Canonical: https://hypler.com/wiki/human-approval

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# Human Approval Is a System Boundary

Approval should be scoped to an identifiable action and supported by policy and evidence, rather than inferred from a model suggestion or a tool's availability.

## A recommendation is not an authorization

SI systems can rank options, draft actions, or assemble context. None of those steps establishes a right to change a record, move money, send a message, or deploy code. Authorization requires an accountable decision within the rules of the system and the organization that operates it.

The interface should make this distinction visible. A user needs to know whether they are reviewing a proposed action, approving a specific packet, or observing a result after execution. Combining those states in one vague confirmation button creates risk for both the user and the system owner.

- Bind approval to a specific actor, action, target, and relevant context.
- Show denial and unknown outcomes as first-class states.
- Expire or invalidate approval when its underlying action changes.

## Policy should be deterministic where it matters

Consequential actions benefit from policy that can be evaluated consistently and explained after the fact. A policy decision may allow, deny, or require more review based on the action and its context. The important point is that a model's interpretation does not silently substitute for the governing policy.

LNSAT's public pre-release material describes exact packets, deterministic policy, scoped approval, one-time authority, receipts, and reconciliation. It is public source-level evidence for this design direction, not a statement that every possible action can be dispatched or that a hosted runtime is available.

- Keep policy inputs explicit and versioned.
- Record the approval scope and the decision result.
- Require reconciliation rather than assuming a request completed.

## People retain consequential decisions

Human approval is not a bottleneck to be designed away. It is a deliberate boundary for actions that have legal, financial, security, or operational consequences. The right interaction gives a responsible person the context needed to decide, preserves what they approved, and makes later review possible.

For lower-risk automation, use bounded rules and clear rollback paths. Match the review and approval process to the consequence of the action.

## Sources

- [LNSAT public source](https://github.com/hypler-dev/LNSAT)
- [Hypler LNSAT project](https://hypler.com/lnsat)

## Related capabilities

- [SI agent governance and authorization](https://hypler.com/ai-agent-governance)