Build a Claude‑Code‑Level AI Agent in 12 Incremental Lessons

Agent Loop

All AI programming agents share a single loop: call the model → execute a tool → return the result. The minimal runnable agent consists of a while True loop that sends the current messages and tools to the Anthropic API, checks response.stop_reason, and dispatches each tool call via execute_tool before appending the result back to messages:

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)

Anthropic uses a single‑threaded main loop for simplicity, debuggability, and fine‑grained control; higher‑level capabilities are layered on top of this core.

Lesson Roadmap (84 → 694 lines)

Lesson 1 – Agent Loop (84 lines): minimal loop with a single tool.

Lesson 2 – Tools (120 lines): register multiple tools in a dispatch map, loop unchanged.

Lesson 3 – TodoWrite Planning (176 lines): generate a step‑by‑step plan before execution.

Lesson 4 – Sub‑agents (151 lines): each sub‑agent owns an independent message array to keep parent context clean.

Lesson 5 – Skills (187 lines): inject knowledge on demand instead of bloating the system prompt.

Lesson 6 – Compact (Context Compression) (205 lines): three‑layer compression monitors context usage, triggers at ~92 % occupancy, and replaces older messages with model‑generated summaries.

Lesson 7 – Tasks (207 lines): file‑based task graph supporting dependencies and parallel execution.

Lesson 8 – Background Tasks (198 lines): asynchronous, non‑blocking execution of long‑running work.

Lesson 9 – Agent Teams (348 lines): agents communicate via asynchronous mailbox messages.

Lesson 10 – Team Protocols (419 lines): unified request‑response protocol for structured communication.

Lesson 11 – Autonomous Agents (499 lines): agents scan a task board, claim tasks, and execute them without external assignment.

Lesson 12 – Worktree Isolation (694 lines): each agent runs in its own directory (worktree), providing execution isolation while sharing task management.

Five‑Layer Orthogonal Architecture

L1 – Tool & Execution : Agent Loop and tool dispatch.

L2 – Planning & Coordination : TodoWrite planning, sub‑agents, skill injection, task dependency management.

L3 – Memory Management : three‑layer context compression for “unlimited” sessions.

L4 – Concurrency : background tasks execute without blocking the main loop.

L5 – Collaboration : mailbox messaging, unified protocol, autonomous task claiming, worktree isolation.

Each layer can be used independently or combined to build a fully autonomous multi‑agent system.

Key Mechanisms

Context Compression (Three‑Layer Strategy)

When the message array approaches the model’s context window limit, the system monitors usage, triggers compression, and replaces a batch of older messages with a summary generated by the model. Compression activates at roughly 92 % of the context window, matching the strategy employed in Claude Code production deployments.

Sub‑Agent Isolation

Sub‑agents create their own message arrays, preventing the parent’s context from being polluted by detailed sub‑task interactions. After completing its work, a sub‑agent returns a concise result to the parent.

Multi‑Agent Evolution

The tutorial demonstrates a four‑step evolution from a single agent to an autonomous team:

Asynchronous mailbox communication (Lesson 9).

Unified request‑response protocol (Lesson 10).

Autonomous task scanning and claiming (Lesson 11).

Worktree‑based execution isolation (Lesson 12).