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What does 'the LLM explains, it does not arbitrate' mean?

It means the model produces language and structure, like a classification or a draft, but it never makes the binding decision that changes state. To arbitrate is to decide that money moves, a record deletes, or an email sends. In this pattern the model recommends by filling a labeled slot, and deterministic code checks preconditions against trusted state and decides whether the action actually happens.

What is deterministic scaffolding for an AI agent?

Deterministic scaffolding is a design where code holds the control flow, usually as a state machine, and the model is called only at specific points to fill a labeled slot such as an intent, extracted fields, or a drafted reply. Code owns routing, validation, retries, policy enforcement, and executing side effects once, so a model mistake is confined to a slot value that code decides how to act on.

Why not just prompt the model to make the right decision?

Because per-call reliability sits below 1.0 and cannot be prompted to 1.0, so a decision the model gets wrong some fraction of the time stays wrong some fraction of the time, and those errors compound across a multi-step run. A precondition checked in code is right every time, is far cheaper, and leaves an auditable trace, so decisions with a definite answer belong in code rather than in a better prompt.

How does deterministic scaffolding help against prompt injection?

It removes the attacker's ability to say anything dangerous rather than trying to detect the attack. The model's only channel into a state-changing path is a slot with a fixed vocabulary of labels, so even an attacker who fully controls the model's output can at most choose one allowed label, and every allowed label lands in code that re-checks preconditions against trusted state. Injection becomes a multiple-choice question where every choice is contained.

When is deterministic scaffolding the wrong choice?

It is the wrong trade for open-ended tasks where the value is in the model taking paths you did not anticipate, such as exploratory research, because a tight scaffold only handles the paths you built. It also fails when the slot vocabulary is too coarse, since forcing ten real intents into three labels makes the model mislabel and the workflow act wrongly with confidence. Scaffolding also needs real precondition checks behind each slot, or the containment is only cosmetic.

In deterministic scaffolding, what is the model allowed to decide on a state-changing path like issuing a refund?

Only the intent label from a fixed vocabulary; code checks preconditions against trusted state and decides whether money actually moves, The model explains, it does not arbitrate. Its only contribution on a state-changing path is a validated intent label from a fixed set. Code holds the authenticated identity and the order record, checks the preconditions (ownership, status, window, cap, prior refunds), and decides whether the refund executes, using the amount from the record. The model never names the amount or the account, so its output cannot move money directly.

A prompt injection makes the model emit {"intent": "issue_refund_now", "auto_execute": true, "amount_cents": 500000}. Why does no refund happen?

The workflow reads only the intent slot and ignores the extra fields, and issue_refund_now is not in the allowed vocabulary, so it routes to a human, Containment here is structural, not detection. The model's only channel into the state-changing path is one labeled slot with a fixed vocabulary. Code reads only the intent field, so auto_execute and amount_cents do nothing, and issue_refund_now is not an allowed intent, so the workflow escalates to a human. Even if the attacker had pushed the label to a real intent like refund_request, it would land in the same precondition gate that re-checks trusted state.

A workflow makes 8 decisions. The model decides each correctly with probability 0.95, and the run is correct only if all decisions are. Moving the 6 deterministic decisions to code leaves the model with 2. What happens to model-dependent reliability?

It rises from about 0.95^8 (roughly 0.66) to 0.95^2 (roughly 0.90), from the same model, because each deterministic term drops out of the product, The run is correct only if every decision is, so the per-decision reliabilities multiply: p to the N. A deterministic code decision contributes a term of effectively 1.0, which drops out of the product, so moving 6 of 8 decisions to code changes the model-dependent reliability from 0.95^8 (about 0.66) to 0.95^2 (about 0.90). Same model, same prompt. The gain comes from asking the model to decide less, which is the compounding argument from the first lesson applied in reverse.

When is a task a poor fit for deterministic scaffolding?

Open-ended work where the value is in the model taking paths you did not anticipate, or where a too-coarse slot vocabulary forces the model to mislabel, Scaffolding trades flexibility for control. It handles only the paths you enumerate, so for exploratory work where the point is the model taking unplanned paths, a tight scaffold can be the wrong trade. It also fails when the slot vocabulary is too coarse: collapsing ten real intents into three labels forces mislabeling and confident wrong actions. Money and data actions are exactly where scaffolding fits best, not worst, as long as real precondition checks sit behind each slot.

Deterministic Scaffolding: The LLM Explains, It Does Not Arbitrate

Name: System Design Masterclass
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