shareAI-lab/learn-claude-code

Kode Agent SDK -- Embed Agent Capabilities in Your Application

A standalone library with no per-user process overhead. Embed it in backends, browser extensions, embedded devices, or any runtime.

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.

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.

• 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.
• 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 pu
• 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 in
• 2019 -- Tencent Jueyu dominates Honor of Kings. Tencent AI Lab's "Jueyu" system defeated KPL professional players in full 5v5 at the World Champion Cup semifinal. In 1v1 mode, pros won just 1 out of 1
• 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 c

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:

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.

• 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
• 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 mod

Agency Comes from the Model. An Agent Product = Model + Harness.

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.