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

s01: The Agent Loop

The entire secret of AI coding agents is a while loop that feeds tool results back to the model until the model decides to stop.

The Problem

Why can't a language model just answer a coding question? Because coding requires interaction with the real world. The model needs to read files, run tests, check errors, and iterate. A single prompt-response pair cannot do this.

Without the agent loop, you would have to copy-paste outputs back into the model yourself. The user becomes the loop. The agent loop automates this: call the model, execute whatever tools it asks for, feed the results back, repeat until the model says "I'm done."

Consider a simple task: "Create a Python file that prints hello." The model needs to (1) decide to write a file, (2) write it, (3) verify it works. That is three tool calls minimum. Without a loop, each one requires manual human intervention.

The Solution

+----------+      +-------+      +---------+
|   User   | ---> |  LLM  | ---> |  Tool   |
|  prompt  |      |       |      | execute |
+----------+      +---+---+      +----+----+
                      ^               |
                      |   tool_result |
                      +---------------+
                      (loop continues)

The loop terminates when stop_reason != "tool_use".
That single condition is the entire control flow.

How It Works

1. The user provides a prompt. It becomes the first message.

history.append({"role": "user", "content": query})

2. The messages array is sent to the LLM along with the tool definitions.

response = client.messages.create(
    model=MODEL, system=SYSTEM, messages=messages,
    tools=TOOLS, max_tokens=8000,
)

3. The assistant response is appended to messages.

messages.append({"role": "assistant", "content": response.content})

4. We check the stop reason. If the model did not call a tool, the loop ends. This is the only exit condition.

if response.stop_reason != "tool_use":
    return

5. For each tool_use block in the response, execute the tool (bash in this session) and collect 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,
        })

6. The results are appended as a user message, and the loop continues.

messages.append({"role": "user", "content": results})

Key Code

The minimum viable agent -- the entire pattern in under 30 lines (from agents/s01_agent_loop.py, lines 66-86):

def agent_loop(messages: list):
    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})

What Changed

This is session 1 -- the starting point. There is no prior session.

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"

Design Rationale

This loop is the universal foundation of all LLM-based agents. Production implementations add error handling, token counting, streaming, and retry logic, but the fundamental structure is unchanged. The simplicity is the point: one exit condition (stop_reason != "tool_use") controls the entire flow. Everything else in this course -- tools, planning, compression, teams -- layers on top of this loop without modifying it. Understanding this loop means understanding every agent.

Try It

cd learn-claude-code
python agents/s01_agent_loop.py

Example prompts to try:

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