How to build software with System One

TL;DR Keep control flow, deterministic rules, and side effects in code. Break broad judgments into narrow, typed questions with explicit instructions and criteria. Give each question only the context it needs. Ask many independent questions in one request (they run in parallel), combine the answers with your own weights and thresholds, and route on confidence.

What it is

System One is TypeSafe's model for building AI-powered software, not agents. It does not generate code or choose its own next action. It provides AI primitives that embed into software, so code remains in control while the model handles common-sense judgments over unstructured data. (It is also not a replacement for the LLM inside a coding agent — see Jev with coding agents: not a drop-in for the LLM behind Claude Code, Cursor, Copilot.)

The docs' own summary — build a normal software workflow and insert System One only where AI is needed: keep control flow, deterministic rules and side effects in code; break broad judgments into narrow typed questions with explicit instructions and criteria; give each question only the context it needs; use probabilities and confidence to act, ask for review or escalate; ask independent questions together and compose their answers in code.

Three software architectures

What makes System One composable

Every output is constrained to the supplied options, so the model returns a full probability distribution over them rather than inventing a value outside the schema. TypeSafe states its target is a greater than 100× intelligence-to-speed-and-cost ratio, on the bet that cheaper intelligence creates much more demand.

The design workflow (7 steps)

1. Use code when you can

Keep deterministic work in code: it is reliable and cheap. Avoid agent while loops when a software workflow expresses the same behavior.

days_overdue = (today - invoice.due_date).days

if days_overdue > 30:
    route_to_collections(invoice)

See Patterns overview for composing model decisions with code.

2. Decompose the input state

Include only the context the current questions need: this avoids distractions and context rot. Do not rely on knowledge in the model weights when current information can come from your own knowledge base.

{
  "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?"
    }
  }
}

3. Use structure in the input state

Use nested JSON for state and questions. Point questions at specific values when that removes ambiguity, and include the backtick characters around each path inside the question — a dot-and-index path such as `support.tickets[0].message`.

{
  "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`?"
    }
  }
}

4. Decompose the questions

Ask the most explicit, narrow, atomic questions you can. Break complex or ill-defined questions into separate questions that each evaluate one property.

The docs flag this as "probably the most important concept in this guide. Broad questions hide several judgments behind one answer. Atomic questions expose those judgments so you can inspect, tune, and combine them in code."

Worked example — spam detection. The broad question (bad) is a single Noul is_spam with instructions "Is message spam?".

Decomposed (good), over a state whose message has sender.display_name "Acme Payroll", sender.email "rewards@claim-bonus.example", subject "Urgent: claim your employee bonus", a bonus-offer body, and a links[0] of text "Claim bonus" → http://claim-bonus.example/acme. All six are {"type": "noul", "instructions": …}:

Worked example — verifying a tool-call trace. The bad version asks one question, tool_calls_are_correct: "Is trace.tool_calls correct for request and available_tools?" The good version asks nine, each checking one property of the same state (a request for Seattle weather in fahrenheit on 2026-09-03, an available_tools map with geocode_city and get_weather, and a trace where tool_calls[1].arguments.unit is "celsius"):

All nine are {"type": "noul", "instructions": …}:

The payoff: the broad question returns one number that hides the unit mismatch; the decomposed set isolates it in weather_unit_matches.

5. Use structure in the questions

Keep questions short. instructions and criteria are usually strings, and for a short, unambiguous question a string is all you need. They can also be objects or arrays: put the question in one field and the data that guides it in the others. Upstream names three cases where structure helps (rewritten 2026-09-21):

• the question needs context or examples — background or example inputs go in named fields "where your code can add to them or swap them without rewriting the question";
• part of the question comes from your code — "when a value comes from a database, put it in its own field instead of splicing it into a string template";
• several questions have similar instructions — the supplementary data is what makes them distinct.

Data a question refers to is named in backticks inside the question text, exactly like a state path:

"instructions": {
  "potential_duplicate": { "name": "John Smith", "location": "Oakland, California", "last_employer": "Google" },
  "question": "Is the resume for the same person as `potential_duplicate`?"
}

criteria descriptions can be objects too. For a Choice, describe what belongs in each option, what belongs in a neighboring option instead (not_for), and a few representative examples. Use the same field names across options so the model can compare them directly.

{
  "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?"
        ]
      }
    }
  }
}

(State for that example: the string "How many disposable virtual cards can I make per day?".)

A short, unambiguous question or criterion can remain a string. Worked example per type: Noul (yes/no) questions (one database record per question, questions built in code), Choice questions (two easily confused options), Score questions (a level description plus example situations), and Cookbook: SDE cascade for the shared-wording case. Full set of places structure is accepted: Structured instructions, options, levels, criteria.

6. Ask a lot of questions

Ask many narrow, independent questions about the same state in one request: that is how you maximize intelligence per dollar. Questions run in parallel, and code combines their signals without serial round trips. See Speculative fan-out and Cookbook: Parallel questions.

7. Combine outputs in code, then route on uncertainty

Combine independent answers with deterministic rules or weighted sums. For learned composition, use the probabilities as features in a downstream classical ML model.

answers = 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)
)

Take different actions for confident and unconfident answers. Escalate uncertain cases to a person or a more expensive reasoning model. Test thresholds by plotting confidence against accuracy on your data.

answer = response.answers["card_help_topic"]

if answer.confidence < 0.8:
    route_to_human_review(ticket)
else:
    route_to_handler(answer.choice, ticket)

See Composite scoring for preserving individual judgments while combining them, Cookbook: Autoresearch feature discovery for training a classical model on System One outputs when you lack labels, and Confidence-gated routing for matching thresholds to each action's risk.

Tip from the docs: decomposition does not require more round trips. Questions over the same state run in parallel.

Putting it all together

The full worked example from the docs: a support-ticket triage that keeps deterministic work in code, sends only relevant structured context, evaluates many atomic questions in one request, and composes the answers with explicit confidence gates.

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)

Contracts visible in that example, worth memorizing:

• Imports come from typesafe_sdk (Choice, Noul, NoulCriteria, Score, TypeSafeClient); TypeSafeClient() is a context manager; the call is client.system_one(state=..., questions=...), and questions is a dict keyed by your own names whose answers return under the same keys via response.answers[...].
• Value accessors per type: .noul (float 0–1), .choice (the option key), .score (float), and .confidence on Choice and Score.
• Noul criteria use NoulCriteria(true=..., false=...); Choice criteria are a dict keyed by option name; Score criteria are an ordered list, lowest level first (the frustration example runs calm → frustrated → very angry). In the Python SDK 0.7.1 Score.criteria is an ordered sequence, not an int-keyed dict.
• Structured instructions fields used in the source: question, focus, inspect, compare. Structured criteria fields: what, not_for, examples, signals.

Gotchas

• Speculative answers are nearly free in latency. The example asks refund_requested and mentions_open_order even though only one branch will use them — that is the intended pattern, not waste. Upstream softened the claim on 2026-09-21 from "typically doesn't add any latency" to "usually has little effect on response time"; extra questions still cost tokens.
• Two different uncertainty gates. spam_is_uncertain thresholds a derived band (0.4 < spam_risk < 0.6); answers["topic"].confidence < 0.75 thresholds the model's own confidence. Noul answers carry no confidence (Confidence vs probability).
• not_for is doing real work. Contrastive criteria (what belongs here vs. the neighboring option) are what make a Choice boundary crisp.
• Backticks matter. The docs require the backtick characters around a nested path inside the question text.
• Do not let a broad question hide a compound judgment. If you cannot name exactly one property it tests, it is not atomic yet.

Related

• System One Models — what the model is
• State: what you send Jev — building the input
• Primitives: Choice, Score, Noul — Choice, Score, Noul
• Structured instructions, options, levels, criteria — structured instructions, options, levels, criteria
• Confidence vs probability — thresholds and escalation
• Speculative fan-out — asking many questions at once
• Composite scoring — combining answers
• Confidence-gated routing — risk-matched gates
• Cookbook: Parallel questions — parallel questions in practice
• Python SDK: install, clients, system_one() — client and system_one() signature

Sources

• raw/docs/concepts__how-to-build-with-system-one.md (https://docs.typesafe.ai/concepts/how-to-build-with-system-one)