Guides / Agent action boundaries

Before an AI agent acts: build a review boundary

An assistant that drafts a reply has a different level of responsibility from one that sends it. The same distinction applies to deleting files, updating customer records, or making purchases.

Sky Link Solutions
Proposed approach / practical guide
Reviewed 21 September 2026

A conceptual permission boundary: an action slip waits outside a glass enclosure, stopped by a solid bar before the receiving slot.
Concept studyA checkpoint before an action.

Permission

Separate a recommendation from permission

Before giving an agent more authority, define which actions are permitted, which require approval, and which must never happen.

A classifier can inspect a proposed action and flag it for review. It might help distinguish a routine update from a request involving sensitive information or an unusual recipient.

That assessment is one signal. It should not grant access or override a rule. The application must enforce permissions independently, including when the model is mistaken or unavailable.

Proposed workflow

  1. Proposed action

  2. Enforced permission check

  3. Risk assessment

  4. Required human approval

  5. Permitted execution

Blocked or uncertain requests stop for review.

One candidate

Where a narrow model might help

Jev is a candidate for evaluating specific questions about an action. For example: does the proposed recipient fall outside the expected organization? Where a fact can be checked directly in code, use that check. Reserve model judgment for information that needs interpretation.

Do not rely on an incoming message’s instructions to define its own safety rules.

The test

Test the boundary before expanding access

Begin with actions that cannot change live systems. Include misleading instructions, missing context, and requests that should be refused. Verify what happens when a check fails or a service times out.

Measure missed risks and unnecessary reviews. Keep permissions narrow, record decisions, and require confirmation for consequential actions.

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Exploring an agent that can take action?

Start with a review of its boundaries and one workflow worth testing.

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