Build an orchestration mode#
Build a session-level mode that grants standing consent for multiagent fan-out, switched on and off with mid-conversation system messages.
会话级别的;持续性凭证;multiagent;mid-conversation
编排模式(orchestration mode)是一种会话级开关:当他开启时,model will pay most efforts to every request,先自行见检查和分析任务,然后默认将工作拆分给几个并发的子智能体
It is not an API parameter. It is built entirely from documented pieces
- 1.An effort level: requests run at a documented effort value such as Max,there no other level higher than it
- A mode reminder: a mid-conversation system message tells the model the mode is active, 每个几次对话发送一行通知
当推出时,会发出退出通知
- Standing consent(持续授权) in the tool description:该模式开启时,对于每一个实质性的任务,都应该在不经过询问的情况下自己设计和运行一套工作流程
该模式会造成成本的增加,原因有二:Max effort level;Multi-agents,subagents
Ser up the loop#
MAX_CONCURRENT限制并发的subagents的数量
MAX_TOTAL_SUBTASKS在一次工作流中的排队的任务的数量
当DOC_TEST_MODE开启时,会将循环次数限制为一次,这样地自动化文本测试框架就可以快速完成编译并快速结束,而不需要运行完整地编排流程
但是在运行个人的用例时,请不要开启或者leave it unset,不然你就会进入文档测试模式
Define the mode reminder#
这些reminders都很简短,他们切换模式并且指向tool description
完整的文本只在刚开启时发送一次;the refresher每隔几轮对话发一次;退出通知只在退出的时候发送
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| MODE_ENTER = (
"Orchestration mode is on: optimize for the most exhaustive, correct answer rather than "
"the fastest one. Use the Workflow tool on every substantive task, sized to the problem's "
"natural decomposition rather than the maximum the tool allows. See the Workflow tool's "
"description for standing consent, granularity guidance, and quality patterns. Work solo "
"only on conversational or trivial turns."
)
MODE_REFRESH = (
"Orchestration mode is still on. Use the Workflow tool; see its standing consent section."
)
MODE_EXIT = (
"Orchestration mode is off. The Workflow tool's standard opt-in rule applies again."
)
|
Workflow工具承载了真正的行为约定(behavioral contract):包括准入规则,持续授权,模型可以采用的质量保障模式等等
subagent还会获得一个report_findings工具,将他们的结果以JSON结构化的形式返回;而bash工具则是anthropic定义的bash_20250124工具,在本地运行
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| WORKFLOW_TOOL = {
"name": "Workflow",
"description": (
"Orchestrate a multiagent workflow: split a large task into independent subtasks "
"and run them as parallel agents, then collect their results.\n\n"
"Opt-in: only use this tool when the user explicitly asks for a workflow, or when a "
"system message confirms that orchestration mode is on.\n\n"
"Quality patterns: adversarial verification (a second wave of agents checks the first "
"wave's findings against the source), a completeness critic (one agent hunts for what "
"the others missed), and multiphase sequencing (understand, design, implement, and "
"review as separate workflow calls, reading results between phases). A useful default "
"is hybrid: scout inline first to discover the work-list, then fan out over it.\n\n"
"Granularity: scope each subtask to a distinct concern, component, or question rather "
"than per line or per file section. Scale the count to what the user asked for: a "
"focused review of a module of a few hundred lines rarely needs more than about ten "
"subtasks; a broad audit of a large codebase can justify more.\n\n"
"Standing consent: while a system message confirms orchestration mode is on, that "
"opt-in is standing. Author and run a workflow for every substantive task by default, "
"and lean toward verifying findings adversarially. Work solo only on conversational "
"turns or trivial mechanical edits. When a system message says the mode is off, "
"revert to the opt-in rule above."
),
"input_schema": {
"type": "object",
"properties": {
"subtasks": {
"type": "array",
"items": {"type": "string"},
"description": "Independent subtask prompts to run as parallel agents",
}
},
"required": ["subtasks"],
},
}
BASH_TOOL = {"type": "bash_20250124", "name": "bash"}
REPORT_TOOL = {
"name": "report_findings",
"description": (
"Report the final findings for your subtask. Call this exactly once, when you are "
"done investigating; it ends your task."
),
"input_schema": {
"type": "object",
"properties": {
"summary": {"type": "string", "description": "Two or three sentences of synthesis"},
"findings": {
"type": "array",
"items": {
"type": "object",
"properties": {
"claim": {"type": "string", "description": "The finding, one sentence"},
"evidence": {
"type": "string",
"description": "How it was verified (file, line, or command output)",
},
"severity": {"type": "string", "enum": ["high", "medium", "low", "info"]},
},
"required": ["claim", "evidence", "severity"],
},
},
},
"required": ["summary", "findings"],
},
}
|
bash处理器运行命令并带运行超时时间,捕获合并后的stderr和stdout并且截断输出,以防止单次输出占满上下文窗口
命令会在启动的文件夹内运行
当DOC_TEST_MODE开启时,框架会给bash命令提供一个临时的,小型的测试目录,并且在退出时删除
没有沙箱,
示例每次调用都会在一个全新的子shell中运行,而不是维持持久化对话
而在生产环境中的agent则应该接入到一个长期运行的subshell
Run one subagent#
每个workflow的子任务都会称为一个独立的小型的带有bash工具的agent循环,并且effort与主循环相同
每个请求都会设置一个单独的超时时间,这样即使链接中断也只是子agent受影响而不会让整个工作流瘫痪停滞
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| def run_subagent(model: str, prompt: str) -> str:
"""One subagent: a small nested agent loop with the bash tool plus report_findings.
Subagents inherit the main loop's effort level."""
subagent_system = (
"You are one agent in a larger parallel fan-out, assigned a single subtask. "
"Investigate it directly, using bash to check facts rather than guessing, and finish "
"by calling report_findings exactly once. Return findings, not narration."
)
messages = [{"role": "user", "content": prompt}]
for _ in range(MAX_SUBAGENT_TURNS):
with client.messages.stream(
model=model,
max_tokens=64000,
system=subagent_system,
output_config={"effort": EFFORT},
tools=[BASH_TOOL, REPORT_TOOL],
messages=messages,
timeout=REQUEST_TIMEOUT_SECONDS,
) as stream:
response = stream.get_final_message()
messages.append({"role": "assistant", "content": response.content})
if response.stop_reason == "pause_turn":
continue
if response.stop_reason != "tool_use":
text = "".join(block.text for block in response.content if block.type == "text")
if response.stop_reason == "max_tokens":
text += "\n\n(warning: subagent response was truncated at max_tokens)"
return text
tool_results = []
report = None
for block in response.content:
if block.type != "tool_use":
continue
if block.name == "report_findings":
report = json.dumps(block.input, indent=2)
output, is_error = "Findings recorded.", False
elif block.name == "bash":
output, is_error = handle_bash_block(block)
else:
output, is_error = f"unknown tool: {block.name}", True
tool_results.append(
{
"type": "tool_result",
"tool_use_id": block.id,
"content": output,
"is_error": is_error,
}
)
if report is not None:
return report
messages.append({"role": "user", "content": tool_results})
return "(subagent hit the turn limit before finishing)"
|
Journal results so reruns resume(记录结果这样重新运行时可以接着执行)#
该模式会开启许多子agent,重启的成本很高.而一个基于内容寻址的日志会使他具有幂等性(idempotent)
在分发智能体之前,先计算器prompt的SHA-256,并且在本地的JSON文件中查找这个哈希值,如果存在,则直接返回之前的结果
如果运行过程被中断,也只有那些从未被完成的子任务才会被重启
该日志会在不同的运行之间去重,但是不会在同一次fan-out中去重
如果想要重头开始执行,只需要删除这个日志文件即可
Fan out,then verify#
扇出最多接受 MAX_TOTAL_SUBTASKS 个 prompt,并通过日志(journal)运行这些任务,同时最多允许 MAX_CONCURRENT 个任务处于执行中(PHP 版本则是顺序执行)。
它会隔离各个子任务的失败:某个子智能体出错时,只会将其降级为一个错误字符串,而不会导致整个运行流程终止。
第一轮执行完成后,会进行第二轮扇出,并复用相同的子智能体执行路径,对第一轮的每个结果进行尝试反驳:
每个验证器(verifier)都会根据原始来源重新推导和核实这些结论;如果无法确定,则默认判定为**“反驳(refuted)”**。
最终,原始结果和对应的验证结论(verdict)都会返回给编排器(orchestrator),由编排器综合考虑两者。
Toggle the mode with mid-conversation system messages#
通过 mid-conversation来切换模式
先追加user_message,然后才是system-message(可能full text,也可能是定时的refresher,也可能是退出通知)
并且这满足了它的位置限制:
某个user turn之后(role: user的tool call也可以视为user turn),对应的assistant之前.或者最后面:
原因之前有,主要是为了prompt caching