Hand‑Craft a Claude‑Style AI Programming Agent from Scratch – A Complete Walkthrough

Recently a site called Learn Claude Code was discovered. Despite its name, the site does more than just teach Claude Code; it guides you from zero to hand‑craft a Claude‑style AI programming agent.

All Agent Underlying Logic

The site states that all AI programming agents share the same core loop:

while True:
    response = client.messages.create(messages=messages, tools=tools)
    if response.stop_reason != "tool_use":
        break
    for tool_call in response.content:
        result = execute_tool(tool_call.name, tool_call.input)
        messages.append(result)

In plain terms the loop consists of:

Invoke the model with a prompt.

Execute tools (file I/O, command execution, etc.).

Return the result to the model.

Iterate until the task is finished.

The rest of the system consists of optimizations and patches built around this loop. Although the examples are in Python, the underlying principles are language‑agnostic; the same loop can be written in Java, Go, TypeScript, etc.

while (true) {
    MessageResponse response = client.messagesCreate(messages, tools);
    if (!"tool_use".equals(response.getStopReason())) {
        break;
    }
    for (ToolCall toolCall : response.getContent()) {
        ToolResult result = executeTool(toolCall.getName(), toolCall.getInput());
        messages.add(result);
    }
}

The Java version mirrors the Python logic exactly.

Progressive Learning Path

The twelve stages (s01‑s12) are grouped into five core capabilities, showing how a mature agent system is built step by step.

L1 Tool & Execution (s01‑s02)

s01 The Agent Loop (84 lines) – The minimal viable agent kernel needs only a while loop and a single tool. Giving the agent a Bash tool lets it read/write files, run commands, and manipulate Git.

s02 Tools (120 lines) – A clean registration mechanism lets you plug in arbitrary tool functions, extending the agent’s capabilities like building blocks.

L2 Planning & Coordination (s03‑s05, s07)

s03 TodoWrite (176 lines) – Without a plan the agent behaves like a headless fly. The core idea is to list steps before execution.

s04 Subagents (151 lines) – Isolates sub‑agent context using a separate messages[] array to keep the main dialogue clean.

s05 Skills (187 lines) – Avoid overloading the system prompt; load rules on demand and feed knowledge back only when a tool returns a result.

s07 Tasks (207 lines) – Handles dependencies between multiple tasks using a file‑based task graph, allowing the agent to understand ordering and parallelism.

L3 Memory Management (s06)

s06 Compact (205 lines) – Token consumption grows with long conversations. A three‑layer compression strategy keeps sessions unlimited:

Sliding window – discard the earliest messages.

Summarization – compress history into a summary.

Selective retention – keep only key information.

L4 Concurrency (s08)

s08 Background Tasks (198 lines) – Slow operations should not block the agent. A non‑blocking design uses background threads and a notification bus so the agent can continue reasoning while work proceeds asynchronously.

L5 Collaboration (s09‑s12)

s09 Agent Teams (348 lines) – When a single agent cannot finish a task, persistent teammates with identities communicate via a file‑based mailbox (JSONL) to handle cross‑cycle delegation.

s10 Team Protocols (419 lines) – Defines a shared request‑response protocol that drives negotiation among multiple agents.

s11 Autonomous Agents (499 lines) – Teams scan a task board and claim work autonomously, removing the need for a central dispatcher.

s12 Worktree + Task Isolation (694 lines) – Prevents directory conflicts by giving each agent its own working directory and binding tasks to IDs.

Conclusion

The site is recommended because it offers:

Progressive learning – from 84 to 694 lines, each step is visible.

Problem‑driven design – each mechanism is introduced to solve a concrete pain point.

Strong visualization – task boards, progress bars, and state flows make abstract concepts tangible.

Code transparency – every lesson shows exact code changes.

Immediate hands‑on – commands and test cases are provided so you can run them right away.

Language‑agnostic principles – although examples use Python, the ideas apply to Java, Go, TypeScript, etc.

After completing the twelve lessons you will understand Claude Code’s design and be able to implement your own AI programming agent in any language, such as a Java version with Spring Boot + LangChain4j or a lightweight Go implementation.