chore: import upstream snapshot with attribution

docs/en/s01-the-agent-loop.md

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+# s01: The Agent Loop
+
+`[ s01 ] > s02 > s03 > s04 > s05 > s06 | s07 > s08 > s09 > s10 > s11 > s12`
+
+> *"One loop & Bash is all you need"* -- one tool + one loop = an agent.
+>
+> **Harness layer**: The loop -- the model's first connection to the real world.
+
+## Problem
+
+A language model can reason about code, but it can't *touch* the real world -- can't read files, run tests, or check errors. Without a loop, every tool call requires you to manually copy-paste results back. You become the loop.
+
+## Solution
+
+```
++--------+      +-------+      +---------+
+|  User  | ---> |  LLM  | ---> |  Tool   |
+| prompt |      |       |      | execute |
++--------+      +---+---+      +----+----+
+                    ^                |
+                    |   tool_result  |
+                    +----------------+
+                    (loop until stop_reason != "tool_use")
+```
+
+One exit condition controls the entire flow. The loop runs until the model stops calling tools.
+
+## How It Works
+
+1. User prompt becomes the first message.
+
+```python
+messages.append({"role": "user", "content": query})
+```
+
+2. Send messages + tool definitions to the LLM.
+
+```python
+response = client.messages.create(
+    model=MODEL, system=SYSTEM, messages=messages,
+    tools=TOOLS, max_tokens=8000,
+)
+```
+
+3. Append the assistant response. Check `stop_reason` -- if the model didn't call a tool, we're done.
+
+```python
+messages.append({"role": "assistant", "content": response.content})
+if response.stop_reason != "tool_use":
+    return
+```
+
+4. Execute each tool call, collect results, append as a user message. Loop back to step 2.
+
+```python
+results = []
+for block in response.content:
+    if block.type == "tool_use":
+        output = run_bash(block.input["command"])
+        results.append({
+            "type": "tool_result",
+            "tool_use_id": block.id,
+            "content": output,
+        })
+messages.append({"role": "user", "content": results})
+```
+
+Assembled into one function:
+
+```python
+def agent_loop(query):
+    messages = [{"role": "user", "content": query}]
+    while True:
+        response = client.messages.create(
+            model=MODEL, system=SYSTEM, messages=messages,
+            tools=TOOLS, max_tokens=8000,
+        )
+        messages.append({"role": "assistant", "content": response.content})
+
+        if response.stop_reason != "tool_use":
+            return
+
+        results = []
+        for block in response.content:
+            if block.type == "tool_use":
+                output = run_bash(block.input["command"])
+                results.append({
+                    "type": "tool_result",
+                    "tool_use_id": block.id,
+                    "content": output,
+                })
+        messages.append({"role": "user", "content": results})
+```
+
+That's the entire agent in under 30 lines. Everything else in this course layers on top -- without changing the loop.
+
+## What Changed
+
+| Component     | Before     | After                          |
+|---------------|------------|--------------------------------|
+| Agent loop    | (none)     | `while True` + stop_reason     |
+| Tools         | (none)     | `bash` (one tool)              |
+| Messages      | (none)     | Accumulating list              |
+| Control flow  | (none)     | `stop_reason != "tool_use"`    |
+
+## Try It
+
+```sh
+cd learn-claude-code
+python agents/s01_agent_loop.py
+```
+
+1. `Create a file called hello.py that prints "Hello, World!"`
+2. `List all Python files in this directory`
+3. `What is the current git branch?`
+4. `Create a directory called test_output and write 3 files in it`