What is inference-time alignment?

The decision happens in context

A model can produce a plausible action without knowing whether it is appropriate in the current workflow. A credit request, for example, may need approval because of its amount, even when the customer’s underlying concern is valid.

The evaluation must consider the requested outcome, relevant policy, current state, and the agent’s authority. A good explanation cannot substitute for a permission check that the operating system actually enforces.

What the judgment layer receives and returns

For a scoped decision point, define an input containing the proposed action, relevant evidence, known constraints, and the current permissions. The output can be a recommendation to proceed, revise the action, or request a named reviewer.

Preserve the evidence and the reason with the decision. Define how missing observations, uncertain judgments, and unavailable reviewers are handled before enabling the system to intervene.

How it relates to guardrails and evaluation

Explicit rules can enforce clear boundaries, such as an approval threshold. Learned judgment can help compare situations with expert examples. Offline evaluation tests both components before they are used in a live decision path.

These roles should be evaluated together. A model’s recommendation is useful only if the integration applies the intended outcome, preserves the existing permissions, and checks what ultimately happened.

A measured path to intervention

Start in observation mode: collect recommendations without allowing them to change the workflow. Compare them with independent expert review and inspect missed issues, unnecessary escalation, latency, and review load.

Enable only agreed action paths when the evidence supports them. Keep a responsible owner, a way to revert the integration, and a record of unresolved disagreement. The pilot should establish the conditions under which more autonomy is justified.

Make the distinction explicit.