Learn Claude Code
Build a nano Claude Code-like agent from 0 to 1, one mechanism at a time
The Core Pattern
Every AI coding agent shares the same loop: call the model, execute tools, feed results back. Production systems add policy, permissions, and lifecycle layers on top.
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)Message Growth
Watch the messages array grow as the agent loop executes
Learning Path
20 progressive sessions, from a simple loop to a complete multi-agent harness
The Agent Loop
The smallest useful agent is a loop that calls the model, runs tools, and feeds results back.
Tool Use
The loop stays stable while capabilities register into a dispatch table.
Permission
Dangerous actions need a harness decision point before the shell runs.
Hooks
Cross-cutting behavior belongs around the loop, not tangled inside it.
TodoWrite
Explicit plans keep long-running work visible and correctable.
Subagent
Subagents give each subtask a clean message history while preserving the main thread.
Skill Loading
Inject specialized knowledge only when the task actually needs it.
Context Compact
Compression keeps the conversation usable when the context window gets crowded.
Memory
Some facts should survive summarization and future sessions.
System Prompt
The system prompt is a generated product of policy, tools, skills, and context.
Error Recovery
A robust harness classifies failures and decides what kind of retry is worthwhile.
Task System
A task graph turns vague goals into ordered, observable work.
Background Tasks
The agent can keep reasoning while slow work completes elsewhere.
Cron Scheduler
Recurring work should be created by the harness, not remembered by the model.
Agent Teams
Persistent teammates let work continue in parallel without stuffing every thought into one context.
Team Protocols
Multi-agent systems need explicit message contracts, not vibes.
Autonomous Agents
Teammates become useful when they can discover and claim work themselves.
Worktree Isolation
Parallel agents need isolated filesystems as much as isolated conversations.
MCP Tools
External services can become agent tools through a standard discovery and call protocol.
Comprehensive Agent
The final harness is still one loop, now surrounded by the systems that make it production-shaped.
Architectural Layers
Five orthogonal concerns that compose into a complete agent