough. Build your own when you need behavior and tools that no existing agent gives you.
• Free-tier alternatives: if Claude’s free tier runs out, Gemini or open-source models (like Llama via Ollama or Hugging Face) can power your agent too.
• Common mistake: over-engineering. Multiple agents, complex hand-offs, and early RAG make a system hard to debug. Start simple and add only what you prove you need.
• Tip for better results: always give clear instructions and a concrete example in your prompts. Models perform best with a well-defined task and a sample of the output you want.