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根据 stop_reason 终止

让 end_turn 结束循环，而不是依赖字符串匹配。

根据 stop_reason 终止 是 CoddyKit 上的免费 Claude Architect 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Claude Architect 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Claude Architect 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

The Loop Needs an Exit

An agentic loop is simple: you send a request, Claude responds, and you decide whether to keep going. The hard question is when to stop.

Every API response carries a stop_reason field. This is the model's own signal about why it stopped generating. Your loop should listen to that signal — not guess by reading the words in the reply.

This lesson teaches one rule that separates robust agents from fragile ones: let end_turn end the loop, not your string matching.

The Four Stop Reasons

Claude returns one of four stop_reason values on every turn:

  • end_turn — the model finished its response naturally. The task turn is complete.
  • tool_use — the model wants to call a tool. Run it, append the result, and continue.
  • max_tokens — output was truncated by your max_tokens limit.
  • stop_sequence — a custom stop sequence you configured was hit.

These four values are a complete, reliable contract. Your control flow should branch on them directly.

resp = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    tools=tools,
    messages=messages,
)
print(resp.stop_reason)  # end_turn | tool_use | max_tokens | stop_sequence

The Anti-Pattern: Parsing Text

A tempting shortcut is to read the model's text and look for a keyword like "done" or "finished" to decide the loop is over.

This is a classic anti-pattern. Text is probabilistic. The model might say "I'm done thinking, now let me call a tool" — and your matcher stops too early. Or it phrases completion differently and your loop runs forever.

Never parse text for completion signals. The structured stop_reason exists precisely so you don't have to.

# ANTI-PATTERN: do NOT do this
text = resp.content[0].text
if "done" in text.lower():
    break  # fragile, unreliable, exam-wrong

The Canonical Loop

Here is the correct shape of the loop. You inspect stop_reason each turn. When it is tool_use, you run the tools and append their results to the conversation. You repeat until stop_reason is end_turn.

Notice the loop is driven entirely by the structured signal — no text inspection decides termination.

while True:
    resp = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=1024,
        tools=tools,
        messages=messages,
    )
    messages.append({"role": "assistant", "content": resp.content})

    if resp.stop_reason == "end_turn":
        break

    if resp.stop_reason == "tool_use":
        results = run_tools(resp.content)
        messages.append({"role": "user", "content": results})
        continue

Full History, Every Turn

One reason the loop works: the model keeps no state between requests. Each API call must include the FULL messages history.

That is why, after a tool_use turn, you append the assistant's tool-call content AND the tool results back into messages before the next request. The model re-reads the entire conversation and decides whether more tools are needed or it can finish with end_turn.

Drop history, and the model loses the thread — it can't reach a coherent end_turn.

# Each request resends EVERYTHING
messages = [
    {"role": "user",      "content": "Refund order 4471."},
    {"role": "assistant", "content": [tool_use_block]},   # prior turn
    {"role": "user",      "content": [tool_result_block]}, # prior turn
]
resp = client.messages.create(model=MODEL, max_tokens=1024,
                              tools=tools, messages=messages)

tool_use Is Not a Stop

A common mistake is treating tool_use as a terminal state. It is not. It means "pause, run this tool, then come back to me."

When you see tool_use, you:

  • Execute the requested tool(s) in your own code.
  • Append the tool_result blocks to messages.
  • Send the request again so the model can continue.

Only end_turn means the work for this turn is genuinely finished.

if resp.stop_reason == "tool_use":
    tool_results = []
    for block in resp.content:
        if block.type == "tool_use":
            output = dispatch(block.name, block.input)
            tool_results.append({
                "type": "tool_result",
                "tool_use_id": block.id,
                "content": output,
            })
    messages.append({"role": "user", "content": tool_results})
    # loop continues -> next request

Decisions Are Model-Driven

The deeper principle: let the model decide when it is done. The model has the full context — the user's goal, the tool results, the conversation so far. It is better positioned than a hard-coded rule to judge whether the task is complete.

Your job as the architect is to provide good tools and clear instructions, then trust the end_turn signal. Reserve hard-coded control flow for guarantees you cannot leave to probability.

The Iteration Cap Is a Safety Net

You should still add a maximum-iteration guard — but understand its role. An iteration cap is a safety net to prevent runaway loops or cost blowouts. It is NOT the primary stop mechanism.

The primary stop is always end_turn. The cap only fires in the rare pathological case where the model never converges. If your loop relies on the cap to end normally, your design is broken.

MAX_ITERS = 20  # safety net, NOT the normal exit
for i in range(MAX_ITERS):
    resp = client.messages.create(model=MODEL, max_tokens=1024,
                                  tools=tools, messages=messages)
    messages.append({"role": "assistant", "content": resp.content})
    if resp.stop_reason == "end_turn":
        break  # normal exit
    # ... handle tool_use ...
else:
    log.warning("Hit iteration cap without end_turn")

Handling max_tokens

max_tokens is a distinct case that needs its own handling. It means the response was truncated mid-generation — the model did not finish its thought.

Treating this as a clean completion would silently cut off the agent's work. Depending on your design you might raise max_tokens, ask the model to continue, or flag the turn. What you must NOT do is fall through and assume the task is done.

if resp.stop_reason == "max_tokens":
    # output was cut off - NOT a completion
    log.warning("Response truncated; consider raising max_tokens or continuing")
    # handle explicitly; do not treat as end_turn

Where Hard Code Belongs

If model-driven decisions are the default, when do you reach for deterministic code?

For guarantees — outcomes that must hold every single time regardless of the model's judgment. Examples: a precondition that blocks a refund until get_customer returns a verified ID, or a hook that rejects any refund over a policy threshold.

These are 100% deterministic enforcement points. Termination, by contrast, is a model-driven decision you read from stop_reason. Don't confuse the two: hard-code guarantees, trust end_turn for flow.

Putting It Together

A production-grade agentic loop combines all the pieces:

  • Branch on stop_reason — never on text.
  • Resend full history each turn (the model is stateless).
  • tool_use → run, append, continue. end_turn → stop.
  • Handle max_tokens explicitly — truncation is not completion.
  • Keep an iteration cap as a safety net only.

This is the backbone of every reliable agent you'll architect.

for _ in range(MAX_ITERS):
    resp = client.messages.create(model=MODEL, max_tokens=2048,
                                  tools=tools, messages=messages)
    messages.append({"role": "assistant", "content": resp.content})

    if resp.stop_reason == "end_turn":
        break
    if resp.stop_reason == "max_tokens":
        handle_truncation(resp); break
    if resp.stop_reason == "tool_use":
        messages.append({"role": "user",
                         "content": run_tools(resp.content)})
        continue

Quick Check: Ending the Loop

A support agent built on the Agent SDK sometimes loops forever and sometimes stops before calling a needed tool. The loop currently breaks when the assistant's text contains the word "resolved". What is the correct fix?

Recap: Trust the Signal

Key takeaways:

  • Terminate on stop_reason, never by parsing text for words like "done" or "resolved".
  • end_turn ends the loop; tool_use means run tools, append results, and continue.
  • Handle max_tokens explicitly — truncation is not completion.
  • The model is stateless: resend the full message history every turn.
  • Termination is a model-driven decision; the iteration cap is only a safety net.
  • Reserve hard-coded enforcement for guarantees (preconditions, hooks), not for ending the loop.

Let end_turn end the loop. That single discipline makes your agents predictable and production-ready.

常见问题解答

「根据 stop_reason 终止」课时是免费的吗？

是的 — 「根据 stop_reason 终止」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Claude Architect 课程的其余内容，请升级到 CoddyKit PRO。 Claude Architect 课程共包含 4 节课。

「根据 stop_reason 终止」这节课中我会学到什么？

让 end_turn 结束循环，而不是依赖字符串匹配。 你通过在浏览器中直接运行的动手代码来练习 Claude Architect，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Claude Architect 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Claude Architect 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。

「根据 stop_reason 终止」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Claude Architect 课中编写并运行代码吗？

能。每节 Claude Architect 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. 核心循环
  2. 根据 stop_reason 终止
  3. 反模式：解析文本判断完成
  4. 反模式：任意设置迭代上限
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