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How to build software with System One

[ concept ][ updated 2026-09-17 ][ confidence high ][ jev-1.13.0 ][ python sdk 0.6.0 ]#architecture · design · decomposition · workflow · composition

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.

The docs' own summary — build a normal software workflow and insert System One only where AI is needed:

Three software architectures

Architecture How it works
Traditional software A complex decision tree made from simple software primitives. Because each primitive is reliable, developers compose them into higher-level abstractions.
LLM agents An agent processes instructions and chooses its next step. Works well when a person is monitoring, but every loop introduces another opportunity to go off the rails.
AI-powered software Code handles deterministic work and owns control flow. The model appears only where the system needs programmable common sense or must interpret unstructured data. Each AI task is kept atomic and constrained.

What makes System One composable

Property What it buys you
Structured Type-safe by construction. Decisions and probabilities conform to the software types and JSON schema your code expects; code never has to recover a value from generated prose.
Parallel Questions are evaluated independently and in parallel. One primitive's result does not become hidden context that changes another's.
Comparable Outputs are sortable and can drive smart if statements, thresholds, and comparisons.
Fast Most queries complete in about 100 ms — fast enough for real-time request paths and user interfaces.
Calibrated confidence RLCD (AI primer: why calibrated decision models) communicates uncertainty through calibrated probabilities instead of tending toward overconfidence.
Self-consistent Designed to return stable answers across repeated evaluations. See Cookbook: Self-consistency — nouls.

Because every output is constrained to the supplied options, the model returns a full probability distribution over those options 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 can express the same behavior.

days_overdue = (today - invoice.due_date).days

if days_overdue > 30:
    route_to_collections(invoice)

See Patterns overview for bounded ways to compose model decisions with code.

2. Decompose the input state

Include only the context relevant to the current questions. This helps the model avoid distractions and context rot. Do not rely on knowledge stored in 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 the state and questions fields. Point questions at specific values when that removes ambiguity, and include the backtick characters around each path inside the question. Use a backticked 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, specific, atomic questions you can. Break down 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. One broad question (bad):

{
  "is_spam": {
    "type": "noul",
    "instructions": "Is `message` spam?"
  }
}

Decomposed questions (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:

{
  "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`?"
  }
}

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"):

{
  "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`?"
  }
}

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 atomic questions short. When instructions or criteria need several kinds of guidance, use objects or arrays with named fields instead of flattening everything into a dense prose string. This makes the decision boundary easier to scan, review, and tune.

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. Add structure when it separates guidance that would otherwise blur together. See Structured instructions, options, levels, criteria for the full set of places structure is accepted.

6. Ask a lot of questions

Ask many narrow, independent questions about the same state in one request. This is how you maximize effectiveness and intelligence per dollar with the API: questions run in parallel, and code can combine their signals without adding serial model 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)
)

Make code 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 (use an ensemble of expensive reasoning models to generate them), and Confidence-gated routing for matching thresholds to the risk of each action.

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:

Gotchas

Related

Sources