#s06: Subagent — Break Large Tasks into Small Ones with Clean Context

s01 → s02 → s03 → s04 → s05 → s06 → s07 → s08 → ... → s20

"Break large tasks small, each with clean context" — Subagent uses an independent messages[], no pollution in the main conversation.

Harness Layer: Sub-Agent — Context isolation, attention doesn't drift.

#The Problem

The Agent is fixing a bug. It reads 30 files to trace the call chain, chatting for 60 rounds along the way. The messages list grows to 120 entries, most of which are intermediate steps from "tracing the call chain" — unrelated to the final goal of "fixing the bug."

These intermediate steps occupy context space, making the Agent increasingly "forgetful" — it can no longer remember what the original problem was.

Think of it differently: when you fix a bug, you'd "open a new terminal" to trace the call chain. When done, close the terminal, write the result into your notes, and return to the original terminal to keep fixing. The Agent needs this ability too — open an independent sub-process, give it an independent message list, let it focus on one thing.

#The Solution

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The minimal hook structure and todo_write tool from the previous chapter are preserved; this chapter focuses on the new task tool. When called, it spawns a sub-Agent with a fresh messages[], running its own loop, and returning only a summary text to the main Agent. Conversation context is discarded, but file system side effects (writes, edits, commands) remain in the working directory.

The sub-Agent's tools are restricted: it has bash/read/write/edit/glob, but no task, preventing recursive spawning. The sub-Agent's tool calls still go through permission hooks; context isolation does not bypass security.

#How It Works

spawn_subagent, gives the sub-Agent a fresh messages list, runs its own loop, returns only the conclusion:

def spawn_subagent(description: str) -> str:
    # Sub-Agent tools: base tools, but no task (no recursion)
    sub_tools = [...]
    messages = [{"role": "user", "content": description}]  # fresh messages[]

    for _ in range(30):  # safety limit
        response = client.messages.create(
            model=MODEL, system=SUB_SYSTEM,
            messages=messages, tools=sub_tools, max_tokens=8000,
        )
        messages.append({"role": "assistant", "content": response.content})
        if response.stop_reason != "tool_use":
            break
        results = []
        for block in response.content:
            if block.type == "tool_use":
                blocked = trigger_hooks("PreToolUse", block)
                if blocked:
                    results.append({... "content": str(blocked)})
                    continue
                handler = SUB_HANDLERS.get(block.name)
                output = handler(**block.input) if handler else f"Unknown"
                trigger_hooks("PostToolUse", block, output)
                results.append({... "content": output})
        messages.append({"role": "user", "content": results})

    # Return only the final text conclusion, all intermediate steps discarded
    return extract_text(messages[-1]["content"])

The main Agent calls it just like any other tool:

TOOLS = [
    {"name": "bash", ...},
    {"name": "read_file", ...},
    {"name": "write_file", ...},
    {"name": "edit_file", ...},
    {"name": "glob", ...},
    {"name": "todo_write", ...},
    # s06: new task tool
    {"name": "task",
     "description": "Launch a subagent to handle a complex subtask. Returns only the final conclusion.",
     "input_schema": {"type": "object", "properties": {"description": {"type": "string"}}, "required": ["description"]}},
]

TOOL_HANDLERS["task"] = spawn_subagent

Three key design decisions:

The dispatch mechanism is unchanged; the task tool is routed through TOOL_HANDLERS[block.name]. The sub-Agent has its own SUB_SYSTEM prompt, explicitly instructing "complete the task, do not delegate further."

#Changes from s05

#Try It

cd learn-claude-code
python s06_subagent/code.py

Try these prompts:

1. Use a subtask to find what testing framework this project uses (sub-Agent reads files, main Agent receives only the conclusion)
2. Delegate: read all .py files in agents/ and summarize what each one does
3. Use a task to create s06_subagent/example/string_tools.py with a slugify(text: str) function, then verify it from the parent agent

What to watch for: Do [Subagent spawned] / [Subagent done] appear? Do sub-Agent tool calls print as [sub] ...? Does the parent Agent continue with only the summary returned by the sub-Agent?

#What's Next

The Agent can now break tasks apart. But different tasks require different knowledge: editing frontend components needs React conventions, writing SQL needs table schemas. Stuffing all this knowledge into the system prompt would blow up the context.

→ s07 Skill Loading: Inject skills on demand instead of piling documents into the system prompt. Load only when needed, as natural as reading a file.