Learn Claude Code -- Harness Engineering for Real Agents
Agency Comes from the Model. An Agent Product = Model + Harness.
Before we write any code, one thing needs to be clear.
Agency -- the capacity to perceive, reason, and act -- comes from model training, not from external code orchestration. But a working agent product needs both the model and the harness. The model is the driver. The harness is the vehicle. This repository teaches you how to build the vehicle.
Where Agency Comes From
At the core of every agent is a neural network -- a Transformer, an RNN, a trained function -- shaped by billions of gradient updates on sequences of perception, reasoning, and action. Agency was never bestowed by the surrounding code. It was learned during training.
Humans are the original proof. A biological neural network, refined by millions of years of evolutionary pressure, perceives the world through senses, reasons through a brain, and acts through a body. When DeepMind, OpenAI, or Anthropic say "agent," they all mean the same core thing: a model that learned to act through training, plus the infrastructure that lets it operate in a specific environment.
The historical record is unambiguous:
2013 -- DeepMind DQN plays Atari. A single neural network, receiving only raw pixels and game scores, learned 7 Atari 2600 games -- surpassing prior algorithms and beating human experts in 3 of them. By 2015, scaled to 49 games at professional tester level, published in Nature. No game-specific rules. One model, learning from experience.
2019 -- OpenAI Five conquers Dota 2. Five neural networks played 45,000 years of Dota 2 against themselves over 10 months, then defeated OG -- the TI8 world champions -- 2-0 in a live match. In the public arena, the AI won 99.4% of 42,729 games. No scripted strategies. Models learned teamwork through self-play.
2019 -- DeepMind AlphaStar masters StarCraft II. AlphaStar beat a professional player 10-1 in closed matches, then reached Grandmaster rank on the European server -- top 0.15% of 90,000 players. An incomplete-information, real-time game with a combinatorial action space far exceeding chess or Go.
2024-2025 -- LLM agents reshape software engineering. Claude, GPT, Gemini -- large language models trained on the full breadth of human code and reasoning -- are deployed as coding agents. They read codebases, write implementations, debug failures, and coordinate as teams. The architecture is identical to every previous agent: a trained model, placed in an environment, given tools for perception and action.
Every milestone points to the same fact: Agency -- the ability to perceive, reason, and act -- is trained, not coded. But every agent also needs an environment to operate in: an Atari emulator, the Dota 2 client, the StarCraft II engine, an IDE and a terminal. The model supplies the intelligence. The environment supplies the action space. Together they form a complete agent.
What an Agent Is NOT
The word "agent" has been hijacked by an entire prompt-plumbing industry.
Drag-and-drop workflow builders. No-code "AI Agent" platforms. Prompt-chain orchestration libraries. They share a single delusion: that stringing LLM API calls together with if-else branches, node graphs, and hardcoded routing logic constitutes "building an agent."
It does not. What they produce are Rube Goldberg machines -- over-engineered, brittle, procedural rule pipelines with an LLM wedged in as a glorified text-completion node. That is not an agent. That is a shell script with grandiose pretensions.
You cannot brute-force intelligence by stacking procedural logic -- sprawling rule trees, node graphs, chained prompt waterfalls -- and praying that enough glue code will spontaneously produce autonomous behavior. It will not. You cannot engineer agency into existence. Agency is learned, not coded.
The Mindshift: From "Building Agents" to Building Harnesses
When someone says "I am building an agent," they can only mean one of two things:
1. Training a model. Adjusting weights through reinforcement learning, fine-tuning, RLHF, or another gradient-based method. Collecting trajectory data -- real-world sequences of perception, reasoning, and action in a target domain -- and using it to shape the model's behavior. This is what DeepMind, OpenAI, Tencent AI Lab, and Anthropic do.
2. Building a harness. Writing the code that gives a model an operational environment. This is what most of us do, and it is the core of this repository.
A harness is everything an agent needs to work in a specific domain:
The model decides. The harness executes. The model reasons. The harness provides context. The model is the driver. The harness is the vehicle.
This repository teaches you to build the vehicle. A vehicle for coding. But the design patterns generalize to any domain.
What Harness Engineers Actually Do
If you are reading this repository, you are most likely a harness engineer. Here is what the job actually entails:
Implement tools. Give the agent hands. File read/write, shell execution, API calls, browser control, database queries. Each tool is one action the agent can take in its environment. Design them atomic, composable, and clearly described.
Curate knowledge. Give the agent domain expertise. Product documentation, architecture decision records, style guides, compliance requirements. Load on demand, not upfront.
Manage context. Subagents keep focused work in a separate message list. Context compaction shortens older history. Task systems let goals persist beyond a single conversation.
Control permissions. Give the agent boundaries. Sandbox file access. Require approval for destructive operations. Enforce trust boundaries between the agent and external systems.
Collect trajectory data. Every action sequence the agent executes in your harness is training signal. Real deployment trajectories are the raw material for fine-tuning the next generation of agent models.
You are not writing intelligence. You are building the world that intelligence inhabits. The quality of that world directly determines how effectively the intelligence can express itself.
Build the harness well. The model will do the rest.
Why Claude Code
Because Claude Code is the most elegant, most complete agent harness implementation we have seen. Not because of any clever trick, but because of what it does not do: it does not try to be the agent. It does not impose rigid workflows. It does not substitute hand-crafted decision trees for the model's own judgment. It gives the model tools, knowledge, context management, and permission boundaries -- then gets out of the way.
Strip Claude Code down to its essence:
Claude Code = one agent loop
+ tools (bash, read, write, edit, glob, grep, browser...)
+ on-demand skill loading
+ context compaction
+ subagent spawning
+ task system with dependency graphs
+ async mailbox team coordination
+ task-bound worktrees for parallel edits
+ permission governance
+ hooks extension system
+ memory persistence
+ MCP external capability routing
That is it. The agent itself? Claude. A model. Trained by Anthropic on the full breadth of human reasoning and code. The harness did not make Claude smart. Claude was already smart. The harness gave Claude hands, eyes, and a workspace.
The takeaway is not "copy Claude Code." The takeaway is: the best agent products come from engineers who understand that their job is the harness, not the intelligence.
THE AGENT PATTERN
=================
User --> messages[] --> LLM --> response
|
contains tool_use block?
/ \
yes no
| |
execute tools return text
append results
loop back -----------------> messages[]
The model decides when to call tools and when to stop.
The code just executes what the model asks for.
This repo teaches you to build everything around this loop --
the harness that makes the agent effective in a specific domain.
Core Pattern
def agent_loop(messages):
while True:
response = client.messages.create(
model=MODEL, system=SYSTEM,
messages=messages, tools=TOOLS,
)
messages.append({"role": "assistant",
"content": response.content})
tool_calls = [
block for block in response.content if block.type == "tool_use"
]
if not tool_calls:
return
results = []
for block in tool_calls:
output = TOOL_HANDLERS[block.name](**block.input)
results.append({
"type": "tool_result",
"tool_use_id": block.id,
"content": output,
})
messages.append({"role": "user", "content": results})
Each lesson isolates one harness mechanism around this loop. s15 reconnects the cumulative runtime; s16 and s17 then study workflow orchestration and goal closure as focused examples. The loop belongs to the agent. The mechanisms belong to the harness.
The loop is constant. Tools, knowledge, and permissions change. Agent = Model (LLM) + a generalized operational environment (Harness).
Version Status
This repository currently contains two tutorial tracks:
Current track: root-level s01-s17
The root-level s01_* ... s17_* folders are the canonical version. Each chapter contains an English default README, Chinese/Japanese translations, runnable code.py, and diagrams where needed.
Legacy transition track: docs/ and agents/
These preserve the older 12-lesson version for existing readers and old links during migration.
If you are starting now, read the root-level s01_agent_loop/ through s17_goal_loop/ chapters. The legacy and current chapter numbers do not always match, so avoid mixing chapter numbers across tracks.
This is a 0-to-1 harness engineering course. Each chapter isolates one mechanism, then s15 reconnects the cumulative runtime in a complete agent loop. s16 extends that loop with workflow orchestration. s17 uses a smaller tool pool to focus on goal-controlled continuation; it is a mechanism example, not another cumulative runtime.
17 Progressive Lessons
Each lesson adds one harness mechanism. Each mechanism has a motto.
s01"One loop & Bash is all you need" — one tool + one loop = one agent
s02"Adding a tool means adding one handler" — the loop stays untouched; new tools register into the dispatch map
s03"Set boundaries first, then grant freedom" — check what can run, what must stop, and what needs approval
s04"Hook around the loop, never rewrite the loop" — add extension points without changing the main loop
s05"An agent without a plan drifts" — list the steps before starting; completion rate doubles
s06 Give a subtask fresh messages[]; its final text returns as one tool result
s07"Load knowledge on demand, not upfront" — list skills first, expand them only when needed
s08"Context always fills up -- have a way to make room" — four compaction steps reduce tool results first, then summarize history when it remains over the limit
s09"Remember what matters, forget what doesn't" — three subsystems: selection, extraction, consolidation
s10"Big goals break into small tasks, ordered, persisted to disk" — a file-backed task graph that lays the groundwork for multi-agent coordination
s11"Slow ops go background, agent keeps thinking" — background threads run commands; notifications inject on completion
s12"Fire on schedule, no human kick needed" — trigger tasks automatically by time
s13"Too big for one agent -- let teammates divide the work" — persistent teammates coordinate, claim ready tasks, and use task-bound working directories
s14"Not enough capability? Plug in more via MCP" — connect external tools into the same tool pool
s15"Many mechanisms, one loop" — the mechanisms used by the integrated example share one harness
s16"When the orchestration shape is fixed, put it in code" — saved workflows with resumable journals
s17"A goal decides when the loop may stop" — an independent evaluator reviews each proposed stop; impossible, failed, or over-limit goals return control to the user
Learning Path
Main line: act → handle complex work → remember across sessions → run long tasks → collaborate → extend and assemble → orchestrate and close goals.
flowchart TD
%% Card styles
classDef stage1 fill:#E3F2FD,stroke:#1976D2,stroke-width:2px,color:#0D47A1,rx:12,ry:12,text-align:left
classDef stage2 fill:#E8F5E9,stroke:#388E3C,stroke-width:2px,color:#1B5E20,rx:12,ry:12,text-align:left
classDef stage3 fill:#FFF3E0,stroke:#F57C00,stroke-width:2px,color:#E65100,rx:12,ry:12,text-align:left
classDef stage4 fill:#FCE4EC,stroke:#C2185b,stroke-width:2px,color:#880E4F,rx:12,ry:12,text-align:left
classDef stage5 fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px,color:#4A148C,rx:12,ry:12,text-align:left
classDef stage6 fill:#E0F7FA,stroke:#0097A7,stroke-width:2px,color:#006064,rx:12,ry:12,text-align:left
%% Group style
classDef groupBox fill:#F8F9FA,stroke:#CED4DA,stroke-width:2px,stroke-dasharray: 5 5,rx:15,ry:15,color:#495057
%% Layer 1: stages 1-3
subgraph Phase1 ["🌱 Stages 1-3: Core capabilities (simple to complex)"]
direction LR
S1["<b>1. Let the Agent act</b><br/>━━━━━━━━━━━━━<br/><b>s01 Agent Loop</b><br/>└─ one loop + bash<br/><br/><b>s02 Tool Use</b><br/>└─ one tool to many tools<br/><br/><b>s03 Permission</b><br/>└─ decide what can run<br/><br/><b>s04 Hooks</b><br/>└─ extension points around tools"]:::stage1
S2["<b>2. Handle complex work</b><br/>━━━━━━━━━━━━━<br/><b>s05 TodoWrite</b><br/>└─ plan first, then execute<br/><br/><b>s06 Subagent</b><br/>└─ fresh messages, final text back<br/><br/><b>s08 Context Compact</b><br/>└─ make room in long context"]:::stage2
S3["<b>3. Remember across sessions</b><br/>━━━━━━━━━━━━━<br/><b>s09 Memory</b><br/>└─ persist and recall reusable knowledge"]:::stage3
S1 ==> S2 ==> S3
end
%% Layer 2: stages 4-6
subgraph Phase2 ["🚀 Stages 4-6: Advanced capabilities (long-running, collaboration, integration)"]
direction LR
S4["<b>4. Run long tasks</b><br/>━━━━━━━━━━━━━<br/><b>s10 Task System</b><br/>└─ persist tasks and deps<br/><br/><b>s11 Background Tasks</b><br/>└─ send slow work background<br/><br/><b>s12 Cron Scheduler</b><br/>└─ trigger by time"]:::stage4
S5["<b>5. Coordinate many Agents</b><br/>━━━━━━━━━━━━━<br/><b>s13 Agent Teams</b><br/>└─ teammates + delivery + protocols<br/>└─ atomic task claims<br/>└─ task-bound worktrees"]:::stage5
S6["<b>6. Extend and assemble</b><br/>━━━━━━━━━━━━━<br/><b>s07 Skill Loading</b><br/>└─ expand skills on demand<br/><br/><b>s14 MCP Plugin</b><br/>└─ external tools, one pool<br/><br/><b>s15 Integrated Harness</b><br/>└─ course mechanisms, one loop"]:::stage6
S4 ==> S5 ==> S6
end
%% Layer 3: orchestration and goal closure
subgraph Phase3 ["🎯 Stage 7: Orchestration and goal closure"]
direction LR
S7["<b>7. Orchestrate and finish</b><br/>━━━━━━━━━━━━━<br/><b>s16 Workflow Runtime</b><br/>└─ scripts own fixed orchestration<br/><br/><b>s17 Goal Loop</b><br/>└─ independent evaluation decides when to stop"]:::stage1
S6 ==> S7
end
%% Connect the three layers
Phase1 ===> Phase2 ===> Phase3
class Phase1,Phase2,Phase3 groupBox
Each chapter is a folder. Open one and you will find:
s08_context_compact/
README.md # English, the default chapter README
README.zh.md # Chinese translation
README.ja.md # Japanese translation
code.py # standalone runnable implementation
images/ # SVG diagrams (where needed)
Read the README.md for the core idea and work through the code. Complex chapters have folds for deep dives -- open them when you want to go deeper. Simple chapters have 0-1 diagrams, complex chapters have more.
Read from s01 through s17 in order. Some mechanisms build directly on the previous runtime; independent mechanism chapters state which earlier kernel they use.
Quick Start
Current 17-Lesson Track
git clone https://github.com/shareAI-lab/learn-claude-code
cd learn-claude-code
pip install -r requirements.txt
cp .env.example .env # configure ANTHROPIC_API_KEY
python s01_agent_loop/code.py # Start here -- one loop + bash
python s08_context_compact/code.py # Context compaction (complex)
python s17_goal_loop/code.py # Endpoint: continue until a checkable goal is met
The web app extracts the root-level course. Lessons s16 and s17 include reading, source, simulator, and architecture views; only their dedicated hero visualizations remain intentionally minimal.
cd web && npm install && npm run dev # http://localhost:3000
Project Structure
learn-claude-code/
s01_agent_loop/ # one folder per chapter
README.md # English default (complete narrative)
README.zh.md # Chinese translation
README.ja.md # Japanese translation
code.py # standalone runnable code
images/ # SVG diagrams
s02_tool_use/
...
s14_mcp_plugin/
s15_integrated_harness/
s16_workflow_runtime/
s17_goal_loop/ # endpoint chapter
agents/ # legacy 12 runnable copies + s_full.py
skills/ # skill files used by s07
docs/ # legacy 12-lesson docs, kept during transition
web/ # generated from the root-level course
tests/
What's Next
After 17 lessons, you understand harness engineering from the inside out. Two paths to turn that knowledge into product:
Kode Agent CLI -- Open-Source Coding Agent CLI
npm i -g @shareai-lab/kode
Skill and LSP support, Windows compatible, works with GLM / MiniMax / DeepSeek and other open models. Install and go.
Sister Tutorial: From Passive Sessions to Always-On Assistants
The harness taught in this repository is the use-and-discard kind -- open a terminal, give the agent a task, close when done, next session starts fresh. Claude Code works this way.
But OpenClaw proves another possibility: on the same agent core, two additional harness mechanisms turn an agent from "poke it and it moves" into "wakes itself every 30 seconds to look for work":
Heartbeat -- every 30 seconds the harness sends the agent a message, letting it check for pending work. Nothing to do? Keep sleeping. Something appeared? Act immediately.
Cron -- the agent can schedule its own future tasks, which fire automatically when the time arrives.
Add IM multi-channel routing (WhatsApp / Telegram / Slack / Discord and 13+ other platforms), persistent context memory, and a Soul personality system, and the agent transforms from a disposable tool into an always-on personal AI assistant.
claw0 is our sister teaching repository, breaking down these harness mechanisms from scratch:
