analysis_claude_code/docs/ja/s08-background-tasks.md
CrazyBoyM a9c71002d2 the model is the agent, the code is the harness
Comprehensive rewrite establishing the harness engineering narrative
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README (EN/ZH/JA): added "The Model IS the Agent" manifesto with
historical proof (DQN, OpenAI Five, AlphaStar, Tencent Jueyu),
"What an Agent Is NOT" critique, harness engineer role definition,
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docs (36 files, 3 languages): injected one-line "Harness layer"
callout after the motto in every session document (s01-s12).

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# s08: Background Tasks
`s01 > s02 > s03 > s04 > s05 > s06 | s07 > [ s08 ] s09 > s10 > s11 > s12`
> *"遅い操作はバックグラウンドへ、エージェントは次を考え続ける"* -- デーモンスレッドがコマンド実行、完了後に通知を注入。
>
> **Harness 層**: バックグラウンド実行 -- モデルが考え続ける間、Harness が待つ。
## 問題
一部のコマンドは数分かかる: `npm install``pytest``docker build`。ブロッキングループでは、モデルはサブプロセスの完了を待って座っている。ユーザーが「依存関係をインストールして、その間にconfigファイルを作って」と言っても、エージェントは並列ではなく逐次的に処理する。
## 解決策
```
Main thread Background thread
+-----------------+ +-----------------+
| agent loop | | subprocess runs |
| ... | | ... |
| [LLM call] <---+------- | enqueue(result) |
| ^drain queue | +-----------------+
+-----------------+
Timeline:
Agent --[spawn A]--[spawn B]--[other work]----
| |
v v
[A runs] [B runs] (parallel)
| |
+-- results injected before next LLM call --+
```
## 仕組み
1. BackgroundManagerがスレッドセーフな通知キューでタスクを追跡する。
```python
class BackgroundManager:
def __init__(self):
self.tasks = {}
self._notification_queue = []
self._lock = threading.Lock()
```
2. `run()`がデーモンスレッドを開始し、即座にリターンする。
```python
def run(self, command: str) -> str:
task_id = str(uuid.uuid4())[:8]
self.tasks[task_id] = {"status": "running", "command": command}
thread = threading.Thread(
target=self._execute, args=(task_id, command), daemon=True)
thread.start()
return f"Background task {task_id} started"
```
3. サブプロセス完了時に、結果を通知キューへ。
```python
def _execute(self, task_id, command):
try:
r = subprocess.run(command, shell=True, cwd=WORKDIR,
capture_output=True, text=True, timeout=300)
output = (r.stdout + r.stderr).strip()[:50000]
except subprocess.TimeoutExpired:
output = "Error: Timeout (300s)"
with self._lock:
self._notification_queue.append({
"task_id": task_id, "result": output[:500]})
```
4. エージェントループが各LLM呼び出しの前に通知をドレインする。
```python
def agent_loop(messages: list):
while True:
notifs = BG.drain_notifications()
if notifs:
notif_text = "\n".join(
f"[bg:{n['task_id']}] {n['result']}" for n in notifs)
messages.append({"role": "user",
"content": f"<background-results>\n{notif_text}\n"
f"</background-results>"})
messages.append({"role": "assistant",
"content": "Noted background results."})
response = client.messages.create(...)
```
ループはシングルスレッドのまま。サブプロセスI/Oだけが並列化される。
## s07からの変更点
| Component | Before (s07) | After (s08) |
|----------------|------------------|----------------------------|
| Tools | 8 | 6 (base + background_run + check)|
| Execution | Blocking only | Blocking + background threads|
| Notification | None | Queue drained per loop |
| Concurrency | None | Daemon threads |
## 試してみる
```sh
cd learn-claude-code
python agents/s08_background_tasks.py
```
1. `Run "sleep 5 && echo done" in the background, then create a file while it runs`
2. `Start 3 background tasks: "sleep 2", "sleep 4", "sleep 6". Check their status.`
3. `Run pytest in the background and keep working on other things`