System One 是 TypeSafe 用于构建 AI 驱动软件(而非智能体)的模型。它不生成代码,也不自行选择下一步动作。它提供可嵌入软件的 AI 原语,让代码始终掌控全局,而由模型处理对非结构化数据的常识性判断。
摘要: 构建普通的软件工作流,只在需要 AI 的地方插入 System One。
- 将控制流、确定性规则和副作用保留在代码中。
- 把宽泛的判断拆解为狭窄的、带类型的问题,并附上明确的 instructions 和 criteria。
- 只给每个问题它所需的上下文。
- 利用概率和置信度来执行、请求审查或升级处理。
- 将独立的问题放在同一次请求中提出,然后在代码中组合它们的答案。
三种软件架构
TypeSafe 专为构建 AI 驱动的软件 而设计:代码掌管工作流,AI 负责狭窄、结构化的决策。
传统代码是由简单的软件原语构成的复杂决策树。由于每个原语都可靠,开发者可以将它们组合成更高层的抽象。
智能体处理指令并选择下一步。在有人监控流程时这运作良好,但每一次循环都多了一次偏离正轨的机会。
代码处理确定性工作并掌管控制流。模型只在系统需要可编程常识或需要解读非结构化数据的地方出现。每个 AI 任务都保持原子化并受到约束。


System One 的可组合性来自哪里
System One 在构造上就是类型安全的。决策和概率都符合你代码所期望的结构化软件类型和 JSON schema,因此它从不需要从生成的散文文本中恢复数值。
各问题独立且并行地求值。一个原语的结果不会变成改变另一个原语结果的隐藏上下文。
输出可排序,可用于驱动智能的 if 语句、阈值和比较。
大多数查询在大约 100 毫秒内完成。System One 的速度足以支撑实时请求路径和用户界面。
RLCD 通过校准的概率传达不确定性,而不是趋向于过度自信。
System One 被设计为在重复求值时返回稳定的答案。参见自洽性指南。
由于每个输出都被约束在所提供的选项之内,模型返回的是这些选项上的完整概率分布,而不是凭空编造一个 schema 之外的值。TypeSafe 的目标是让智能与速度和成本之比超过 100 倍;这背后的押注是:更便宜的智能将催生大得多的需求。
设计 System One 工作流
能用代码时就用代码
把确定性工作保留在代码中,它既可靠又便宜。当软件工作流能够表达相同行为时,避免使用智能体的
while循环。示例:将确定性规则保留在代码中
pythondays_overdue = (today - invoice.due_date).days if days_overdue > 30: route_to_collections(invoice)浏览 System One 模式,了解以受约束的方式将模型决策与代码组合的做法。
拆解输入状态
只包含与当前问题相关的上下文。这有助于模型避免分心和上下文退化。当最新信息可以来自你自己的知识库时,不要依赖模型权重中存储的知识。
示例:只发送相关上下文
requestjson{ "state": { "ticket_message": "My flight was cancelled. Can I get a refund?", "refund_policy": "Cancelled flights are eligible for a full refund." }, "questions": { "policy_supports_refund": { "type": "noul", "instructions": "Does the refund policy support the refund requested in the ticket?" } } }在输入状态中使用结构
在
state和questions字段中使用嵌套 JSON。当指向特定值可以消除歧义时,让问题指向这些值,并在问题内部为每个路径保留反引号字符。示例:引用嵌套值
使用带反引号的点与索引路径让问题指向某个特定的嵌套值,例如
support.tickets[0].message。requestjson{ "state": { "support": { "tickets": [ { "message": "I was charged twice for order A-104." }, { "message": "How do I reset my password?" } ] }, "commerce": { "orders": [ { "id": "A-104", "charges": [ { "amount_usd": 49, "status": "captured" }, { "amount_usd": 49, "status": "captured" } ] } ] }, "account": { "security": { "password_reset": "Email a reset link to the address on file." } } }, "questions": { "duplicate_charge": { "type": "noul", "instructions": "Do `support.tickets[0].message` and `commerce.orders[0].charges` indicate a duplicate charge?" }, "password_reset_supported": { "type": "noul", "instructions": "Can `account.security.password_reset` resolve the request in `support.tickets[1].message`?" } } }拆解问题
尽可能提出最明确、最狭窄、最具体、最原子化的问题。把复杂的或定义不清的问题拆成多个问题,让每个问题只评估一个属性。
这可能是本指南中最重要的概念。宽泛的问题会把多个判断藏在同一个答案背后。原子化的问题把这些判断暴露出来,便于你在代码中检查、调优和组合。
示例:拆解垃圾信息检测
一个宽泛的问题(差)json{ "is_spam": { "type": "noul", "instructions": "Is `message` spam?" } }拆解后的多个问题(好)json{ "requests_credentials": { "type": "noul", "instructions": "Does `message.body` ask the recipient to provide a password or other login credential?" }, "offers_unexpected_reward": { "type": "noul", "instructions": "Does `message.body` claim the recipient received an unexpected prize, payment, or reward?" }, "creates_time_pressure": { "type": "noul", "instructions": "Does `message.subject` or `message.body` pressure the recipient to act quickly?" }, "sender_identity_mismatch": { "type": "noul", "instructions": "Does the organization named in `message.sender.display_name` conflict with the domain in `message.sender.email`?" }, "link_domain_mismatch": { "type": "noul", "instructions": "Does the domain in `message.links[0].url` conflict with the organization named in `message.sender.display_name`?" }, "disguises_link_destination": { "type": "noul", "instructions": "Does `message.links[0].text` conceal or misrepresent the destination in `message.links[0].url`?" } }示例:验证工具调用轨迹
一个宽泛的问题(差)json{ "tool_calls_are_correct": { "type": "noul", "instructions": "Is `trace.tool_calls` correct for `request` and `available_tools`?" } }拆解后的多个问题(好)json{ "geocode_tool_is_relevant": { "type": "noul", "instructions": "Is `trace.tool_calls[0].name` an appropriate tool for resolving `request.location`?" }, "geocode_location_matches": { "type": "noul", "instructions": "Does `trace.tool_calls[0].arguments.city` match `request.location`?" }, "geocode_arguments_match_schema": { "type": "noul", "instructions": "Does `trace.tool_calls[0].arguments` conform to `available_tools.geocode_city.parameters`?" }, "geocode_result_matches_call": { "type": "noul", "instructions": "Does `trace.tool_results[0].tool_call_id` match `trace.tool_calls[0].id`?" }, "weather_tool_is_relevant": { "type": "noul", "instructions": "Is `trace.tool_calls[1].name` an appropriate tool for answering `request.text`?" }, "weather_arguments_match_schema": { "type": "noul", "instructions": "Does `trace.tool_calls[1].arguments` conform to `available_tools.get_weather.parameters`?" }, "weather_uses_geocoded_coordinates": { "type": "noul", "instructions": "Do the coordinates in `trace.tool_calls[1].arguments` match those in `trace.tool_results[0].output`?" }, "weather_date_matches": { "type": "noul", "instructions": "Does `trace.tool_calls[1].arguments.date` match `request.date`?" }, "weather_unit_matches": { "type": "noul", "instructions": "Does `trace.tool_calls[1].arguments.unit` match `request.unit`?" } }在问题中使用结构
保持问题简短。
instructions和criteria通常是字符串;对于简短、无歧义的问题,一个字符串就足够了。它们也可以是对象或数组。把问题本身放在一个字段中,把引导该问题的数据放在其他字段中。在以下情况下,结构会派上用场:
- 问题需要上下文或示例。较长的背景信息或示例输入列表应放进问题旁边的具名字段中,这样你的代码无需改写问题就能增删或替换它们。
- 问题的一部分来自你的代码。当某个值来自数据库时,把它放进独立的字段,而不是拼进字符串模板。
- 多个问题的指令相似。一次请求接收一个 state,并且可以包含多个问题。添加补充数据有助于让问题彼此区分。
示例:引用来自你代码的记录
这个 Noul 将 state 中的一份简历与候选人数据库中的一条记录进行比较。记录原样放进
potential_duplicate,问题则通过名称引用它。questionsjson{ "same_as_record_18": { "type": "noul", "instructions": { "potential_duplicate": { "name": "John Smith", "location": "Oakland, California", "last_employer": "Google" }, "question": "Is the resume for the same person as `potential_duplicate`?" } } }来自代码的 "potential_duplicate" 数据会随时间变化。"question" 使用反引号引用它。
criteria内的描述也可以是对象。对于 Choice,每个选项的描述可以是一个对象,说明该选项涵盖什么、哪些内容属于其他选项,以及若干示例。在各选项之间使用相同的字段名,以便模型直接比较。示例:定义对比式 Choice criteria
questionsjson{ "card_help_topic": { "type": "choice", "instructions": { "question": "Which disposable virtual card topic is the user asking about?", "focus": "Classify the information the user wants." }, "criteria": { "get_disposable_virtual_card": { "what": "Purpose, eligibility, or setup", "not_for": "Quantity, transaction, or merchant restrictions", "examples": [ "How can I get a disposable virtual card?", "What are disposable cards for?" ] }, "disposable_card_limits": { "what": "Quantity, transaction, or merchant restrictions", "not_for": "Purpose, eligibility, or setup", "examples": [ "How many disposable cards can I make per day?", "Where can I use a disposable card?" ] } } } }每种问题类型的页面都有一个完整的示例:
- Noul 将一份简历与多条候选人记录进行比较,每条记录一个问题,问题由代码构建。
- Choice 描述两个容易混淆的选项,说明各自涵盖什么、不适用于什么,并给出示例。
- Score 为每个级别提供描述和示例场景。
结构化数据提取级联指南展示了共用措辞的场景:对提取记录的每个字段提问同一组问题。
简短、无歧义的问题或标准可以仍然是一个字符串。当结构能把原本混在一起的引导信息区分开时,就加上结构。关于所有接受结构的位置,请参阅进阶:结构。
提出大量问题
在代码中组合问题输出(或喂给经典机器学习模型)
用确定性规则或加权和组合独立的答案。若要学习出组合方式,可以把概率作为下游经典机器学习模型的特征。
示例:用加权分数组合信号
pythonanswers = response.answers # Combine independent signals into one application-specific score. quality = ( 0.4 * answers["answers_request"].noul + 0.4 * answers["citations_are_supported"].noul + 0.2 * (1 - answers["contradicts_context"].noul) )组合评分展示了如何在组合各判断的同时保留它们。如果你没有用于下游模型的标签,可以用一组昂贵的推理模型来生成标签;AutoResearch 指南展示了如何在 System One 输出上训练经典模型。
根据不确定性进行路由
拆解并不意味着更多的往返请求。针对同一个 state 的问题会并行运行。
综合运用
这个支持工单工作流把确定性工作保留在代码中,只发送相关的结构化上下文,在同一次请求中评估许多原子化问题,并用显式的置信度门槛组合答案。
from typesafe_sdk import Choice, Noul, NoulCriteria, Score, TypeSafeClient
def triage_ticket(ticket, customer):
# Handle deterministic states without calling a model.
if ticket["status"] == "closed":
return "no_action"
open_orders = [
order for order in customer["orders"] if order["status"] != "delivered"
]
# Include only the structured context needed by the questions below.
state = {
"ticket": {
"message": ticket["message"],
"sender": ticket["sender"],
"links": ticket["links"],
},
"customer": {
"plan": customer["plan"],
"open_orders": open_orders,
},
"policy": {
"sensitive_credentials": ["password", "security code", "API key"],
},
}
# Ask structured, atomic questions together so they run in parallel.
questions = {
"topic": Choice(
instructions={
"question": "Which team should handle `ticket.message`?",
"focus": "Classify the customer's primary request.",
},
criteria={
"billing": {
"what": "Charges, invoices, refunds, or subscriptions",
"not_for": "Order tracking or account access",
"examples": ["I was charged twice", "Where is my refund?"],
},
"orders": {
"what": "Order status, delivery, cancellation, or returns",
"not_for": "Charges or account access",
"examples": ["Where is my order?", "Cancel my shipment"],
},
"account": {
"what": "Login, profile, permissions, or security",
"not_for": "Charges or order tracking",
"examples": ["Reset my password", "I cannot sign in"],
},
},
),
"requests_credentials": Noul(
instructions={
"question": "Does the message request a sensitive credential?",
"compare": [
"`ticket.message`",
"`policy.sensitive_credentials`",
],
"focus": "Look for a request to disclose the credential itself.",
},
criteria=NoulCriteria(
true={
"what": "Asks the recipient to disclose a listed credential",
"examples": [
"Reply with your password",
"Send us your API key",
],
},
false={
"what": "Does not ask the recipient to disclose a credential",
"not_for": "A legitimate instruction to reset a credential",
"examples": ["Use this link to reset your password"],
},
),
),
"sender_identity_mismatch": Noul(
instructions={
"question": "Does the claimed sender identity conflict with its domain?",
"compare": [
"`ticket.sender.display_name`",
"`ticket.sender.email`",
],
"focus": "Compare the named organization with the email domain.",
},
criteria=NoulCriteria(
true={
"what": "Claims an organization unrelated to the email domain",
"examples": ["Acme Payroll sent from claim-bonus.example"],
},
false={
"what": "The identity and domain agree or make no conflicting claim",
"examples": ["Acme Payroll sent from acme.example"],
},
),
),
"unexpected_reward": Noul(
instructions={
"question": "Does the message announce an unexpected reward?",
"inspect": "`ticket.message`",
"focus": "Look for an unsolicited prize, payment, or reward claim.",
},
criteria=NoulCriteria(
true={
"what": "Announces an unrequested prize, payment, or reward",
"examples": ["You were selected for a $1,000 bonus"],
},
false={
"what": "Contains no reward claim or discusses an expected payment",
"not_for": "A customer asking about a known refund or payroll deposit",
"examples": ["When will my approved refund arrive?"],
},
),
),
"refund_requested": Noul(
instructions={
"question": "Does the customer explicitly request a refund or credit?",
"inspect": "`ticket.message`",
"focus": "Require a requested remedy, not a billing complaint alone.",
},
criteria=NoulCriteria(
true={
"what": "Directly asks for money back or an account credit",
"examples": ["Please refund the duplicate charge"],
},
false={
"what": "Does not ask for a refund or credit",
"not_for": "A complaint or billing question without a requested remedy",
"examples": ["Why was I charged twice?"],
},
),
),
"mentions_open_order": Noul(
instructions={
"question": "Does the message refer to a supplied open order?",
"compare": [
"`ticket.message`",
"`customer.open_orders`",
],
"focus": "Match an order id or other identifying details.",
},
criteria=NoulCriteria(
true={
"what": "Refers to an open order by id or identifying details",
"examples": ["Where is order A-104?"],
},
false={
"what": "Does not identify any supplied open order",
"not_for": "A generic order question with no matching details",
"examples": ["How long does shipping usually take?"],
},
),
),
"frustration": Score(
instructions={
"question": "How frustrated does the customer appear?",
"inspect": "`ticket.message`",
"focus": "Judge expressed frustration, not issue severity.",
},
criteria=[
{
"what": "Calm and matter-of-fact",
"signals": ["Neutral wording", "No complaint about the experience"],
},
{
"what": "Frustrated but civil",
"signals": ["Expresses annoyance", "Remains constructive"],
},
{
"what": "Very angry or threatening to leave",
"signals": ["Hostile language", "Threatens cancellation or churn"],
},
],
),
}
with TypeSafeClient() as client:
response = client.system_one(
state=state,
questions=questions,
)
# Compose independent spam signals with weights controlled by code.
answers = response.answers
spam_risk = (
0.45 * answers["requests_credentials"].noul
+ 0.30 * answers["sender_identity_mismatch"].noul
+ 0.25 * answers["unexpected_reward"].noul
)
# Escalate uncertain judgments instead of guessing.
spam_is_uncertain = 0.4 < spam_risk < 0.6
if spam_is_uncertain or answers["topic"].confidence < 0.75:
return route_to_human_review(ticket)
if spam_risk >= 0.6:
return quarantine_as_spam(ticket)
# Let code decide which speculative answers matter on this path.
if answers["topic"].choice == "billing":
return route_to_billing(
ticket,
refund_requested=answers["refund_requested"].noul >= 0.7,
)
if answers["topic"].choice == "orders":
return route_to_orders(
ticket,
mentions_open_order=answers["mentions_open_order"].noul >= 0.7,
)
priority = (
"high"
if answers["frustration"].confidence >= 0.7
and answers["frustration"].score >= 1.5
else "normal"
)
return route_to_account_support(ticket, priority=priority)