Harness Engineering: From Claude Code to AI Coding

---
name: harness-engineering-cc-to-ai-coding
description: Expert skill for reading, navigating, and applying insights from the "Harness Engineering" book — a deep technical analysis of Claude Code's architecture, agent loops, prompt engineering, context management, and AI coding best practices.
triggers:
  - "help me understand claude code architecture"
  - "explain agent loop from harness engineering"
  - "how does claude code handle context management"
  - "what does the horse book say about prompt caching"
  - "harness engineering best practices"
  - "claude code permission system explained"
  - "how to build ai coding agents like claude code"
  - "马书 claude code 源码分析"
---

# Harness Engineering: From Claude Code to AI Coding

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

## What This Project Is

**Harness Engineering** (中文别名:《马书》) is a Chinese technical book that analyzes Claude Code `v2.1.88`'s publicly released package and source map reconstructions. It extracts architectural patterns, context strategies, permission systems, and production practices for building AI coding agents.

- **Online reading (Chinese):** https://zhanghandong.github.io/harness-engineering-from-cc-to-ai-coding/
- **Online reading (English preview):** https://zhanghandong.github.io/harness-engineering-from-cc-to-ai-coding/en/
- **GitHub:** https://github.com/ZhangHanDong/harness-engineering-from-cc-to-ai-coding

This is a **book project** built with [mdBook](https://rust-lang.github.io/mdBook/). The primary deliverable is the book itself, not a library or CLI tool you integrate into code.

---

## Local Setup & Preview

### Prerequisites

Install [mdBook](https://rust-lang.github.io/mdBook/):

```bash
# Via Rust/Cargo
cargo install mdbook

# Via Homebrew (macOS)
brew install mdbook

# Via pre-built binary (Linux/macOS/Windows)
# Download from: https://github.com/rust-lang/mdBook/releases

Clone & Serve

git clone https://github.com/ZhangHanDong/harness-engineering-from-cc-to-ai-coding.git
cd harness-engineering-from-cc-to-ai-coding

# Build the book
mdbook build book

# Serve locally with live reload
mdbook serve book
# Default: http://localhost:3000

Build Output

book/
├── src/           # Markdown source files
│   ├── SUMMARY.md # Table of contents / navigation
│   ├── assets/    # Images (cover, diagrams)
│   └── **/*.md    # Chapter files
└── book.toml      # mdBook configuration

---

Book Structure

The book has 7 main parts and 4 appendices:

Part	Topic
Part 1	Architecture — Claude Code's overall design, Agent Loop, tool orchestration
Part 2	Prompt Engineering — System prompts, tool prompts, model-specific tuning
Part 3	Context Management — Auto-compression, micro-compression, token budgets
Part 4	Prompt Caching — Cache strategies, prompt cache design patterns
Part 5	Security & Permissions — Permission modes, rule systems, YOLO classifier, Hooks
Part 6	Advanced Subsystems — Multi-agent orchestration, skill systems, Feature Flags, unreleased pipelines
Part 7	Lessons for AI Agent Builders — Production best practices, limitations, inspirations

---

Key Concepts Covered

1. Agent Loop Architecture

Claude Code's agent loop processes tasks through repeated cycles of:

1. Observe — Read context (files, shell output, user input)
2. Plan — Decide next action via LLM reasoning
3. Act — Execute tool calls (bash, file read/write, search)
4. Reflect — Incorporate results into next context window

Key insight from the book: The agent loop is not a simple REPL — it manages token budgets dynamically and compresses context when approaching limits.

2. Context Management Strategies

Context Window
├── System Prompt (static, cached)
├── Tool Definitions (static, cached)
├── Conversation History (dynamic)
│   ├── Full messages (recent)
│   └── Compressed summaries (older)  ← Auto-compression kicks in here
└── Current Task Buffer

Auto-compression trigger: When context approaches ~80% of max tokens, Claude Code summarizes older conversation turns to free space.

Micro-compression: Individual long tool outputs (e.g., large file reads, bash stdout) are truncated/summarized inline before being added to history.

3. Permission System

Claude Code uses a layered permission model:

Permission Levels (least → most privileged)
├── read-only       — File reads, directory listing, search
├── write           — File writes, creation, deletion
├── execute         — Shell commands (bash tool)
└── network         — HTTP requests, external services

YOLO mode: Bypasses interactive permission prompts. The book analyzes the YOLO classifier that determines which operations are auto-approved.

Hooks system: Pre/post action hooks allow custom permission logic, logging, or side effects to be injected into the tool execution pipeline.

4. Prompt Caching

Claude Code aggressively caches:

• System prompt — Rarely changes; always at top of cache
• Tool definitions — Static per session; cached after system prompt
• File contents — Frequently accessed files cached as pseudo-static context

Pattern from the book:

[CACHED BOUNDARY]
System Prompt
Tool Definitions
Common File Contents    ← Extended cache prefix
[DYNAMIC BOUNDARY]
Conversation turns      ← Not cached (changes each turn)
Current user message

5. Multi-Agent Orchestration

The book reveals Claude Code's sub-agent spawning for parallel tasks:

Orchestrator Agent
├── Sub-agent A: "Search codebase for X"
├── Sub-agent B: "Write tests for Y"
└── Sub-agent C: "Fix linting errors in Z"
        ↓
Results merged back into orchestrator context

---

Applying Book Insights: Code Patterns

Pattern 1: Implement a Token Budget Manager

Based on Part 3 (Context Management):

class TokenBudgetManager:
    def __init__(self, max_tokens: int, compression_threshold: float = 0.8):
        self.max_tokens = max_tokens
        self.compression_threshold = compression_threshold
        self.history: list[dict] = []

    def should_compress(self, current_token_count: int) -> bool:
        return current_token_count / self.max_tokens > self.compression_threshold

    def compress_history(self, llm_client, history: list[dict]) -> list[dict]:
        """Summarize older turns to free token budget."""
        if len(history) <= 4:
            return history  # Keep at least 2 exchange pairs

        to_compress = history[:-4]  # Keep last 4 messages verbatim
        recent = history[-4:]

        summary_prompt = (
            "Summarize the following conversation history concisely, "
            "preserving key decisions, file paths, and technical details:\n\n"
            + "\n".join(f"{m['role']}: {m['content']}" for m in to_compress)
        )

        summary = llm_client.complete(summary_prompt)

        compressed = [{"role": "system", "content": f"[History Summary]: {summary}"}]
        return compressed + recent

    def add_message(self, message: dict, token_count: int, llm_client=None):
        self.history.append(message)
        if self.should_compress(token_count) and llm_client:
            self.history = self.compress_history(llm_client, self.history)

Pattern 2: Layered Permission Gate

Based on Part 5 (Security & Permissions):

from enum import IntEnum
from typing import Callable

class PermissionLevel(IntEnum):
    READ = 1
    WRITE = 2
    EXECUTE = 3
    NETWORK = 4

class PermissionGate:
    def __init__(self, auto_approve_level: PermissionLevel = PermissionLevel.READ):
        self.auto_approve_level = auto_approve_level
        self.hooks: list[Callable] = []

    def register_hook(self, hook: Callable):
        """Register a pre-execution hook (logging, auditing, etc.)"""
        self.hooks.append(hook)

    def request(self, action: str, level: PermissionLevel, details: dict) -> bool:
        # Run pre-execution hooks
        for hook in self.hooks:
            hook(action=action, level=level, details=details)

        # Auto-approve if within threshold
        if level <= self.auto_approve_level:
            return True

        # Interactive prompt for elevated permissions
        response = input(
            f"\n⚠️  Permission required: {action}\n"
            f"   Level: {level.name}\n"
            f"   Details: {details}\n"
            f"   Allow? [y/N]: "
        )
        return response.strip().lower() == "y"


# Usage
gate = PermissionGate(auto_approve_level=PermissionLevel.READ)

# Audit hook
def audit_log(action, level, details):
    print(f"[AUDIT] {action} ({level.name}): {details}")

gate.register_hook(audit_log)

# Auto-approved (READ <= READ threshold)
gate.request("read_file", PermissionLevel.READ, {"path": "src/main.py"})

# Prompts user (EXECUTE > READ threshold)
gate.request("run_bash", PermissionLevel.EXECUTE, {"command": "rm -rf /tmp/old"})

Pattern 3: Prompt Cache Prefix Builder

Based on Part 4 (Prompt Caching):

def build_cached_prompt_prefix(
    system_prompt: str,
    tool_definitions: list[dict],
    pinned_files: dict[str, str],  # {path: content}
) -> list[dict]:
    """
    Constructs the static (cacheable) portion of the context.
    Place this before dynamic conversation history.
    """
    messages = []

    # 1. System prompt — always first for maximum cache reuse
    messages.append({
        "role": "system",
        "content": system_prompt,
        "cache_control": {"type": "ephemeral"}  # Anthropic API cache hint
    })

    # 2. Tool definitions block
    if tool_definitions:
        tools_block = "# Available Tools\n" + "\n".join(
            f"## {t['name']}\n{t['description']}" for t in tool_definitions
        )
        messages.append({
            "role": "system",
            "content": tools_block,
            "cache_control": {"type": "ephemeral"}
        })

    # 3. Pinned file contents (frequently accessed files)
    for path, content in pinned_files.items():
        messages.append({
            "role": "system",
            "content": f"# File: {path}\n```\n{content}\n```",
            "cache_control": {"type": "ephemeral"}
        })

    return messages

---

Contributing to the Book

The book source is Markdown files in book/src/. To contribute:

# 1. Fork and clone
git clone https://github.com/YOUR_USERNAME/harness-engineering-from-cc-to-ai-coding.git

# 2. Edit chapters
vim book/src/chapter-name.md

# 3. Update SUMMARY.md if adding new chapters
vim book/src/SUMMARY.md

# 4. Preview changes
mdbook serve book

# 5. Build to verify no errors
mdbook build book

# 6. Submit PR

SUMMARY.md Format

# Summary

[Introduction](./intro.md)

# Part 1: Architecture
- [Chapter 1: Agent Loop](./part1/agent-loop.md)
- [Chapter 2: Tool Orchestration](./part1/tools.md)

# Appendix
- [Appendix A: Glossary](./appendix/glossary.md)

---

Troubleshooting

mdBook not found

# Verify cargo installation
cargo --version

# Install mdbook
cargo install mdbook

# Add cargo bin to PATH if needed
export PATH="$HOME/.cargo/bin:$PATH"

Build fails with missing file

error: Chapter 'X' not found at expected path 'src/...'

Check book/src/SUMMARY.md — every linked .md file must exist.

Port 3000 already in use

mdbook serve book --port 3001

Chinese characters display incorrectly

Ensure your terminal and browser use UTF-8 encoding. The book is UTF-8 throughout.

---

Key Takeaways for AI Agent Builders

From Part 7 of the book:

1. Token budget is first-class — Design your agent loop with token counting from day one, not as an afterthought
2. Cache the static, compress the dynamic — System prompts and tool definitions should never eat into your conversation budget
3. Permission granularity matters — Distinguish read/write/execute/network at the tool level, not just at the agent level
4. Hooks over inheritance — Claude Code uses hooks for extensibility; prefer composition over deep inheritance in agent frameworks
5. Fail loud on context overflow — Silent truncation causes subtle bugs; always surface context pressure to the operator
6. Multi-agent for parallelism, single-agent for coherence — Don't spawn sub-agents unless tasks are truly independent

---

Resources

• Book online: https://zhanghandong.github.io/harness-engineering-from-cc-to-ai-coding/
• English preview: https://zhanghandong.github.io/harness-engineering-from-cc-to-ai-coding/en/
• GitHub repo: https://github.com/ZhangHanDong/harness-engineering-from-cc-to-ai-coding
• mdBook docs: https://rust-lang.github.io/mdBook/
• Claude Code (analyzed version): v2.1.88
• License: MIT